Eco_Lógicas 2015/2016 (EN)

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2015/2016

KNOWLEDGE - SUSTAINABILITY - INTEGRATION

Latin American Research Paper Contest on Renewable Energy and Energy Efficiency

Sponsored by


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Eco_Lรณgicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.


Eco_Lรณgicas Latin American Research Paper Contest on Renewable Energy and Energy Efficiency Selected Papers

Quito - Ecuador 2015/2016


Eco_Lรณgicas Latin American Research Paper Contest on Renewable Energy and Energy Efficiency 2015/2016 Selected Papers Instituto IDEAL (Institute for Developing Alternative Energy in Latin America) OLADE (Latin American Energy Organization) Quito, Ecuador 262 p. ISBN: 978-9978-70-118-8


ORGANIZING COMMITTEE Mauro Passos - Instituto IDEAL Fรกtima Martins - Instituto IDEAL Gabrielle Bittelbrun - Instituto IDEAL Andressa Braun - Instituto IDEAL Lourdes Pillajo - OLADE

SCIENTIFIC AND EVALUATING COMMITTEE 1. Sandra Garzรณn La Salle University Bogota, COLOMBIA 2. Roberto Lamberts and his team of five specialists. Federal University of Santa Catarina Florianopolis, BRAZIL 3. Rodrigo Flora Calili Pontifical Catholic University Rio de Janeiro, BRAZIL 4. Reinaldo Castro Souza and his team of five specialists. Pontifical Catholic University Rio de Janeiro, BRAZIL 5. Fundaciรณn Bariloche and his team of six specialists. Buenos Aires, ARGENTINA 6. Ing. Enrique Riegelhaupt Mexican Bioenergy Network MEXICO 7. Fernando Luiz Cyrino Oliveira, D.Sc. Industrial Engineering Department. Pontifical Catholic University Rio de Janeiro, BRAZIL

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JUDGING COMMITTEE Institute for Developing Alternative Energy in Latin America, Instituto IDEAL Latin American Energy Organization, OLADE

EDITORIAL COMMITTEE Marcelo Ayala Communication Department OLADE Andressa Braun Project and Communications Manager Instituto IDEAL DESIGN & LAYOUT Ana María Arroyo Independent Consultant TRANSLATION Gabriela Martínez OLADE Peter Newton Independent Consultant

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Eco_Lógicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.


PRESENTATION

OLADE is the political and technical support organization that contributes to integration, sustainable development and energy security in the region, advising and promoting cooperation and coordination among its Member Countries. Under this scheme, OLADE has created spaces where alliances have been forged with the Academia in order to encourage research in renewable energy and energy efficiency, through capacity building in virtual, partial-distance and face-to-face programs. OLADE’s Regional Energy Training (CAPEV) has played an extremely important and recognized role in the region, so much so that during 2015 more than 7000 officials from the ministries and energy secretariats of the 27 member countries of Latin America and the Caribbean were trained. Likewise, OLADE, through the platform of technical networks of the Energy Sector of Latin America and the Caribbean (Network Experts), has encouraged dialogue and exchange among specialists to support the analysis and decision-making of experts, governments and other stakeholders in the energy sector. In addition to the above, the Ecological Contest, an initiative promoted by the IDEAL Institute of Brazil and executed with the support of the Latin American Energy Organization OLADE, has been added. Through this alliance with OLADE, the first edition of the competition, to be held jointly in 2013/2014, was attended by 12 OLADE member countries: Argentina, Brazil, Chile, Colombia, Cuba, El Salvador, Mexico, Nicaragua, Panama, Paraguay, Peru and Uruguay. In the 2014/2015 edition, 42 works from 9 member countries of OLADE were presented: Argentina, Brazil, Chile, Colombia, Guatemala, Mexico, Paraguay, Peru and Uruguay. These works were evaluated by OLADE’s staff of Training Instructors, which is made up of around 100 specialists from Universities and Research Centers of the highest level in the region. After an exhaustive evaluation, a winner in the Renewable Energy category and one in the Energy Efficiency category was selected, thus contributing to the commitment to knowledge, sustainability and integration in the region. Through the publication of this book, the IDEAL Institute and OLADE want to extend their congratulations and recognition to the students and professors who participated in this edition of the contest, for their dedication and contribution to achieving a clean and sustainable energy matrix.

Fernando Ferreira Executive Secretary of OLADE

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RENEWABLE ENERGY <BRAZIL>

DEVELOPMENT OF A THREE-PHASE TRANSFORMERLESS INVERTER FOR CONNECTING PHOTOVOLTAIC SYSTEMS TO THE ELECTRICAL GRID

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JULIAN CEZAR GIACOMINI <BRAZIL>

GENERATING POWER FROM WIND SOURCES IN RIO GRANDE DO NORTE

37

LUZIENE DANTAS DE MACEDO <CHILE>

ESTIMATOR OF AVAILABLE ENERGY FOR THE BATTERY BANK OF A MICROGRID BASED ON RENEWABLE ENERGY SOURCES

59

CLAUDIO DANILO BURGOS MELLADO <COLOMBIA>

NEW CONTRIBUTIONS TO THE DESIGN OF PHOTOVOLTAIC BUILDINGS FOR SUSTAINABLE CITIES

89

LUIS FERNANDO MULCUE NIETO <MEXICO>

DEVELOPMENT OF AN AUTONOMOUS MOBILE EMERGENCY SYSTEM FOR WATER PURIFICATION

115

DULCE KRISTAL BECERRA PANIAGUA

ENERGY EFFICIENCY <BRAZIL>

DECISION-MAKING SUPPORT METHOD TO IMPROVE ENERGY EFFICIENCY IN RESIDENTIAL BUILDINGS

137

ARTHUR SANTOS SILVA / LAIANE SUSAN SILVA ALMEIDA <BRAZIL>

USER IMPACT ON NATURAL AND ARTIFICIAL LIGHTING SYSTEMS: A CASE STUDY OF CLASSROOMS AT THE UFMG SCHOOL OF ARCHITECTURE

167

CAMILA CAMPOS GONÇALVES <BRAZIL>

ENERGY EFFICIENCY IN THE ELECTRICITY INDUSTRY PLANNING FOCUSED ON CO2 EMISSIONS

185

LUIZ FILIPE ALVES CORDEIRO <BRAZIL>

ENERGY RECOVERY FROM URBAN SOLID WASTE: CASE STUDY IN THE MUNICIPALITY OF ITANHAEM, SAO PAULO

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LUIZ HENRIQUE TARGA GONÇALVES MIRANDA <BRAZIL>

DEMAND-SIDE MAGANEMENT AND PHOTOVOLTAIC SYSTEMS CONNECTED TO THE GRID AS ENERGY RESOURCES: A CASE STUDY OF THE ELECTRIC INDUSTRY IN NICARAGUA CARLOS GERMÁN MEZA GONZÁLEZ

225



RENEWABLE ENERGY


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Eco_Lรณgicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.


BRASIL DEVELOPMENT OF A THREE-PHASE TRANSFORMERLESS INVERTER FOR CONNECTING PHOTOVOLTAIC SYSTEMS TO THE ELECTRICAL GRID JULIAN CEZAR GIACOMINI Guidance: Professor Dr. Cassiano Rech

ABSTRACT

Photovoltaic solar energy has been growing over the last years because it is a renewable and virtually inexhaustible energy source. In this sense, the grid-connected photovoltaic systems are very promising, once that generated energy can be used to supply local loads and the energy excess can be injected into the grid. In the grid-connected systems, the inverter is the element responsible for adequating continuous voltage level provided by the photovoltaic system in an alternate level compatible with the electrical distribution grid. In this scenario, the transformerless photovoltaic inverters are an interesting solution, since they present high efficiency, low cost and reduced size/weight due to the lack of galvanic isolation. However, the leakage current circulation between the stray capacitances of the grounded array and the grid is a concern in these systems. The leakage current circulation results in electromagnetic interference, safety issues, waveform distortion of the grid current and increased losses. Therefore, the development of a three-phase grid-connected transformerless photovoltaic inverter is presented in this work. This inverter presents a high efficiency and keeps the leakage current in accordance with standard limits. Besides, the grid current quality is ensured by employing a LCL passive filter, where the harmonic distortion limit defined by national standards is not violated. Experimental results are presented employing the prototype developed in order to verify its performance.

KEYWORDS: Transformerless photovoltaic inverter, Leakage current, Grid-connected photovoltaic systems.

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INTRODUCTION The goal of this chapter is to provide a context for the subject discussed in this monography. The first part gives a global picture on energy and emphasises the importance of renewable energy. A mayor focus goes out to photovoltaic solar energy and its increasing usage over the last years. Then, a description of the basic features of grid-connected photovoltaic systems will be given. Some aspects related to using the transformer in grid-connected photovoltaic systems will be dealt with too, stressing its advantages and disadvantages. The last part of this chapter describes the final goals and aims of this monography. OVERVIEW AND PERSPECTIVES ON ENERGY One of the most popular forms of energy in the world is electricity. This is because of its easy means of transmission and reduced levels of energy loss in comparison to other energy sources, amongst other reasons. Economic development and population growth cause an increase in the consumption of electrical energy. As it fundamentally affects all aspects of human life, modern society becomes more and more dependent on electrical energy. To illustrate this, Figure 1 shows the development of the annual estimated electrical energy consumption in the world over the last years. You can observe that electrical energy consumption increased from 15136 billion kWh in 2004 to almost 19710 billion kWh in 2012, equivalent to a 30.2% increase over this period. 20000

Billions kWh

15000

10000

5000

0 2004

2005

2006

2007

2008

2009

2010

2011

2012

Figure 1 – Development of annual estimated electrical energy consumption in the world Source: Adapted from (EIA, 2015). To provide the supplies for this growing demand, the energy matrix needs to be operationally prepared. It is known that most of global electrical energy consumption comes from non-renewable energy sources, as oil and oil products. Figure 2 confirms this statement in representing the distribution in percentages of the main primary electrical energy generating sources in the world in the year 2012. Renewable energy only makes up 21.2% of all generated electricity in the world, while the remaining share predominantly comes from non-renewable sources.

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Eco_LĂłgicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.


The usage of non-renewable energy has a rather negative influence on the environment. The emission of toxic gases in the atmosphere as a result of burning oil lead to global warming (greenhouse effect) and affect the climate and our ecosystem. The likely shortage of fossil fuels has led to investments being made in renewable energy and the implementation of sustainable practices. Out of the different types of renewable energy, solar energy has particularly increased over the last years. Solar energy is known to be a very promising source of renewable energy. It can be applied in different ways, especially in photovoltaic systems. In these systems, solar energy is used to generate energy from photovoltaic modules. Photovoltaic solar energy presents various advantages when compared to other energy sources, in that for example: it is noise-free, long-lasting ,doesn’t pollute, it originates from an abundant energy source (the sun), it could be installed directly at the place of consumption, and it is easy to install due its modular structure (LUQUE and HEGEDUS, 2011). 22,5%

10,9%

21,2%

16,2% 5,0%

40,4% 5,0%

Oil Coal/peat Natural Gas Nuclear Hydro Others*

* Geothermal, solar, wind, etc. included

Figure 2 – World electricity generation. Source: Adapted from (IEA, 2014a). Photovoltaic solar energy continues to be on the rise, even though its contribution is by far not as significant compared to energy generated by other renewable sources, as could be observed in Figure 3, based on 2012 data from the International Energy Agency (IEA). While photovoltaic solar energy is only responsible for 2.11% of all renewable electrical energy generated worldwide, hydro energy has a share of 77.8%.

7,85%

77,80%

22,20%

10,79% 2,11% 1,45%

Hydro Biomass Wind Photovoltaic Geothermal

Figure 3 – World distribution of electrical energy generated by renewable sources. Source: Adapted from (IEA, 2014b).

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14000

Installed Capacity (MW)

12000 10000 8000 6000 4000 2000 0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Europe Pacific Asia America

China Middle East and Africa Rest of the world

Figure 4 – Cumulative evolution of global installed capacity of photovoltaic generation. Source: Adapted from (EPIA, 2014). Figure 4, however, demonstrates that global installed capacity of photovoltaic power generation has been experiencing a strong growth over the years. With a total of 81.5 GW in 2013, Europe is consolidated as the first region in the world in terms of installed capacity. This value represents a share of 59% of worldwide installed capacity of photovoltaic generation. America occupies the third place with approximately 13.7 GW of installed power. As a result of the search for alternative energy sources, together with the increasing investments in this field, global installed capacity of photovoltaic power generation reached a value of 138.85 GW in 2013, according to data of the European Photovoltaic Industry Association (EPIA) (EPIA, 2014). In the case of Brazil, photovoltaic solar energy has not transformed yet in a widely used technology as occurred in European countries. It has mainly been applied to small isolated or autonomous systems in places of no distribution of power supply. According to 2014 data from the Balance Energético Nacional (National Energy Balance), Brazil had an installed capacity of photovoltaic power generation of only 5 MW in 2013 (EPE, 2014). On the other hand, compared to most European countries, which have the highest share in global installed capacity of photovoltaic power generation, solar irradiance is superior in any region of Brazil. Brazil, for example, has a daily insolation of between 4.5 and 6 kWh/m2, whereas in Germany maximum daily insolation doesn’t exceed 3.5 kWh m2 (VILLALVA and GAZOLI, 2012). These data demonstrate that Brazil doesn’t make maximum use of this resource. Brazilian territory offers great potential for solar photovoltaic power generation, a potential that is much higher than present in Germany, a country that has the highest installed capacity of photovoltaic solar power generation in the world (EPIA, 2014). Over the course of the last years, there has been an increasing interest from the relevant organisations to make photovoltaic systems competitive and connect them to the Brazilian energy matrix. Proof of this is the adoption in 2012 of normative resolution n° 482 of the Brazilian National Agency for Electrical Energy

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Eco_Lógicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.


(ANEEL) (ANEEL, 2012b). This resolution has established the general conditions for micro-generation and mini-generation to access the electrical energy distribution grid. It has also set up an electrical energy remuneration system that allows the consumer to have a photovoltaic system in his house for private consumption. In case of excess power generation, a credit will be made available which will be compensated on subsequent energy bills, or this surplus energy could be transferred to another consumer unit. Once the barriers related to photovoltaic solar energy are overcome, it offers great opportunities to convert itself into one of the most widely-used sources of renewable energy in Brazil and in the world. Future perspectives on global photovoltaic solar energy seem quite promising. According to IEA data, it is estimated that in 2020 the installed global capacity of photovoltaic solar energy will be close to 400 GW, nearly three times as much as actual installed capacity (IEA, 2014b). GRID-CONNECTED PHOTOVOLTAIC SYSTEMS Grid-connected photovoltaic systems connect energy that is generated through photovoltaic modules to the grid. They can be divided in large scale centralized systems (centrals) or small scale decentralized systems (distributed generation). ANEEL has created three categories of grid-connected photovoltaic systems based on their power (ANEEEL, 2012a): • Micro-generation: installed power of up to 100 kW; • Mini-generation: installed power between 100 kW and 1 MW; • Power plants: installed power of more than 1 MW. Power plants produce great quantities of electrical energy through a large scale photovoltaic system. As they require big surfaces for their installation and are located relatively far away from the actual consumers of the generated power, they rely on extensive transmission systems. Micro- and mini-generation systems are installed in a distributive way (Distributed Generation) next to the end-user location. Generated energy from these systems is used as a supplement to grid-connected energy. Electrical power transmission and distribution losses will in this way to a great extent be eliminated. Moreover, distributed generation allows consumers to save on electricity fees while it also increases the trustworthiness of the system. Mini-generation systems are mostly used by businesses and industries, whereas micro-generation is particularly adopted by individual households who have a lower level of electrical energy consumption (VILLALVA e GAZOLI, 2012). To exemplify this, Figure 5 shows a simplified diagram of a residential photovoltaic grid-connected system. The inverter converts the direct current into alternating current compatible with the grid. The passive filter mitigates harmonic commutations generated by the inverter and makes sure current standards regarding harmonic current distortion are observed. The electric meter of the end-user should be bi-directional so that energy excess could be injected into the grid. This means that if there would be enough energy generated to fulfil the needs of the household, the surplus energy could be injected into the grid and the amount credited on subsequent electricity bills.

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Photovoltaic system Inverter

Filter Grid Meter Load

Figure 5 – Simplified diagram of residential photovoltaic grid-connected system.

USE OF TRANSFORMER IN GRID-CONNECTED PHOTOVOLTAIC SYSTEMS Depending on the degree of electrical isolation between the photovoltaic system and the grid, gridconnected photovoltaic systems could be isolated or non-isolated. This galvanic isolation derives from using a transformer that could be connected to the grid (low frequency) as is outlined in Figure 6, or configured to the DC (high frequency) link within the DC-AC converting block. In order to be allowed to connect to the electrical system, ANEEL has made it a prerequisite for Brazil that distributed generation systems with installed power superior to 100kW should at least contain an isolation transformer (ANEEL, 2012a). On the other hand, this requirement doesn’t apply to systems with an installed power of less than 100 kW. The use of a transformer in photovoltaic grid-connected systems has several advantages, like for example: easy switch between different voltage levels, less injections of direct current into the grid and it prevents leakage current circulation though the stray capacitances of the photovoltaic system (GONZALEZ et al., 2006; GUBÍA et al., 2007; SUAN et al., 2011). Further, it offers major protection against electrical shocks as the transformer provides isolation preventing the current to circle through the human body in case someone would touch the metallic frame (not grounded) of the photovoltaic module (FARIAS, 2011). DC-AC Filter

Transformer

Grid

PV

Figure 6 – Isolated photovoltaic system (PV) with low frequency transformer

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Eco_Lógicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.


However, when the system is galvanically isolated, the transformer operates at a low frequency, causing an increase in weight, size and cost, while also lowering efficiency resulting from transformer losses (GONZALEZ et al., 2006; GUBÍA et al., 2007). Size/cost reduction could be obtained by using a high frequency transformer. Higher frequency increases magnetic core losses and compromises efficiency due to an extra energy-conversion step (LOPEZ et al., 2007; MARANGONI, 2012). Eliminating galvanic isolation by taking out the transformer, increases efficiency, lowers costs and reduces size/weight (KEREKES et al., 2007; GONZALEZ et al., 2008). In this sense, production costs of the inverter are rather minor, making the grid-connected photovoltaic system more market-competitive. A lower weight also entails a reduction in transportation and fuel costs. A simplified scheme of a grid-connected transformerless photovoltaic system is shown in Figure 7. When there is no galvanic isolation, however, the metallic housing of photovoltaic modules should be grounded for security reasons. Grounding prevents electric shocks in case a person touches the housing. It forms stray capacitances between the photovoltaic cells and their grounded frame. Different levels of voltages pass through these stray capacitances, depending on the modulation and topology of the inverter used for this non-isolated system, and they create leakage currents that pass through the inverter and are injected into the grid (TEODORESCU et al., 2010; SUAN et al., 2011).

DC-AC Filter

Grid

PV

Figure 7 – Transformerless grid-connected photovoltaic system

To demonstrate this, Figure 8 presents the leakage current (if) that is generated in the photovoltaic system due to equivalent stray capacitances (Cp) between the output terminals of the photovoltaic module and the grounded array. This presents one of the disadvantages of non-isolated photovoltaic systems as the leakage current causes electromagnetic interference (EMI) problems, distortion of the inverter output current waveform, and hence increases losses and security issues (MYRZIK e CALAIS, 2003).

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DC-AC

PV

Filter

le du

Mo

Cp

iƒ/2

Cp

Vg

iƒ/2

Figure 8 – Picture of stray capacitances and leakage current in a transformerless grid-connected photovoltaic system. Table 1 – Maximum time of disconnection from the grid for abrupt changes in leakage current, according to IEC62109-2. Change in root-mean-square value of leakage current

Maximum disconnection time from grid

30 mA

0,30 s

60 mA

0,15 s

150 mA

0,04 s

International standards have established limits regarding the amount of leakage current that a photovoltaic system may contain. International standard IEC 62109-2 on safety in photovoltaic systems, defines that the inverter should be equipped with a leakage current control unit and should be disconnected from the grid in case limits are exceeded (IEC, 2011). This standard proposes two different ways of monitoring this. The first option is to use a Differential Residual (DR) current device of 30mA. The second form is by applying a Residual Current Monitoring System (RCMS). Should the RCMS be employed, then the inverter has to be disconnected from the grid for 0.3 seconds if the leakage current is higher than 300mA. If the inverter has a capacity of more than 30kVA, the maximum leakage current should be increased with 10mA for each additional kVA. The inverter has to be disconnected from the grid in case the RCMS detects abrupt changes in the leakage current, as mentioned in Table 1. This detects an electric shock in case someone touches an electrically loaded conductor.

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GOALS The general goal of this monography is to present the research on and development of a three-phase transformerless inverter for connecting photovoltaic systems to the grid. The inverter should inject currents to the grid without compromising the degree of harmonic distortion as set within the national standards for grid-connected photovoltaic systems. Furthermore, the inverter should be highly efficient and keep the leakage current of the photovoltaic system within existing regulatory frameworks. It has to be stressed that this work is the result of a master’s thesis in Electrical Engineering and fruit of a project in Investigation and Development (P&D) with Schneider Electric. This work has the following specific objectives: • Undertake research on the phenomenon of leakage current in transformerless grid-connected photovoltaic systems; • Come up with topology for high efficiency for the three-phase transformerless invertor; • Implementation of method for reducing leakage current in the photovoltaic system by topological research; • Experimental verification of theoretical results. STRUCTURE OF MONOGRAPHY This monography consists of four chapters, including the introduction and final remarks. Chapter 2 presents the phenomenon of leakage current in transformerless photovoltaic systems as well as the topology of the photovoltaic inverter. The experimental results are outlined in Chapter 3. At the end, some final thoughts regarding this monography are presented to provide an overview of its main contributions.

TOPOLOGICAL DESCRIPTION OF THE PHOTOVOLTAIC INVERTER INTRODUCTION This chapter will first describe the phenomenon of leakage current in transformerless photovoltaic systems and point out how stray capacitances appear, how these could be modified, where leakage current comes from and what its negative effects are on the system. Furthermore, this chapter outlines the topology of the designed photovoltaic inverter and the structure of the grid-connected passive filter. This filter not only reduces harmonics in the grid current, but also diminishes leakage current in the photovoltaic system. Finally, some aspects regarding this filter draft will be addressed. LEAKAGE CURRENT Voltage fluctuations across the stray capacitances that exist between the photovoltaic module terminals and its grounded frame, create leakage current. Parasitic capacitances in photovoltaic systems rise from the specific features of the materials that were used for fabrication of the photovoltaic module.

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As we can observe in Figure 9, these capacitances are found between the cells, on the side of the module and within the top glass layer (CALAIS et al., 1999). The value of the capacitance is defined essentially by constructive factors, like the features of the material used for construction of the module, and by external factors as humidity and accumulation of dirt on the surface of the module (SUAN et al., 2011). The larger the module surface, the more these capacitances increase. In photovoltaic systems of a considerable size, these capacitances can reach values of around 50 to 150 nF/kW in crystalline silicon modules, depending on climate conditions and structure of the modules (MYRZIK e CALAIS, 2003). When stray capacitances are affected by voltage fluctuations, leakage currents are created that flow through the inverter’s circuit, inject into the grid and then return to the grounded terminal, as shown in Figure 10. A connecting capacitator on the negative terminal of the photovoltaic module can alleviate the effect of the parasitic capacitance (GUBÍA et al., 2007), as illustrated in Figure 10. Cp

Glass

N P

Cp

Cp Cp

Substrate

Cp

Cp

N P

Cp Cp

N P

Frame

Cp Cp

Figure 9 – Schematic draft of parasitic capacitances in a photovoltaic module. Inverter

Filter

Grid iƒ

PV

Vp

+ -

Cp

Figure 10 – Illustration of leakage current flow. The more voltage fluctuations increase in frequency and amplitude, the higher the leakage current will be. This could be verified by analysing the equation on the current that flows through a capacitor in correlation with its terminal’s voltage fluctuation: dvp (t) iƒ (t) = Cp , (1) dt Where vp is voltage across the parasitic capacitance. This explains why the scale of the leakage current rests on the value of the parasitic capacitance and the voltage fluctuations across it. Once undesirable power components fluctuate through the grid, the leakage current can negatively affect the quality of the current that is injected to the grid (CALAIS et al., 1999). Another side effect of the leakage

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Eco_Lógicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.


current is the increase of high frequency noises that cause electromagnetic compatibility issues within the inverter and can interfere with the performance of other nearby devices. As previously stated, the scale of the leakage current is directly connected to the voltage fluctuations across the stray capacitance. These fluctuations basically depend on the topology of the inverter (and the output filter) and the applied modulation strategy (KEREKES et al., 2007). In order to present the measures that need to be taken to minimize the impact of the circulation of leakage current in transformerless three-phase inverters, it is necessary to find the mathematical relationship for voltage across parasitic capacitances according to the features of the inverter. For this, a three-phase inverter with three cables that is connected to the grid via an inductive filter is introduced, as illustrated in Figure 11. Inverter a b

PV

c vcn Vp

n

La

ia

Lb

ib

Lc

ic

va vb vc

g

vcg

Cp g

Figure 11 – Three-phase inverter with three cables, connected to grid with inductive filter. When applying KVL (Kirchhoff’s Voltage Law) to the three-arm converter, we get: vp + van - vag = vp + vbn - vbg = vp + vcn - vcg = 0

, (2)

Voltage values Vag, Vbg and Vcg stand for voltage drop on filter inductances added up with the mains voltages. Adding up the three equations in (2) and hereby singling out voltage across the parasitic capacitance, we get this: vag + vbg + vcg — (van + vbn + vcn ) . (3) vp = + 3 3 The first part of the equation (3) is known as common mode voltage in the inverter and represents the average voltage wavelengths from the inverter’s arms: van + vbn + vcn . (4) vcmv = 3 As defined previously, voltages va, vb and vc are obtained by summing the mains (va, vb and vc) with the voltage drops in the filter (vLa, vLb and vLb) so we have:

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vag + vbg + vcg = vLa + va + vLb + vb + vLc + vc

. (5)

Considering that three-phase mains voltages are sinusoidal and balanced (va + vb + vc = 0) we get: vag + vbg + vcg = vLa + vLb+ vLc

. (6)

By substituting (4) and (6) in (3) we get the following formulation: v +v +v

. (7) vp = —vcmv + La Lb Lc 3

When analysing the equation (7), we can deduct that voltage across the stray capacitance is fundamentally based on common mode voltage and voltage drops on filter inductances. In this way, assuming that the filter inductances are identical (La = Lb = Lc = L), the equation (7) could be shown as a common mode equivalent circuit, as pointed out in Figure 12. -

n

+

iƒ Vp

L 3

+ -

g

Cp g

Figure 12 – Equivalent common mode circuit for three-phase inverter with inductive filter However, modulation techniques applied to three-phase inverters mostly result in common mode voltage of high amplitude and frequency, leading to mayor circulation of leakage currents. In this regard, many authors have tried to change the topology of the inverters and apply modulation techniques that reduce the leakage current in transformerless photovoltaic systems. There are some techniques we can define that could be used to diminish the leakage current: • Use of fourth wire: Use of inverters that enable a fourth wire to be connected to the mains earth and to the centre of the capacitator divider of the DC busbar (KEREKES et al., 2009). This makes that the voltage across the parasitic capacitance stays virtually constant and equal to half of the voltage in the DC busbar, in this way decreasing the leakage current. But would this mitigation method be used, then the impedance of the fourth wire has to be minimalized. Using the fourth wire for grid-connection also carries the disadvantage of limiting the maximum line voltage amplitude generated by the three-phase inverter in the linear region to 86.6% of total voltage in the DC busbar; • Modulation techniques: In case the device doesn’t offer a possibility to implement a fourth wire, an alternative is to use modulation techniques that reduce or eliminate variations in high frequency of common mode voltage. One of the most widely used modulation techniques is the SVPWM (Space Vector Pulse Width Modulation) (PINHEIRO et al., 2005)). Nonetheless, these kind of techniques usually reduce the number of vectors and with this the synthetic voltage capacity of the invertor. Further, the SVPWM modulation is quite complicated to implement;

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Eco_Lógicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.


• Passive filters: leakage current can also be reduced by connecting a passive output filter. One example of this is applying a LCL passive filter with the common point of capacitators connected to the centre of the DC distribution bar (DONG, 2012). This type of connection creates less impedance for the leakage current, minimizing its circulation through the grid. Reducing leakage current via this method doesn’t require any modifications in the inverter’s topology nor adding extra components to the system. The modulation strategy doesn’t have to be adjusted either if the synthetic voltage capacity of the invertor stays the same. However, a careful study of its elements is required so that the leakage current could effectively be reduced without causing instability issues with regards to the grid-connection; • Modifications in the topology of the inverter: There are still techniques that modify mainstream topologies of inverters to limit leakage current circulation (VAZQUEZ et al., 2010; BRADASCHIA et al., 2011; RODRIGUEZ et al., 2011). On the other hand, the rise in number of semiconductors increases the complexity and cost of the system. These extra elements also increase losses of the inverter and reduce its efficiency. It is therefore important that the inverter developed in this work, incorporates one of these techniques for reducing leakage current in the photovoltaic system. The next section will present the topology of the threephase inverter as well as the method for mitigating leakage current.

TOPOLOGY OF PHOTOVOLTAIC INVERTER High efficiency and quality of generated voltage were criteria for selecting the topology to be developed. Due to its capacity to synthesize three voltage levels per phase and to halving the blocking voltage of the switch, the three-phase three-level NPC inverter (Neutral Point Clamped) (NABAE et al., 1981) was implemented, see also Figure 13. With a growing number of levels, we can synthesize voltages/current with less harmonic content which makes it possible to use smaller filters. Decreasing blocking voltage allows for the use of lower voltage switches that are known to have fewer losses. The parameters of the three-phase inverter are detailed in Table 2. Connection to the grid is made through a three-phase network with three cables of nominal line voltage of 380 V and nominal frequency of 60 Hz. Its rated active power (Pn) was set on 10 kW and it could operate with a power factor (PF) adjustable of 0.9 inductive till 0.9 capacitive, as set out in the regulations (ABNT, 2013). The topology of the output filter is as in a passive LCL filter, as it provides a better attenuation of harmonics and minor volume in comparison with a passive filter that is purely inductive (L filter). However, the LCL filter needs a damping method for its resonant peak to avoid possible disruption in its control system. In this application, passive damping was applied as it is a low cost solution and easy to implement. The damping is represented by the resistor Rd as shown in Figure 13.

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p

E 2

+

D1a

-

C1

S2a

PV

D2a E 2 Cp

S1a

+

S1a

L1

Cn S2a

-

a b c

L2 Rd

a’ b’ c’

vg

Cd

C2 n

Figure 13 – Three-phase grid-connected NPC inverter Table 2 – Specifications of three-phase NPC inverter

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Specification

Value

Rated active power

10 kW

Electrical grid

380 V 3 Φ/60 Hz

Power factor

0,9i – 0,9c (adjustable)

Nominal DC input voltage

700 V – 800 V

Maximum DC input voltage

1000 V

Minimum DC input voltage

630 V

Inductance on output of inverter (L1)

1,1 mH

Inductance of grid-connection (L2)

200 μH

Capacitance Cd

15 μF

Capacitance Cn

10 μF

Damping resistance (Rd)

Switching frequency (fs)

7,68 kHz

Sampling frequency (fa)

15,36 kHz

Eco_Lógicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.


Unlike the classical LCL filters, the filter used in this work is structurally modified so that it could help reducing the leakage current in the photovoltaic system. This modification is made at the connection of the common point of the filter capacitators at the centre of the DC distribution bar (o point), characterized by a modified LCL filter (LCLM). The LCLM filter in this way diminishes impedance for the leakage current, lessening its circulation through the grid (DONG, 2012). This technique for reducing the leakage current, moreover, overcomes the disadvantages related to the techniques presented in the previous section, as under-utilization of the DC busbar and modifications in the topology of the inverter are being avoided. In order to get a clearer picture, we could examine the equivalent common mode circuit for the LCLM passive filter, as presented in Figure 14. The capacitive frame shaped by capacitators Cd and Cn makes sure high frequency voltage variations across the stray capacitance are minimized, hereby reducing the leakage current. However, the LCLM loses efficiency with the insertion of the damping resistor Rd. The reason for this is that the inclusion of a resisting element in the capacitive frame increases its impedance and in this way affects the attenuation of the leakage current. As the resistor is needed to guarantee a steady control system, careful research on this component has to be undertaken in order for it to control the leakage current of the photovoltaic system and keep the system stable. The damping resistor was set up based on two main criteria: gain margin of the control system and root mean square of leakage current. The gain margin provides stability information on the system that is responsible for controlling the currents injected into the grid.

L1 3

+

Rd 3 3Cn

-

Vcmv

Cp

L2 3

3Cd

g

g

Figure 14 – Common mode equivalent circuit for LCLM filter. Figure 15 represents the curves created for the draft on Rd resistance. For parasitic capacitance, a value of 1,25 μF was taken, which falls within the normal values for crystalline silicon modules (50-150 nF/kW) (CALAIS et al., 1999). The curve in Figure 15 (a) shows the leakage current RMS in relation to the damping resistance. As the damping resistance increases, the leakage current augments. This is due to the growth of the impedance of the capacitive frame of the LCLM filter that hence harms the attenuation of high frequency elements The maximum damping resistance (Rd,máx) is given by the intersection of the curve of the leakage current RMS with the line that narrows the limit imposed by regulation IEC 62109-2. As for the parameters used in this analysis, the maximum damping resistance value is 4 Ω. In Figure 15 (b) the correlation between the gain margin of the control system and the damping resistance is shown. A negative gain margin value indicates that the system is unstable. We can infer that as the damping resistances decreases, the gain margin also decreases, leading to instability of the system. From

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this curve, a minimum damping resistance value for maintaining stability is defined (Rd,mín). In this case the value is 0,2 Ω. This work therefore applied a damping resistor of 1 Ω in order to meet the previously mentioned criteria. 0,5 Does not meet IEC 62109-2

0,4 0,35 0,3 0,25

Rd,máx = 4,0 Ω

0,2

Meets IEC 62109-2

0,15 0,1 0,05 0,0 0

1

2

3 4 5 6 7 Damping resistance (Ω) (a)

8

12 10 8 6 4 2 0 -2 -4 -6 -8 -10 0

Gain margin (dB)

Leakage current (A)

0,45

9

10

Stable

Rd,mín = 0,2 Ω

2

Unstable

4 6 Damping resistance (Ω) (b)

8

10

Figure 15 – Selection of damping resistor: (a) leakage current in relation to Rd; (b) gain margin in relation to Rd. Geometrical scaling for carriers was used as the inverter’s modulation technique (GRIGOLETTO e PINHEIRO, 2009)2009. This modulation is characterized by its easy implementation and for enabling voltage maximisation from the DC busbar. In relation to the control system of the inverter, this modulation technique principally tries to control the flow of active and reactive power through the grid- injected current. Current control is widely used to provide greater safety, stability and fast transient response. The purpose of the control system of the inverter is to inject sinusoidal currents into the grid while controlling the power factor. It also has to regulate the DC busbar voltage to adjust to an adequate level for connection to the grid, and balance voltages in the DC busbar capacitators. The DC busbar voltage reference is imposed by the maximum power point tracking algorithm from the photovoltaic modules. FINAL CONSIDERATIONS This chapter presented several points related to the generation and mitigation of leakage current in transformerless grid-connected three-phase photovoltaic systems. It also showed that generation of leakage current is directly related to the common mode voltage generated by the inverter. Based on this, several mitigation techniques were described, each with its advantages and disadvantages. The NPC inverter and the selected topology for its employment were then introduced. The selection was mainly based on criteria as high efficiency and voltage quality. The passive LCLM filter was used as a mitigation method for the leakage current, as it avoids under-utilisation of the DC busbar and doesn’t require modifications in the inverter’s topology. Besides, the LCLM filter helps attenuating harmonics existent in the currents injected into the grid.

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Eco_Lógicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.


EXPERIMENTAL RESULTS INTRODUCTION The prototype that was developed in the laboratory is shown in Figure 16. Its approximate sizes are as following: 85 cm long, 65 cm deep and 36 cm high. The assembly could be subdivided in three principal pieces: inverter, output filter and network interface. The control of the inverter was completely digitally produced, using a digital signal processor of the type TMS230F28335, from Texas Instruments. Three single-phase NPC modules, fabricated by Semikron (model SKiM301MLI12E4) were used for the semiconductor devices. The modules apply the SKiM 4 encapsulation technology that provides a longer life for the semiconductors (ZANIN, 2014). Driver circuits produced by Semikron were used for the drive of semiconductors. For the LCLM output filter, inductors with magnetic grain oriented (GO) material were utilized, as they are associated with fewer losses on lower frequencies. The function of the network interface is mostly to connect/disconnect the grid through contactors and to preload the LCLM filter capacitators, avoiding high current peaks to occur. It is important to stress that all assemblage and the set-up of the draft of the inverter was carried out in the research laboratory of the university. Next sections will present the experimental results of the NPC transformerless three-phase gridconnected inverter.

Interphase to network DSP Plate

Pre-charging circuit

Add-in CC Board

Driver LCLM Filter

Capacitor CC Board

NPC Module

Figure 16 – Developed inverter

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RESULTS Figure 17 (a) gives a presentation of three-phase currents injected into the grid while Figure 17(b) shows voltage in the grid and the current in the a phase for the unity power factor, referring to the non-injection of fundamental reactive power in the grid. The total harmonic current distortion (THD) was 3.25%. This value falls within the norm ABNT NBR 16149 for grid-connected photovoltaic systems that stipulates a maximum limit of THD of 5% in relation to the fundamental component in rated power of the inverter(ABNT, 2013). Figure 17 (b) displays grid voltage and injected current in the a phase and it could be observed that both find themselves synchronized and have a basically united power factor (0.9989), also in line with ABNT NBR 16149. These results highlight the capacity of the inverter to inject controlled currents of high quality into the grid.

(a)

(b)

Figure 17 – Experimental results: (a) grid current (10 A/div); (b) Voltage (100 V/div) and current (20 A/div) in the a phase for unity power factor.

(a)

(b)

Figure 18 – Experimental results: (a) current in phase a (20 A/div) and synthesized line voltages in output of inverter (500 V/div); (b) Grid voltage (200 V/div), current in phase a (50 A/div) and voltages in capacitators of DC busbar (200 V/div) for unity power factor.

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Eco_Lógicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.


Figure 18(a) shows synthesized line voltages in the output of the inverter and the injected current in the a phase. This result makes it possible to verify that the three-phase NPC inverter is capable to synthesize five levels in line voltage outputs, allowing for a reduction of the electromagnetic interference generated by the inverter, and also enabling the use of magnetic components of less volume. Voltages in DC busbar capacitators (vc1 e vc2) are illustrated in Figure 18(b), together with the voltage and current in the grid. It could be observed that DC voltage levels in the capacitators are balanced (vc1 = 349,3 V, vc2 = 350,2 V), and that the current in the a phase is synthesized with the respective grid voltage. This result indicates that the control system of the inverter is also capable of regulating voltages of the DC busbars, avoiding in this way damage to a semiconductor in the event of voltage unbalance. Operation with a 0.9 inductive power factor 0.9 is presented in Figure 19(a), while operation with capacitive power factor 0.9 is shown in Figure 19(b). Voltages in the DC busbar capacitators stayed stable for both the inductive power factor (vc1 = 349,7 V, vc2 = 350,5 V), as for the capacitive power factor (vc1 = 350,1 V, vc2 = 350,8 V). The three-phase inverter thus operates in a satisfactory way, taking and supplying reactive power to the grid. It could also be employed to compensate reactive power and in this manner to improve the voltage levels in the grid. Results for a current level injected into the grid are presented in Figure 20(a). It shows variations of injected power into the grid, ranging from 5kW to 10 kW.

(a)

(b)

Figure 19 – Grid voltage (200 V/div), injected current in phase a (50 A/div and voltages in capacitators of DC busbar (200 V/div): (a) FP = 0.9 inductive (b) FP = 0.9 capacitive. The current injected into the grid demonstrates a satisfactory transient response, complying with nominal current standards. This is an important feature for grid-connected inverters as it shows the capacity of the invertor to adapt rapidly to power fluctuations in the photovoltaic system. Figure 20(b) presents the results on leakage current in the stray capacitance of the photovoltaic system. The root mean square value that was obtained on the leakage current was 287.6 mA, measured with a power analyser of the brand Yokogawa, model WT1800, which has a bandwidth of 5 MHz. This current level is in accordance with the limits defined by IEC regulation 62109-2 that sets 300 mA for photovoltaic inverters with a monitoring unit for the leakage current. The leakage current could be even less in reality, depending on climate factors, if in the tests a stray capacitance of considerable value was applied. This result again shows that the developed inverter complies with existing norms for grid-connected photovoltaic systems.

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(a)

(b)

Figure 20 – Experimental results: (a) Grid voltage (100 V/div) current in phase a (20 A/div) power level (5 kW → 10 kW); (b) leakage current (1 A/div), Grid voltage (200 V/div) and current in phase a (50 A/div). The curve on efficiency of the system was also obtained by utilizing the power analysing equipment of Yokogawa, model WT1800. For this test a united power factor was taken into account. The efficiency curve in relation to the active power injected into the grid is shown in Figure 21. In this case three curves were obtained: overall efficiency, efficiency of LCLM filter and efficiency of the inverter. The checked maximum overall efficiency (inverter + filter) was 96.24% on a level of 10 kW. Regarding the inverter’s performance, a maximum value of 98.43% was obtained, also in 10 kW. 100 98

Efficiency (%)

96 94 92 90 88 86 84

Global Inverter Filter

82 80

1

2

3

4

5 Power (kW)

6

7

8

9

10

Figure 21 – Experiment efficiency curve according to active power injected in the grid

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Eco_Lógicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.


In virtue of the efficiency curves shown in Figure 21, and taking into account a power level of 10 kW, of all the losses, 59.15% correlate with losses in the filter and 40.85% correspond to losses in the inverter. The efficiency levels that are obtained are quite satisfactory as they are consistent with most commercial photovoltaic inverters. As most losses originate from the output filters, there exists an opportunity to increase overall efficiency even more, as an optimized design of the magnetic elements would reduce losses in this phase. FINAL CONSIDERATIONS This chapter presented the experimental results on the three-phase grid-connected transformerless NPC inverter. It proved that the inverter is capable to synthesize sinusoidal currents with low harmonic content (THDi = 3,25%), complying in this way with regulation ABNT NBR 16149. Also highlighted was the working of the three-phase inverter for different power factor values and by applying power levels (5 kW → 10 kW), all leading to a satisfactory performance. After, the experimental results on the leakage current in the photovoltaic system were presented, evidencing the fulfilment of regulation IEC 62109-2. The last part demonstrated a maximum overall efficiency of the system of 96.24% which is a quite satisfying result. CONCLUSIONS Transformerless grid-connected photovoltaic inverters have a high efficiency level due to the omission of the transformer. The transformer weighs a lot and, depending on the frequency of its use, has a high volume and cost. On the other hand, taking away galvanic isolation from the photovoltaic system and the grid could cause safety hazards and would provide a route for leakage current to circulate. The main objective of this monography, therefore, was to design a transformerless three-phase grid-connected photovoltaic inverter that is highly efficient and complies with the standards set for leakage current and harmonic content of grid-injected currents. In Chapter 2, several aspects related to the generation and mitigation of leakage current in transformerless three-phase grid-connected photovoltaic systems were presented. It verified that leakage current generation is directly linked to the common mode voltage of the inverter. Subsequently, an overview of some of the most used techniques for its mitigation were presented. It gave a description of the required topology needed for the inverter to be designed, with a given preference for the topology of a three-phase inverter with a connected neutral. In this selection process, criteria as high efficiency and voltage quality were considered. A LCL modified filter (LCLM) with passive damping was applied for connection to the grid and to reduce leakage. Lastly, Chapter 3 gave an overview of the main experimental results that were obtained on the transformerless three-phase grid-connected NPC inverter in order to validate the theory presented throughout this monography. It verified the ability of the inverter to synthesize sinusoidal currents with low harmonic content (THDi = 3,25%) and hence showed its compliance with regulation ABNT NBR 16149. The next part included the experimental results on the leakage current in the photovoltaic system and confirmed compliance with standard IEC 62109-2. Finally, maximum overall efficiency was set to be 96.24%, a value compatible with most commercial photovoltaic inverters. The objectives of this work were therefore successfully met as the designed inverter exhibits a high efficiency level and also adhered to the mentioned standards. A more profound study is still needed to verify the feasibility of introducing the inverter into the market and to check on its suitability to comply with the required test procedures as defined by national and international standards.

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BIBLIOGRAPHIC REFERENCES ABNT. NBR 16149 - Sistemas fotovoltaicos (FV) - Características da interface de conexão com a rede elétrica de distribuição. Rio de Janeiro, 2013. ANEEL. Procedimentos de Distribuição de Energia Elétrica no Sistema Elétrico Nacional – PRODIST. Módulo 3 – Acesso ao Sistema de Distribuição, 2012a. ______. Resolução normativa nº 482, 2012b. BRADASCHIA, F. et al. Modulation for Three-Phase Transformerless Z-Source Inverter to Reduce Leakage Currents in Photovoltaic Systems. IEEE Transactions on Industrial Electronics, v. 58, n. 12, p. 5385-5395, 2011. CALAIS, M. et al. Multilevel converters for single-phase grid connected photovoltaic systems Solar Energy, v. 66, p. 325-335, Aug. 1999. DONG, D. Ac-dc Bus-interface Bi-directional Converters in Renewable Energy Systems. 2012. Ph.D. dissertation (Doctor of Philosophy in Electrical Engineering). Virginia Polytechnic Institute and State University, Virginia, 2012. EIA. International Energy Statistics. 2015. Disponível em: < http://www.eia.gov/cfapps/ipdbproject/ IEDIndex3.cfm?tid=2&pid=2&aid=2 >. Acesso em: 27 de Janeiro de 2015. EPE. Balanço Energético Nacional 2014: Ano base 2013. Rio de Janeiro. 2014 EPIA. Global Market Outlook for Photovoltaics 2014-2018. Bruxelas. 2014 FARIAS, A. M. D. Técnicas de Modulação para Inversores Fotovoltaicos sem Transformador Conectados à Rede Elétrica. 2011. Dissertação (Mestrado em Engenharia Elétrica). Universidade Federal de Pernanbuco, Recife, 2011. GONZALEZ, R. et al. Transformerless Single-Phase Multilevel-Based Photovoltaic Inverter. IEEE Transactions on Industrial Electronics, v. 55, n. 7, p. 2694-2702, 2008. GONZALEZ, R. et al. High-Efficiency Transformerless Single-phase Photovoltaic Inverter. 12th International Power Electronics and Motion Control Conference, 2006. GRIGOLETTO, F. B.; PINHEIRO, H. Generalized PWM approach for DC capacitors voltage balancing in diodeclamped multilevel converters. 13th European Conference on Power Electronics and Applications, 2009. GUBÍA, E. et al. Ground currents in single-phase transformerless photovoltaic systems. Progress in Photovoltaics: Research and Applications, v. 15, n. 7, p. 629-650, Nov. 2007. IEA. Key World Energy Statistics. Paris. 2014a ______. Medium-term Renewable Energy Market Report 2014. Madri. 2014b

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IEC. IEC 62109-2 - Safety for power converters for use in photovoltaic power systems. Part 2: Particular requirements for inverters. Genebra: IEC 2011. KEREKES, T. et al. Evaluation of Three-Phase Transformerless Photovoltaic Inverter Topologies. IEEE Transactions onPower Electronics, v. 24, n. 9, p. 2202-2211, 2009. KEREKES, T. et al. Transformerless Photovoltaic Inverters Connected to the Grid. Twenty Second Annual IEEE Applied Power Electronics Conference, 2007. LOPEZ, O. et al. Eliminating ground current in a transformerless photovoltaic application. IEEE Power Engineering Society General Meeting, 2007. LUQUE, A.; HEGEDUS, S. Handbook of Photovoltaic Science and Engineering. 2. ed. Madri: Wiley, 2011. MARANGONI, F. Inversores Monofásicos para Conexão de Sistemas Fotovoltaicos à Rede. 2012. Dissertação (Mestrado em Engenharia Elétrica). Universidade Tecnológica Federal do Paraná, Pato Branco, 2012. MYRZIK, J. M. A.; CALAIS, M. String and module integrated inverters for single-phase grid connected photovoltaic systems - a review. IEEE Bologna Power Tech Conference Proceedings, 2003, 23-26 June 2003. p.8 pp. Vol.2. NABAE, A. et al. A New Neutral-Point-Clamped PWM Inverter. IEEE Transactions on Industry Applications, v. IA-17, n. 5, p. 518-523, 1981. PINHEIRO, H. et al. Modulação Space Vector para Inversores Alimentados em Tensão: uma Abordagem Unificada. Revista Controle & Automação, v. 16, n. 1, 2005. RODRIGUEZ, P. et al. Constant common mode voltage modulation strategy for the FB10 power converter. IEEE Energy Conversion Congress and Exposition (ECCE), 2011. SUAN, F. T. K. et al. Modeling, analysis and control of various types of transformerless grid connected PV inverters. IEEE First Conference on Clean Energy and Technology (CET), 2011. TEODORESCU, R. et al. Grid Converters for Photovoltaic and Wind Power Systems. John Wiley & Sons, 2010. VAZQUEZ, G. et al. A photovoltaic three-phase topology to reduce Common Mode Voltage. IEEE International Symposium on Industrial Electronics (ISIE), 2010. VILLALVA, M. G.; GAZOLI, J. R. Energia solar fotovoltaica: conceitos e aplicações. São Paulo: Érica, 2012. ZANIN, A. SKIM IGBT Modules. Mounting Instructions: Semikron 2014.

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BRASIL GENERATING POWER FROM WIND SOURCES IN RIO GRANDE DO NORTE LUZIENE DANTAS DE MACEDO Advisor: Jose Bonifacio de Sousa Amaral Filho, PhD

ABSTRACT

This paper aims to explain the presence of the wind industry in Rio Grande do Norte and explore the business opportunities it creates. Therefore, we discuss the wind energy potential in the state, and the benefits and challenges faced. Through a theoretical and empirical study involving the literature, official documents and secondary data collection, the following findings are offered. The Northeast region has the best wind potential in the country, so a production chain has been created in states that offer the economic scope and governmental capacity needed to introduce specific policies to attract wind equipment manufacturers, as is the case of Bahia, Pernambuco and Ceara. Rio Grande do Norte is remarkable in terms of the number of wind projects under construction, approved and in operation. However, this state does not have a consolidated supply chain because bottlenecks keep this state from attracting an equipment supply chain to reflect the number of wind farms or routes implemented. Having wind alone is not enough. It is necessary to support this industry with power transmission/logistical infrastructure and financial incentives. Otherwise, the state will lose competitiveness in the medium and long term and its ability to take advantage of the opportunities the sector offers for socioeconomic development in areas where wind farms are being deployed. KEYWORDS: Energy policy - Rio Grande do Norte (RN); Wind energy

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INTRODUCTION

The global interest in new renewable energy sources1 is emerging from a new approach to energy policies, centered on the need to diversify sources vis-à-vis fossil fuels to ensure energy security and address greenhouse gas emissions to stabilize climate change at 2 degrees Celsius. Since the energy industry is a major cause of such emissions, it makes sense for countries to propose policies that would mitigate the environmental damage caused by energy production based on fossil fuels. The electricity field is paying special attention to the importance of wind power in its energy mix, because wind technologies are well established in the world and thus have more competitive costs. In fact, it is so important that from 2005 to 2014, the cumulative installed wind power capacity registered a growth rate of 22.6% per year, dominated in 2014 by China, USA, Germany, Spain, and India, which according to the GWEC (2006; 2014) held 71.7% of global installed wind power capacities. In 2014, Brazil had 1.6% of the global cumulative installed capacity in MW, ranking among the ten countries with the largest production structures, according to the GWEC (2014), only behind Italy, France and Canada. In the case of new installed capacity, Brazil added 2,472 MW, coming in 4th among the 10 countries that increased their installed wind power capacity the most in that year. However, in contrast to what is happening in countries such as China, USA and Germany, where the wind power market has stabilized in relative terms, Brazil has had an expanding market under two federal initiatives, regarded as driving the inclusion of this source in the energy mix: the program to incentivize alternative power sources (PROINFA – Programa de Incentivo a las Fuentes Alternativas de Energía Eléctrica) in 2002, and the wind energy contract auctions regulated since 2009. We should stress that these two initiatives only became possible when the new Brazilian Electric System was implemented in 2003 to regain sectoral planning, for which the energy research company (EPE – Empresa de Pesquisa Energética) was established in 2004 to ensure that growth in power supplies would privilege social and environmental concerns. So it was that wind energy became part of EPE’s planning and research, which enabled its inclusion it as a protected investment for its development as an energy source. This facilitated electricity planning through the National Energy Plan to 2030 (PNE 2030) and the Ten-Year Electricity Expansion Plan (PDEE 2006-2015) developed in 2007 and 2006, respectively, and wind energy was positioned as a focus of the country’s energy policy. The 2008-2017 and 2019 PDEEs developed in

1 This paper includes wind, solar, biomass, and small hydro plants (SHPs) among the new renewable energy sources available for power generation to supplement the existing hydropower plants. Since it is not possible to build new large hydroelectric plants in Brazil, it is time to consider diversifying the energy mix with new alternative sources, not to replace hydropower, but as a vital opportunity to ensure energy security. Therefore, this paper will use the term “new renewable” energy sources instead of “new alternatives” or “alternatives” to highlight that all of these sources should have a share in the national energy mix, not as an alternative to replace existing sources for generation, but as vital sources in times of gradual depletion of the country’s water resources.

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Eco_Lógicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.


2009 and 2010, respectively, made the inclusion of wind power in energy mix planning and diversification more transparent as its share in the country’s power generation capacity began to grow and consolidate. Projections of this energy source for power generation grew in the 2020, 2021 and 2022 PDEEs, with the latter marking a contract expansion of 15,658 MW by 2022 compared to 1,805 MW in 2012, for an annual growth rate of 25.5% and a 9.5% share of this source in the total installed capacity of the power grid. According to data from the Brazilian wind energy association ABEEólica (Associação Brasileira de Energia Eólica) at Jan. 2015, this country’s wind potential was 16,138.8 MW, concentrated primarily in three regions: Northeast, South and Southeast. The Northeast region had more than 80% of all wind energy capacity in commercial operation in the country (under construction and contracted), with plants distributed primarily in the states of Ceara, Rio Grande do Norte, Bahia, and Paraiba. The South Region holds second place in the rankings with 2,329.6 MW, primarily in the states of Rio Grande do Sul and Santa Catarina. The Northeast region has the largest wind generation capacity, with over 50% of Brazil’s overall potential. This attests to the importance of wind for the region, where wind speeds and quality in the coastal and semi-arid areas ensure power generation in a context of scarce water resources. In 2014, Rio Grande do Norte (RN) ranked first among the Brazilian states as having the largest wind power capacity in commercial operation, with a total of 1,557.3 MW. In terms of total wind power, Rio Grande also takes first place with a total of 4,492.2 MW, which is 27.8% of all projects that are approved, under construction, contracted, in operation, and undergoing tests in the country. However, most manufacturers of wind power equipment are not based in this state, but rather in those of Ceara, Bahia and Pernambuco. Interestingly, the Port of Suape (PE) and the Camaçari Industrial Complex (BA) are setting up an entire wind supply chain, while 5.7% of all contracted projects are in the state of Pernambuco and 25.8% are in Bahia. Therefore, the general concern of this study is to answer the following questions: i) What are the basic factors and prospects for wind energy production in the state of Rio Grande do Norte? and ii) Although Rio Grande do Norte stands out among the Northeast states as having the best conditions for wind energy production, why is most of the equipment supply chain not located in that state? Based on these questions, the overall aim of this study is to explain the presence of the wind industry in Rio Grande do Norte and to explore the business opportunities that it is creating. Specifically, it seeks to analyze the potential for wind generation in Rio Grande do Norte and to discuss its development in terms of investments made in the wind industry, as well as aspects of its promotion in that state. This paper seeks to demonstrate the following: i) Governmental efforts to diversify the energy mix, which led to structuring the national wind industry, have become a policy aimed at developing to Northeast region to internalize all the links of that industry’s production chain. ii) However, in the case of Rio Grande do Norte, the wind sector has focused on only a few links of the production chain, which limits the transformational potential of the productive structure and distribution of the income generated by that business. This study is a theoretical/empirical analysis of the development of the wind business in Rio Grande do Norte. Its practical basis for discussion is a review of the literature on the subject matter and the collection of secondary data.

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From a methodological perspective, this study can be classified as exploratory, as it includes a survey of the literature, official documents and legislation, in addition to Internet searches. Most data was collected from official documents, particularly the websites of the Agência Nacional de Energia Elétrica (ANEEL), the Empresa de Pesquisa Energética (EPE), the Ministério de Minas e Energia (MME), the Cámara de Comercialización de Energía Eléctrica (CCEE), and Centrais Elétricas Brasileiras (Eletrobras), as well as other Web sources such as the Jornal da Energía, the Tribuna do Norte, the journal Brasil Energía, and the Associação Brasileira de Energia Eólica (ABEEólica). This work is divided into three sections, in addition to this introduction and the conclusions. The first is a review of the wind power potential in Rio Grande do Norte; the second presents state-level aspects of specific policies aimed to promote this source in the state; and the last section analyzes the wind generation capacity in Rio Grande, its benefits, and the challenges to be faced in the process of developing the wind industry. WIND ENERGY POTENTIAL IN RIO GRANDE DO NORTE The state of Rio Grande do Norte stands out in the country’s wind energy industry, as its coastal and semiarid areas have significant wind potential that can be harnessed to generate electricity. The coastal area has sandy dunes formed by the trade winds (COSERN, 2003), with annual averages ranging from 6m/s to 9m/s. The semi-arid area has a dry climate with constant winds, which hold great potential for wind generation. The combination of hot weather (average temperatures over 18 °C), long dry periods (7 to 8 months per year) and less than 750mm/year of rainfall (Amarante et al., 2001) makes it an ideal place to develop this activity, which depends on an abundant natural resource (wind) to turn it into electricity production capacity. Its wind potential was measured by an initiative of the Companhia Energética do Rio Grande do Norte (COSERN) with Iberdrola Empreendimentos do Brasil S.A. (IBENBRASIL), which installed eight 48-meter towers in Guamare, Touros, Tabatinga, Porto do Mangue, Pedra Grande (coastal), Lago Novo (interior), Serra do Mel, and São Miguel (in the west). Observing the average and maximum wind speeds enabled them to gather two important pieces of information: i) The regions with the strongest yearly average winds are located along the northern coastline and in the central highlands. ii) It is possible to complement these two areas according to the following seasonal wind variations. On the coast, with its sea and land breezes, the strongest winds are between late morning and mid-afternoon along a coastal belt extending from the Guamare area to Touros-Tabatinga. In the highland regions, the west of the state and in Serra do Mel, Lago Novo and São Miguel, winds are strongest at night, early morning and noon. In this context, it is clear that Rio Grande do Norte has a suitable climate for power generation using wind energy. It is well known for its predominance of trade winds, which are of a planetary scale and have a significant level of constancy (COSERN, 2003), primarily in the late winter and spring months of August to November, when average wind speeds range from 7.5 m/s to 9.5 m/s (as measured from 50-meter towers). This condition is found in the area of Touros, Guamare and Serra do Mel, when average wind speeds reach 9.5 m/s. At those speeds, average generation could reach 1.5 MW/km2, so a capacity factor of 0.47 could generate an estimated 613 GWh of wind energy (COSERN, 2003), which is 16.3% of that state’s total power generation in 2013. This potential will become increasingly significant as wind technologies develop and enable people to operate towers of over 50m to capture higher wind speeds in areas with semi-arid climates (central west and east regions), considering potential sources of capital in that industry due its ability to generate power from wind.

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Eco_Lógicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.


The western region of the state features areas like Areia Branca, Tibau and Porto do Mangue, where towers of over 100m reach average wind speeds of up to 9.5 m/s and can generate 5,116 GWh of wind power, which is more than the 3,756 GWh the state produced in 2013. The 100-meter towers have shown that in the spring there are several points with wind generation capacity, shown as darker-colored areas. These areas cover not only the Rio Grande coastal area, made up of northern coastal municipalities such as Touros, São Miguel do Gostoso and Pedra Grande, also part of the coastal dune line, but also the mountain region with Bodó, Lago Novo and Tenente Laurentino Cruz, in addition to lowland micro-regions in places like Parazinho, Joao Camara and Jandaira. It is no wonder that programs such as PROINFA and the contract auctions have attested to the ability of this energy source to attract capital to that region, driven by this type of activity that can potentially benefit the entire region.

STATE ASPECTS OF SPECIFIC POLICIES TO PROMOTE THIS SOURCE IN THE STATE OF RIO GRANDE DO NORTE This section presents data on the promotion of this energy source in the state of Rio Grande do Norte and highlights state-level aspects of PROINFA and the statewide location of projects contracted through the auction. PROINFA: REGIONAL ASPECTS An important aspect of implementing specific policies to promote wind energy is electricity development in states by setting up a procedure to assess areas where the attraction to generate electricity from new renewable sources ultimately gives way to a structure that is capable of supporting this type activity. In this context, states are redefining their role within the framework of plans that are more consistent with their water resources and socio-environmental contexts. With a view to national energy security, they are seeking to diversify their electricity grids without losing sight of the country’s main features, regardless of the use of renewable/alternative resources for power generation. It still stands that complementarity should be achieved through specific policies to ensure a stable seasonal supply of electricity. By enabling this stability with regional contributions to electric generation using wind power, PROINFA only confirms the importance of this sector. This should become a state policy to continue this incentive so that wind generation will not be merely a backup power system, but rather an essential option for reducing the use of thermoelectric generation based on fossil fuels. This, then, is a strategic approach for any country, because it complements existing sources of electricity. Moreover, it is clear that since the highest capacity factor figures ​​are concentrated in the Northeast region, its states with potential for electricity generation should make this activity an essential aim of their policies for economic development planning. Table 1 shows only the plants in which PROINFA is involved, with average generation data at December 2013, showing the real situation of wind generation, which indicates that the Northeast region offers about four times more potential for wind energy production than other regions.

Eco_Lógicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.

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Table 1. Brazil - PROINFA wind farms - power (MW), number of plants, generation (avg. MW), and capacity factor, by state of the federation, Dec. 2013 Fed. Unit

Power (MW)

Number of plants

Generation (avg.MW)

Capacity factor

RN

100,3

2

32,06

0,32

CE

500,5

14

243,4

0,49

PE

21,3

5

6

0,28

PB

59,5

12

14

0,24

PI

17,8

1

11

0,61

RJ

28,0

1

7

0,25

RS

227,6

5

74,8

0,33

SC

9,0

1

1,7

0,19

Total

964

41

390

0,40

Source: Developed by author from Eletrobras (2014) and CCEE (2014). Ceara and Paraiba stand out in this region for the numbers of projects contracted in PROINFA, and Ceara has the highest productivity, with a capacity factor of 49%. The state of Piaui contracted one project with a capacity factor of 61%, and Rio Grande do Norte contracted two projects with 32% productivity. PROINFA has thus become a door of opportunity to implement wind energy in these areas through specific policies. However, this will increasingly require an effective plan to ensure that wind energy will cover a growing share of the country’s energy mix on the long term. According to a report published in the journal Brasil Energía (issue 400, March 2014), infrastructure will have to grow immensely over the coming years. That is, average growth should enable an increase of 3,000 MW or more per year to reach 20,000-30,000 MW in times of dry reservoirs. This will make it possible “for wind power to come on line instead of costly thermoelectric generation at critical times” (BRASIL ENERGÍA, issue 400, March 2014, p. 20). REGIONAL DISTRIBUTION OF ENERGY AUCTIONS Energy auctions have shown significant potential to include this resource in the country’s energy mix. It is a means for contracting wind power that, unlike the feed-in tariff,2 promotes a certain amount of competition through the lowest-rate criterion.

2 This is a way to ensure that renewable energy producers sell power at a fixed price guaranteed by contract, for a given period of time (usually 5, 10, 15, or 20 years). Currently some 50 countries have some form of feed-in tariff (IEDI, 2011).

42

Eco_Lógicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.


From a regional perspective, the importance of wind generation for the Northeast region can be seen in the number of projects contracted through the auctions held in the country since 2009. This reveals a region’s ability to attract this type of investment, given its wind potential for power generation, which is the primary feature able to attract investors to the region. The second important point is the infrastructure to receive the wind projects auctioned out, i.e., the more the country invests in transmission lines, the more competitive the auctions become.3 The third point is to bring on line more than 2 GW of the wind capacity contracted at the auction, which is the power needed to supply the existing production chain. The share of wind energy at the auctions shows a significant concentration of wind projects in the country, most of which are located in the Northeast region, especially in the states of Rio Grande do Norte, Bahia and Ceara. In the South region, wind power has a 15% share in contract auctions, concentrated primarily in the state of Rio Grande do Sul (Figure 1).

NORTHEAST RS; 100

NORTHEAST; 85

RN; 33

SUL; 15 SE; PI; 0 MA; 9 PE; 3 5

CE; 17

BA; 32

RN BA CE PE PI MA SE SUL RS

Figure 1. Northeast region - Share (%) of states in wind projects auctioned out (MW), 2009-2013. Source: Developed by the author from CCEE (2014). In this context, the regional distribution of awarded projects is an important factor in the contracting environment, because as soon as approved projects/companies/consortia are awarded auction contracts, regional/state location of projects, investment planning, financing, and implementation require initiatives to reduce investment risk, related primarily to transporting the power they generate.4 Thus, auctions essentially bring an important feature to the process: to make these events a regional policy for wind power and manufacturing capacity, in order to complete the production chain where wind farms are to be installed. 3 Considering the delay in completing transmission lines, which hampered the startup of completed wind farms, the current prerequisite to participate in auctions is for wind investors to include investments in transmission lines in their projects, said Elbia Melo, CEO of ABEEólica (Tribuna do Norte, 6/24/2013). 4 It was on this basis that the MME Decree 132 of 04/25/2013, demanded in Art. 5, § 2 that the final outcome of the projects awarded at the 5th Reserve auction of 08/23/2013 should include “the ability to sell electricity through the transformers and transmission lines of basic and border grids.”

Eco_Lógicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.

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Table 2 shows that Rio Grande do Norte was awarded contracts for 3,402 MW, which gave the state a 32.4% share of the total contracted projects in the Northeast region, or 27.7% of all projects contracted in Brazil. This ranks it first among the states that benefited from wind power contracts, ahead of states such as Bahia (3,245 MW), Ceara (1,839 MW) and Pernambuco (811.7 MW). From 2009 to June 2014, Rio Grande do Norte was awarded 126 projects or 31% of total number of projects envisioned for the Northeast region. Table 2. Rio Grande do Norte, outcome of wind power auctions, 2009-2014 Auction

Date

Power (MW) Brazil

Power (MW) NE

Power (MW) RN

Contracted power (avg. MW) - BR

Contracted power (avg. MW) - NE

Contracted power (avg. MW) - RN

No. projects - BR

No. projects - NE

No. projects - RN

2nd LER

14/12/2009

1.805,7

1.619,7

657

753

685

286

71

63

23

2nd LFA

26/08/2010

1.519,6

1.293,8

817,4

643,9

550,3

361,1

50

41

30

3rd LER

26/08/2010

528,2

508,2

247,2

255,1

247,2

116,4

20

19

9

12th Auction A-3/2011

17/08/2011

1.067,7

575,6

52,8

410

211,9

6,4

44

23

2

4th LER

18/08/2011

861,1

728,7

405,4

422,1

360,1

197,7

34

29

15

13th Auction A-5/2011

20/12/2011

976,5

856,9

321,8

452,4

400,8

153,5

39

34

12

15th Auction A-5/2012

14/12/2012

281,9

253,9

-

151,6

140,1

0,0

10

9

0

5th LER

23/08/2013

1.505,2

1.424,7

132

675,5

647,9

56,1

66

62

7

17th Auction A-3/2013

18/11/2013

867,6

541

-

332,5

232,5

0

39

20

0

18th Auction A-5/2013

13/12/2013

2.337,8

2.185,8

684,7

989,6

924

261,3

97

87

25

19th Auction A-3/2014

06/06/2014

551

503

84

265,6

245,2

48,6

21

19

3

11 events

12.302

10.491

3.402

5.351,3

4.645

1.487,1

491

406

126

Source: CCEE (2014). 44

Eco_Lรณgicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.


Rio Grande do Norte was awarded the most contracts at the 2nd Alternative Energy Source Auction of 26/08/2010 (LFA from the Portuguese), having received 30 of the 41 contracts allocated to the Northeast region, or 73.2% of all projects contracted at that auction. Then, at the 4th Reserve Auction of 18/08/2011 (LER from the Portuguese), that state received 15 of the 29 contracts listed for the Northeast region, or 52% of all projects. Figure 2 shows that Rio Grande do Norte took the lead at the first auctions held since 2009. However, as of the 13th New Energy Auction of 20/12/2011 (LEN A-5 from the Portuguese), it began to lose ground to other states of the region that had better infrastructure for processing wind power, such as Ceara, Bahia, Maranhao, and Piaui. Figure 2 shows the main auctions in the region. In terms of average MW contracted, the state of Rio Grande do Norte was awarded a share at 9 auctions, Bahia at 10, Ceara at 9, Pernambuco at 5, Piaui at 4, Maranhao at 2, and Sergipe at one auction. The fact that Rio Grande do Norte lost competitiveness to other states of the region is significant, because the lack of investment in substations and transmission lines is costing that state its leadership at wind contract auctions. According to Elbia Melo, this is not due to a lack of wind, but rather a lack of investment in the industry (TRIBUNA DO NORTE, 6/24/2013).

RN

BA

CE

PE

PI

MA

SE

82.8

65.6 52.9

59.5

54.9

53.8

49

47.1

41.8

38.3 31.8 25

22.1

21.5

15.5 13.1

12.3

22.6 22.5

37.7

35

32.1

18.6

17.2

000

2º LER 2009

1.5

0000

2º LFA 2010

00000

3º LER 2010

00

0000

00 0

14.6 8.7

8.1 3

48.4

0

28.3 18.8 18.7

19.8 20.7

13.5

9.6

8.3 5.3

6.9

0 00 0

00 0

00

00

0

000

12º 4º LER 13º 15º 5º LER 17º 18º 19º Auction 2011 Auction Auction 2013 Auction Auction Auction A-3/2011 A-5/2011A-5/2012 A-5/2013 A-5/2013 A-3/2014

Figure 2. Brazil, Northeast region, state shares (%) at wind energy auctions (avg. MW), 2009-2014 Source: CCEE (2014). Meanwhile, other states are emerging (Piaui, Maranhao and Pernambuco) that also have significant wind energy potential, which Elbia Melo says accounts for their ability to compete with Rio Grande at the auctions. This need compete must be answered if RN is to regain its majority share at upcoming auctions.

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The 2013 auction records of August (5th LER), November (17th A-3) and December (18th A-5), show that RN lost to Bahia and Piaui, whose percentages ranged from 32.1-49%, while RN had percentages of 8.719.8% and no wind power contracts at the 17th auction. At the 18th LEN of December 2013 and the 19th in 2014, it had shares of 28.3% and 19.8% respectively, which put it back in the race for regulated power marketing, but at the 18th LEN it lost to Bahia, which took a 48.4% share, and to Pernambuco at the 19th LEN. From this it is clear, considering the requirement that projects participating in auctions need transmission lines, that the states awarded the largest shares at the 2013 auctions had the best distribution infrastructure for the electricity they generated and, therefore, greater chances that investments would be made in their areas. These investments are important for increasing the yield of the National Interconnected System (SIN from the Portuguese). Transmission is the capacity to distribute electricity produced from various sources, and requires daily planning by the National Electric System Operator (ONS from the Portuguese) to control operations and ensure supply security. Therefore, transmission is an important factor when seeking to expand power generation from any source, due to the pressure on the energy load. Clearly, any obstacles that entrepreneurs face in this regard will determine the ability of a region or state to receive investments in wind generation through contract auctions. At the 19th auction of 06/06/2014, the state of Pernambuco was awarded the largest share (59.5%), followed by Ceara (20.7%) and Rio Grande do Norte (19.8%). By that date, auction outcomes were a warning sign to the latter state that regional restrictions caused by insufficient distribution infrastructure for wind energy production were penalizing it, while the placement of projects in other regions had become a crucial factor that explained the loss of regional competitiveness in the race for such projects at the auctions. In other words, the question of regional project placement is directly related to the lack of sufficient infrastructure to distribute the power that is generated. Rio Grande do Norte is tending towards renewed competitiveness, as initiatives have been taken to overcome several obstacles to investment in transmission lines that came into operation in 2014. This includes completion of a number of CHESF facilities on 02/28/2014 comprising two transmission lines and two substations (in Joao Camara and Extremoz), which enabled the startup of other wind farms whose works had already been completed.

THE WIND INDUSTRY IN RIO GRANDE DO NORTE The purpose for this section is to discuss the benefits that the promotion of wind energy is bringing to Rio Grande do Norte, especially for the municipalities where wind farms are installed, and the challenges the state faces to ensure a privileged position in terms of the number of wind farms in operation, under construction and approved, mostly derived through contracts awarded at the auctions. BENEFITS OF PROMOTING WIND POWER IN RIO GRANDE DO NORTE According to information from ABEEรณlica, obtained through an interview with Melo Elbia in January 2015 and organized in Table 3, Rio Grande do Norte recorded a 27.8% share of all wind energy investments in Brazil, totaling just over R$ 20 million, followed by Bahia with a 25.8% share, Ceara with 15.1%, Rio Grande do Sul 13%, and Piaui 7.6%.

46

Eco_Lรณgicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.


Table 3. Brazil, investments in wind projects (R$ million) by federal units, 2014 State Rio Grande do Norte Approved Under construction Contracted In operation Being tested Bahia Approved Under construction Contracted In operation Ceará Under construction Contracted In operation Rio Grande do Sul Under construction Contracted In operation Being tested Piauí Under construction Contracted In operation Pernambuco Contracted In operation Being tested Maranhão Contracted Santa Catarina In operation Paraíba Contracted In operation Sergipe In operation Rio de Janeiro In operation Paraná In operation Total

Power in MW 4.492,2

Investments (in R$ millions) 20.214,86

304,0 703,6 1.726,6 1.557,3 200,7 4.159,4

18.717,52

25,8

11.001,30

2.090,7

15,1

9.408,04

150,0 981,0 88,0

13,0

675,00 4.414,52 396,00

815,7 24,8 79,9

5,7

1.583,55

236,4

2,2

1.063,80

90,0 69,0 155,25

0,6 0,4 0,2

155,25 126,23

28,1 2,5

1,5 1,0

405,00 310,50

34,5 28,1

2,2 1,5

715,50

34,5

5,1 0,2 0,5

1.583,55 1.063,80

159,0

0,9 6,1 0,5

3.670,65 111,38 359,55

351,9 236,4

2,7 3,9 3,9 2,4 7,6

4.141,58

351,9

0,9 6,6 7,6

1.959,30 2.853,45 2.853,00 1.742,29 5.485,52

920,4

0,6 4,0 16,0 5,2

684,45 4.767,30 5.549,55

435,4 634,1 634,0 387,2 1.219,0

1,9 4,4 10,7 9,6 1,2

403,65 2.905,94 11.620,32 3.787,61

152,1 1.059,4 1.233,2

0,2 0,2

126,23 11,25

2,5 16.138,8

27,8 1.368,00 3.166,20 7.769,70 7.007,81 903,15

89,7 645,8 2.582,3 841,7 2.444,7

% of total

0,2 0,0

11,25 72.624,38

0,0 100

Source: ABEEólica (interview in January 2015).

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As stated above, wind power expansion is concentrated in the Northeast region in terms of both MW and number of farms. Therefore, the number of power projects still under construction in various states of Brazil (Figure 3) “indicates the short term trend for wind project development” (Elbia Melo, from an interview in January 2015).

BA

RN

CE

RS

PE

MA

90 3

13

351.90

815.70 29

41 PI

Total number of wind farms

54

435.4 634.10

981.00

Contracted (MW)

150

53

152.1

93

130

645.8

703.6

1,059.40

1,726.60

2,582.30

Under construction (MW)

PB

Figure 3. Brazil. Short-term development of wind projects by federal units, 2014 Source: ABEEólica (January 2015). According to the data in Figure 3, Rio Grande do Norte only lost to Bahia in short-term wind project development, having a total of 1,726.60 MW in contracted projects and 703.6 MW in projects under construction, with a total of 93 wind farms. Therefore, the prospects for continued growth in wind power shares of Brazil’s total generation capacity are positive for the country in general and for the Northeast region in particular. According to the PDEE 2022, wind power will contribute more than 9% to the energy mix of the country’s overall 17 GW in 2022. According to the FIERN MaisRN study of July 2014, the idea is to expand the installed capacity for wind power to 12.3 GW in 2035, which is almost the capacity of the Itaipu hydro plant (with an installed capacity of 14 GW). This forecast is less optimistic compared with the one submitted by the PDEEs. This expansion will represent a growth rate of nearly 689.8% compared to the installed capacity of Rio Grande do Norte in 2014 (1.5573 GW). Certain strategies will have to be implemented to reach this goal (FIERN, Jul. 2014, p. 41): Encouraging the construction of new wind farms in the Norte, Mossoró and Serras Centrais regions, especially in areas with favorable winds at 100 meters or more; updating the survey of available wind potential in Rio Grande do Norte; promoting research and development of technologies adapted to local climates; investing in training and qualifying the manpower to operate and maintain the machines and equipment for the entire production chain, projects, construction and management; and implementing policies to attract the equipment industry. 48

Eco_Lógicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.


A total investment of R$ 20 million in wind power projects in the state will ensure an installed capacity of 4,492.2 MW in the coming years, including those approved for construction, under construction, contracted, in operation, and being tested (see Table 3), as well as hiring 35,000 workers in RN (at least 20% per year with technical and higher degrees) (TRIBUNA DO NORTE, 5/25/2014). It is important to list some of the benefits that this wind power expansion will offer the state. The first benefit is land leasing, which is important because it offers local benefits and generates income for families who make their land available to install wind farms and can continue using their land to carry out other complementary activities. According to a report published in the journal O Estado de São Paulo of 01/11/2015, each company has a different formula for paying land leases. Accordingly, “some pay a percentage of the energy per installed generator. Depending on the criterion applied, each family can earn R$ 1000 per tower” (O ESTADO DE SÃO PAULO, 01/11/2015). The report cites the case of an owner of 45 hectares of land who has two wind towers of almost 100m on her property and receives R$ 1,300 per month for the lease of part of her lot. Another very interesting case is cited in the municipality of Parazinho, RN, in which an old settlement has seen radical changes in its income structure and landscape due to land leasing. Here, 32 wind turbines were installed, which enabled 29 families to receive R$ 1000 per month each. So “the fruits of this extra income are piles of sand, stone and cement in front of the homes, almost all rebuilt” (O ESTADO DE SÃO PAULO, 01/11/2015). Therefore, the wind is actually reshaping income structures in places where investments are made, with direct payments to landowners for periods of no less than 20 years. This is an important benefit for the people in these localities who now have the chance to receive lease money to help with their expenses. In the case of the Renova firm, which works with land leasing to place their towers, CEO Mathias Becker said, “This model adds huge economic and social value to the regions where we operate, as it provides extra income for people who remain the rightful owners of the land and can continue carrying out their other activities” (MATHIAS BECKER, 2013, p. 20). Accordingly, there should be a sustainable counterpart for the municipalities receiving these investments, such as establishing royalty payments. However, according to Pinto (2013), wind energy pays no royalties because Brazilian law provides that the taxes collected be paid to the states that consume the generated energy and not to those that produce it.5 A second benefit of the wind business is agrarian regularization through INCRA Normative Instruction 76 of 08/23/2013, which regulates the purchase and leasing of rural property by foreign individuals residing in the country or by foreign corporations authorized to operate in Brazil. Melo Elbia states: Currently, wind energy companies have difficulties leasing this land because they have foreign capital. There are also opposing views regarding the contents of this Normative Instruction among notaries, finance banks and the Attorney General. (ELBIA MELO, from an interview in January 2015) 5 There is no tax compensation for states that generate wind power. According to Pinto (2013, p. 308), “There is the concept of an environmental tax that some countries are already implementing. The idea of this tax is that each state that implements environmental conservation activities is entitled to a percentage of the ICM collected. The states of RN, BA, SC, PB, AL, SE, and ES have no legislation on an environmental ICM.”

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Another direct impact of land leasing is that many families have been able to pay off their debts. A report in O Estado de São Paulo, published on 01/11/2015, presented the case of a farm purchased with money from the Banco de la Tierra, whose yearly payments could not be covered by family farming alone. When a wind farm was built on their parcel by CPFL, the company made an agreement with the property owner to repay the debt and deduct the payment from the lease for five years. In this way, “the company paid R$ 500 of every R$ 1000 that each inhabitant was entitled to.” (O ESTADO DE SÃO PAULO, 01/11/2015) A third benefit relates to progress in road infrastructure, which has had positive externalities as roads are paved by companies responsible for wind farms, while some left as dirt roads are now in a better conditions, this according to the newspaper O Estado de São Paulo of 01/11/2015, which cited the example of the road connecting Parazinho and São Miguel do Gostoso. A fourth benefit has to do with taxation. During wind farm construction, prefectures collect a service tax (ISS) for projects installed on lands within the municipality. This is deemed a specific tax for the regions where wind farms are implemented. Furthermore, production chains in areas where wind equipment is installed have certain tax incentives such as the ICMS, PIS and COFINS. Finally, there is the CONFAZ ICMS agreement of 1/10/97 and the special incentive regime for infrastructure development (REIDI from the Portuguese). A fifth benefit relates to the creation of businesses such as shops, restaurants and inns installed next to simple homes, which contrast two different realities that coexist in the same locality or area and are changing people’s lives, as is the case in some of the municipalities mentioned above. These new (and existing) business establishments are starting to operate more dynamically as projects are implemented, and this has a positive impact on the gross local product (GLP) of their municipalities. In the data shown in Table 4, which covers only a few municipalities having large numbers of wind projects in operation, under construction and awarded, the cases of São Miguel do Gostoso, Joao Camara, Pedra Grande, Jandaira, and Bodó stand out because they gained significant positions in the 2012-2008 period in relation to the 2007-2003 period. Table 4. Rio Grande do Norte. Selected municipalities, percentage change of GLP, 2007-2003 and 2012-2008 Municipalities of RN Guamaré Parazinho São Miguel do Gostoso Ceará-Mirim João Câmara Pedra Grande

State position of RN 1st 6th

Var. (%) 2007-2003 457,3 130,5

Var. (%) 2012-2008 0,5 110,6

State position of RN 167th 5th

62nd

73,0

86,9

9th

63rd 79th 128th

73,0 69,5 55,4

62,7 90,6 92,6

42nd 7th 6th

Jandaíra

136th

52,9

59,8

52nd

Bodó

147th

48,1

53,1

79th

Source: Developed by author from IBGE (2014).

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This is the case of São Miguel do Gostoso, which besides being a bedroom community of engineers and executives of large corporations such as CPFL, Energisa and Voltalia (O ESTADO DE SÃO PAULO, 01/11/2015), has an awarded wind power portfolio of 142.8 MW (ANEEL, 06/03/2014), and Pedra Grande, Jandaira and Bodó, which also have wind projects. An important point that shows the change in the economic dynamics of these municipalities is the number of local units and total employees hired (Table 5), especially in São Miguel do Gostoso, whose percentage change in the number of local units and total employees hired was significant at 187.2% and 53.2%, respectively. Parazinho and Bodó stand out due to the percentage change in the number of local units, but not in the total employees hired, especially Bodó, which had a negative percentage change in total employees hired. This has tended to change as wind farms are implemented, because they demand temporary employees from the local work force. Table 5 - Rio Grande do Norte - Selected Municipalities - number of local units and total employees hired - 2008 and 2012 Municipalities of RN

Number of local units

Total employees hired

2008

2012

Var. (%) 2012/2008

2008

2012

Var. (%) 2012/2008

João Câmara

404

522

29,2

2.253

2.782

23,5

Parazinho Pedra Grande São Miguel do Gostoso Jandaíra Bodó Ceará-Mirim

35 31

56 42

60,0 35,5

295 312

316 356

7,1 14,1

47

135

187,2

541

829

53,2

51 12 630

52 28 726

2,0 133,3 15,2

627 281 6.346

620 277 5.954

-1,1 -1,4 -6,2

Source: Developed by author from IBGE (2014). Therefore, the statistics on the total number of persons hired in Rio Grande do Norte for 2013 and 2014 shows a positive trend in the municipalities of Bodó, Jandaira and Ceara-Mirim, and a greater percentage change in Joao Camara, Parazinho and São Miguel do Gostoso, depending on the number of wind farms under construction and awarded as of those years. At 12/17/2014, Joao Camara and Parazinho already held first and second place, respectively, in terms of wind projects awarded and under construction. A sixth benefit relates to the technical and higher education that the wind sector fosters, as it has a positive impact on the demand for qualified personnel in the industry. Although this factor is important for the sector, it can also pose an obstacle due to the lack of qualified personnel to work in the industry. Therefore, “The academia and training institutes need to adapt to ensure the quantity and quality of education in this new area that is still largely unexplored in Brazil.” (TRIBUNA DO NORTE, 05/25/2014) The report states that in municipalities where the wind sector is booming, as a result of the shortage of skilled labor for both wind farm assembly and equipment manufacture, higher wages are paid for certified professionals. This has caused a rise in the costs for the sector, attracted foreign professionals and increased employee turnover in the companies. This is an important point because in the poorest, most

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remote municipalities, it ultimately generates a socioeconomic dynamic with employment opportunities and wages, which would be the seventh benefit of the wind business. This ultimately attracts people from other municipalities and states of the region and the country, or from other countries. Thus, the development of the wind sector brings qualitative and quantitative changes to these areas, as it offers people the opportunity to improve their living conditions when this activity is established in an area. People are beginning to invest in technical and higher education in order to get better jobs or positions and higher wages in the industry, which would otherwise not be possible in an environment where more than half of the population is considered poor, as in the case of Parazinho and Joao Camara. This is why the National Development Bank (BNDES) has been implementing social projects in some of the towns of Rio Grande do Norte, which is the eighth benefit to be mentioned. That bank is coordinating and implementing social projects for the firms Atlantic, Contour, CPFL, Desa, and Energisa, which are building 24 wind farms in Rio Grande do Norte. The idea is to support the social investments of these companies near wind farms in the municipalities of Joao, Camara and Parazinho through a line of Corporate Social Investments (CSI). This is an important initiative because it can contribute to raising the socio-economic indicators of these municipalities, thereby enhancing the living conditions of the communities in the areas where wind farms are being installed. Accordingly, in order to optimize social investments and make possible larger, more beneficial projects for local inhabitants, entrepreneurs have decided to join forces and establish two joint projects, the first in Joao Camara in the area of health and sanitation, and the other in Parazinho in the field of education. Each of these projects is expected to be for of up to R$ 1 million. From a more general interest viewpoint, developing the wind business in keeping with the need to incentivize research, sectoral innovation and entrepreneurship, along with progress in the number of wind farms in commercial operation, is sparking interest in building an energy technology park in the metropolitan region of Natal, which would bring companies, universities, research institutions, and government agencies together in one place. This is mentioned as a potential new benefit that could come from the wind business. According to a report released on 06/06/2014 by the Secretariat of Economic Development (SEDEC from the Portuguese) of Rio Grande do Norte, this project requires resources in the order of R$ 42 million, secured from the Sustainable RN Program. The park has been conceived to extend access to energy companies and science & technology educational and research institutions to help innovate and develop new technological capabilities, in addition to adapting existing technologies to the wind conditions of each location. CHALLENGES TO WIND GENERATION IN RIO GRANDE DO NORTE Although Rio Grande do Norte is one of the beneficiaries of the wind business in the Northeast region, that state faces a number of challenges, which have been presented by entrepreneurs and Elbia Melo lists (in an interview in January 2015): Poor roads (narrow and potholed); increased risk of accidents due to communities settling along the roads; a potential for serious injury due to horses, donkeys and dogs on the roadsides; drastic speed limit reductions, justified by the communities next to the roads; political intervention by colonels and ranchers in environmental licensing procedures; increased costs to entrepreneurs for final disposal of solid waste from plants because

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legal disposal sites are far away; and difficulties complying with the preservation of Legal Reserve sites (mandatory), as some communities do not respect this obligation. We found that wind businesses are centered in the Northeast region, particularly in the states of Pernambuco, Bahia, Ceara, and Rio Grande do Norte. The first three have entire production chains installed for wind equipment, consisting of turbines, blades and towers, and this has lead to a heavy concentration of production in those areas. Rio Grande do Norte differs in this respect because it only has two tower factories installed. As stated above, we found that there is no lack of wind energy potential, but rather of local political will to use this natural resource (wind) to create economic development opportunities for the state of Rio Grande do Norte. In the words of Milton Pinto, the Wind Sector Manager for CERNE, “Here we have more than enough wind but are lacking in government incentives. We run the risk to lagging behind.” (TRIBUNA DO NORTE, 12/07/2014). A study entitled MaisRN, prepared by the FIERN with support from the SEDEC, stated that despite their prestige, state political leaders are not fostering a favorable business environment, “which hampers confidence and security in project continuity and in the collaboration and commitment of institutional leadership.” (FIERN, Feb. 2014, p. 28). The lack of an attractive environment to concentrate production in a given sector has a negative impact on industries such as wind energy, which need specific policies to incentivize development in the areas where it is established. That is what has been done in other states of the Northeast region, such as Bahia, Ceara and Pernambuco. In this regard, the MaisRN study interviewed businesspeople and leaders of opinion and found that the policies of the Ceara and Pernambuco have attracted investments and disrupted the economy of Rio Grande do Norte and Paraiba simply because they provide financial incentives, not merely tax incentives, help with environmental licenses, and improve their roads, which make their governments partners with the entrepreneurs. The interviewees called to mind that “granting tax incentives is no longer a differentiating measure, because all the Northeastern states offer them as a way to attract investments” (FIERN, Feb. 2014, p. 29). It is clear that failing to establish the conditions to facilitate investments poses a significant obstacle that keeps states from benefiting from investment opportunities, and that this is the situation in Rio Grande do Norte. As Elbia Melo says, “the state has a very important role in attracting new businesses.” (TRIBUNA DO NORTE, 12/07/2014) In addition to this matter, there are equally important issues that hinder the dynamic action of entrepreneurs in Rio Grande do Norte. According to a special survey by FIERN in September 2012 on the bureaucracy and industry of Rio Grande do Norte, the main stumbling stone has to do with the excessive number of legal obligations that must be met, which raises costs and diverts resources to unproductive ends. The areas most hampered by the state bureaucracy are environmental and labor issues. This assessment was confirmed in February 2014 by the MaisRN study, which indicated the following negative points in the state’s investment climate: infrastructure and logistical problems; restrictions in the state tax incentive system; uncertainty and legal insecurity; slow bureaucratic procedures; and ideological motivations among regulatory bodies, particularly in the environmental area. In general, Brazil still has microeconomic and regulatory disadvantages, as seen in relation to tax burdens, labor benefits and legal uncertainty.

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Similarly, Alessandro Gregori, the New Business director in CPFL, stresses that the government of that state “needs to prepare its local infrastructure to attract business [...], contribute to professional certification, and adjust its inspection and licensing agencies to the size of the wind industry’s demand.” (TRIBUNA DO NORTE, 12/07/2014) With regard to the issuance of environmental permits, the MaisRN study interviewees stated that this creates an unfavorable environment for investment because regulators are very slow and bureaucratic, which ultimately delays assessments and licenses for projects in almost all industries. In a report published in the Tribuna do Norte on 12/07/2014, State Secretary of Economic Development Sílvio Torquato stated that it is not necessary to increase the staff of the Institute for Sustainable Development and the Environment (IDEMA from the Portuguese), but rather to create municipal departments for the environment, as done in São Gonçalo do Amarante when the “Governador Aluizio Alves” international airport was built in Natal. He even argued, “It is not necessary to create a new secretariat for this or that matter. We have an Energy Coordinator in the SEDEC who can easily meet the demand.” (TRIBUNA DO NORTE, 12/7/2014) Another major obstacle in Rio Grande do Norte has to do with transmission infrastructure. Since 2012, several wind farms that were approved to start commercial operations had to change their calendars simply because the transmission lines, substations and collection stations had not been completed. Although this problem was partially solved in 2014, there are still a number of transmission lines and substations that need to be implemented in order for Rio Grande do Norte to regain its former position at the energy auctions.6 Furthermore, Rio Grande do Norte faces hurdles in the road transport of wind power equipment to where wind parks are being implemented, and the state lacks a seaport such as the ones in Pecém, Camaçari and Suape, as stated by Elbia Melo in an article published in the Tribuna do Norte on 12/07/2014. It follows, therefore, that the loss of competitiveness in the wind industry of RN is also due to its lack of port infrastructure, in addition to the other factors listed under this topic. Suffice it to say that the states of the region that offer this infrastructure (Ceara, Bahia and Pernambuco) are attracting more investments. The port infrastructure of Rio Grande do Norte consists of three seaports – the Port Terminal of Natal, the Isla de Arena Branca port, and the Guamare port –, which do not support large vessels or have cabotage lines to move large containers. Therefore, it lacks the infrastructure needed to transport wind equipment such as blades and towers, and lacks the metalworking industrial scale needed to attract manufacturers and take advantage of the economic impact that this sector could have by diversifying production. In this regard, State Secretary of Economic Development Silvio Torquato said he could present the state government with a project detailing the location and economic feasibility of a new port in Rio Grande do Norte, which should be built Porto do Mangue in the salt flat region. This is part of an old debate regarding the need for this project. The Secretary stressed, “Several studies have already been conducted to determine the ideal location and its economic and financial viability. However, no conclusion has been forthcoming with regard to the financial and management model.” (TRIBUNA DO NORTE, 12/7/2014) 6 The expectation is that the investments indicated in the PAC2 and PDEE for 2022 will be completed within the planned term and that the EPE, through the Transmission Expansion Program - Cycle 2014 - 1st and 2nd semesters for the 20142019 period, is plan investments of R$ 5.0 billion along 4,388 km of new power lines for the entire Northeast region, in addition to 13 substations.

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CONCLUSION This study sought to explain the connection between the wind sector of Rio Grande do Norte and the business opportunities created by this industry. Any study aiming to include wind power in the national energy mix should include not only the use of wind energy in times of gradual depletion of water resources, but also and primarily the potential for this sector to create socioeconomic development options where wind farms are installed. Herein lies the importance of that sector, both generally to supplement existing hydroelectric generation, and more specifically to drive the productive chain in regions that have winds of suitable quality for electricity generation. Being a new topic in the country, wind energy production was not easy to discuss given the lack of data, especially because this study centered on Rio Grande do Norte.Therefore, there is a perceived need to deepen this discussion and raise awareness of this sector’s importance and its potential to create economic development opportunities for regions such as Brazil’s Northeast. It is clear that having wind is not enough. The production chain must be fostered through investments in transmission/logistical infrastructure and financial incentives, as is done in leading countries in terms of installed capacity in MW, which also develop entire R&D structures support more efficient wind energy generation, considering the intermittent nature of this source. At energy auctions, Rio Grande do Norte has been awarded wind power contracts for 3,402 MW or 32.4% of the total for the Northeast region, ranking first among the Northeast states with the most contracts in MW at auctions that included wind energy. Clearly, one of the impacts of this significant growth in installed wind power capacity is increased human pressure on the environment in areas where wind farms are implemented. Parazinho and Joao Camara are important cases because they concentrate the largest number of wind farms––both approved and under construction––in the state of Rio Grande do Norte. This is generating very different socio-economic dynamics from the prevailing ones based on trade and agriculture, and is the basis for their future income and public administration. Starting with the 13th LEN of 2011, this state started losing auction contracts to other states of the region with significant wind power potential because they had better infrastructure to distribute the power they generated. In 2013, this loss of competitiveness was accentuated by the requirement that only wind projects having transmission lines could participate in the auctions. Accordingly, it became evident that the main obstacle to developing the wind business in this state was its lack of infrastructure in terms of transmission lines, logistics for land transport and seaports, or environmental industries. In response, it needs to find ways to overcome these obstacles. This requires incentives and institutional arrangements by local political powers to attract the needed investments, which will involve the engagement of the entire wind industry, not only in terms of infrastructure, but also in relation to the ability to attract equipment manufacturers to areas where wind farms are being installed. Finally, promoting wind energy in this state is vital to the municipalities that receive payments related to this business, because in addition to reconfiguring physical spaces, it encourages people to seek certification and better jobs, whether within the production chain or in areas relating to the R&D sector.

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REFERENCES ABEEólica. Interview with Elbia Melo. January 2015. AMARANTE, O. A. C.; ZACK, J.; BROWER, M.; LEITE DE SÁ, A. Atlas do potencial eólico brasileiro. Brasilia, 2001. ANEEL. Banco de Informações de Geração (BIG). Capacidade de Geração no Brasil. Available at http:// www.aneel.gov.br/. Accessed on 06/03/2014. CCEE. Leilões. Available at http://www.ccee.org.br/. Accessed on May 2014. _____. PROINFA – Tratamento da energia do PROINFA na CCEE. Nº 94 – December 2013. Available at www. ccee.org.br. Accessed on 05/10/2014. COSERN. Potencial Eólico do Estado do Rio Grande do Norte. COSERN. 2003. ELETROBRAS. PROINFA. Dados de geração das CGEE participantes do PROINFA – 2006-2013. Available at http://www.eletrobras.com/. Accessed on March 2014. _____. PROINFA. Dados de geração das CGEE participantes do PROINFA – 2014. Available at http://www. eletrobras.com/. Accessed on March 2014. EPE. Plano Nacional de Energia 2030. Rio de Janeiro: EPE, 2007. _____. Programa de Expansão da Transmissão – ciclo 2014 – 1º semestre. Rio de Janeiro: EPE, March 17, 2014. Available at www.epe.gov.br. Accessed on 01/10/2015. _____. Programa de Expansão da Transmissão – ciclo 2014 – 2º semestre. Rio de Janeiro: EPE, August 21, 2014. Available at www.epe.gov.br. Accessed on 01/10/2015. FIERN. MaisRN – Diagnóstico e cenários de Desenvolvimento Econômico para o Rio Grande do Norte – 2015-2035. February 2014. Available at www.maisrn.org.br. Accessed on 01/10/2015. _____. Estratégia de Desenvolvimento Econômico e Promoção de Investimentos do Rio Grande do Norte – 2015-2035. July 2014. Available at www.maisrn.org.br. Accessed on 01/10/2015. GWEC. Global Wind Report: Annual Market Update, 2014. Available at http://www.gwec.net/. Accessed on March 2015. ________. Global Wind 2006 Report. 2006. Available at http://www.gwec.net/. Accessed on Feb. 2014. IEDI. Políticas para a promoção de energia verde. March 2011. Available at http://retaguarda.iedi.org.br/ midias/artigos/4d9e2a0557e47f98.pdf. Accessed on 11/10/2012. IBGE. Cidades@. Available at http://www.cidades.ibge.gov.br/xtras/temas.php?lang=&codmun=240880 &idtema=16&search=rio-grande-do-norte|parazinho|sintese-das-informacoes-. Accessed on Dec. 2014.

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_____. Contas Regionais do Brasil. Available at http://www.ibge.gov.br/home/estatistica/economia/ contasregionais/2012/default.shtm. Accessed on Nov. 2014. INCRA. Instrução Normativa 76, de 23 de Agosto de 2013. Available at http://www.incra.gov.br/media/ imprensa/IN_76_P.pdf. Accessed on 01/16/2015. MATHIAS BECKER. Entrevista - Bahia detém 15% do potencial eólico brasileiro. In: Conjuntura & Planejamento, No. 181, pp. 18-21, Oct. 10, 2013. MME/EPE. Plano Decenal de Expansão de Energia 2019. Brasilia: MME/EPE, 2010. _____. Plano Decenal de Expansão 2020. Brasilia: MME/EPE, 2011. _____. Plano Decenal de Expansão 2021. Brasilia: MME/EPE, 2012. _____. Plano Decenal de Expansão 2022. Brasilia: MME/EPE, 2013. _____. Plano Decenal de Expansão de Energia Elétrica 2006-2015. Brasilia: MME/EPE, 2006. _____. Plano Decenal de Expansão 2008-2017. Rio de Janeiro: EPE, 2009. MME. Portaria 132, de 25 de Abril de 2013. Published in the DOU of 04/26/2013. Available at http:// www.epe.gov.br/leiloes/Documents/Leil%C3%B5es%202013/Portaria%20MME%20n%C2%BA%2013213.pdf. Accessed on Dec. 2014. O ESTADO DE SÃO PAULO. Nordeste é a nova fronteira elétrica. 01/11/2015. Available at http://economia. estadao.com.br. Accessed on 01/18/2015. PINTO, M. Fundamentos de energia eólica. Rio de Janeiro: LTC, 2013. REVISTA BRASIL ENERGIA. Agora é essencial. Year 33. Nº 400. March 2014. SEDEC. Energia Renovável. 06/06/2014. Available at http://www.sedec.rn.gov.br/Conteudo. asp?TRAN=ITEM&TARG=15443&ACT=&PAGE=0&PARM=&LBL=Energia. Accessed on 01/01/2015. TRIBUNA DO NORTE. Nova indústria, novas oportunidades: Para quem? 05/25/2014. Available at http:// www.tribunadonorte.com.br/eolica/. Accessed on 01/08/2015. _____. Há novos estados no mapa e o RN pode ficar para trás. 06/24/2013. Available at http://www. portalabeeolica.org.br/index.php/noticias/721-%C2%91h%C3%A1-novos-estados-no-mapa-eo-rn-podeficar-para-tr%C3%A1s%C2%92.html. Accessed on 12/23/2014. _____. Governador eleito avaliará projeto. 12/07/2014. Available at http://tribunadonorte.com.br/noticia/ governador-eleito-avaliara-projeto/300274. Accessed on 01/01/2015

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CHILE ESTIMATOR OF AVAILABLE ENERGY FOR THE BATTERY BANK OF A MICROGRID BASED ON RENEWABLE ENERGY SOURCES CLAUDIO DANILO BURGOS MELLADO Advisors: Doris Sรกez Hueichapan Marcos Orchard Concha Roberto Cรกrdenas Dobson

ABSTRACT The use of non-conventional energy sources (such as solar and wind based power sources) in electrical applications requires the use of storage systems. Often, storage systems are based on battery banks in charge of supplying or storing the demanded energy in cases of (non-conventional) power deficit or surplus respectively. The operating mode of the battery bank is determined by a battery energy management system. This system defines the charge and discharge cycles of the battery bank based on the state-of-charge (SOC) of the batteries. Since SOC cannot be directly measured, the estimation of this quantity is mandatory. In this study, a fuzzy-logic-based SOC estimator is proposed. Such estimator can be implemented in real-time applications such as micro-grids, electric vehicles and solar cars. For designing the proposed estimator, information obtaining through laboratory tests and taken from real operational conditions of a battery bank is combined. Indeed, information of real operation of the battery bank installed in the micro-grid of Huatacondo (north of Chile) is considered for tuning and assessing the estimator performance. Since the estimator is tuned using both real operation and laboratory information its implementation does not require interrupting the energy service and its use can be extended to other battery banks with the same batteries. In addition, the resulting algorithm for the implementation of the estimator is as simple as possible. Then, the estimator can be integrated into an embedded system providing the possibility of having a potential commercial product. KEYWORDS: State of Charge (SOC), battery bank, microgrids, fuzzy logic

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INTRODUCTION Non-conventional renewable energy (NCRE) sources are gaining daily in global importance. Since the 1990s, there has been a marked increase of NCRE sources in energy markets as a result of incentives for this type of energy in several countries and a growing global trend to seek environmentally friendly energy sources [1]. In this context, the Department of Electrical Engineering of the Universidad de Chile [2] has centered its efforts on technological development to harness this country’s large solar and wind energy potential. Accordingly, Chile’s first intelligent microgrid [3] was installed in northern Chile, in the town of Huatacondo (Figure 1a), with active community engagement towards efficient energy use. This microgrid supplies towns with electricity 365 days a year, of which they were deprived prior to its installation. A microgrid can be defined simply as a set of power-generator units that are interconnected via distribution grids near consumption centers (Figure 1b), which can operate in a controlled, coordinated fashion whether the microgrid is connected to the main grid or isolated from it [4] [5]. Isolated microgrids are a good solution to electrify for small towns far from major urban areas, offer a highly efficient, replicable solution for diverse energy distribution points, and make power systems more robust and stable [6]. One example of this is the isolated microgrid in the town of Huatacondo (see Figure 1b), comprising the following generation units [3]: • The main 23kW photovoltaic plant • A small 1.8kW photovoltaic plant • A set of 3kW wind turbines • A 90kW diesel group • A water storage system • An energy storage system, comprising 120kWh of lead-acid batteries

a)

Solar

b)

Wind

Water Tower

Solar

Diesel Battery bank

Control and monitoring system

Demand management system

Figure 1. a) Town of Huatacondo in Tarapaca, Chile, b) Layout of the Huatacondo microgrid

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Eco_Lógicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.


The electricity from each generating unit of the microgrid is dispatched through an EMS (Energy Management System) that executes an optimization algorithm to control each unit’s power production, thereby minimizing operating costs and ensuring that the system meets power demands [7]. As shown in Figure 2, input variables to the EMS include estimates of available energy in the battery bank (also called ‘State of Charge’ or SOC), weather forecasts, solar and wind power predictions, and of the town’s anticipated energy and water consumption. EMS output variables include power produced by the photovoltaic plant and diesel generators, unsupplied power, power to the inverter connected to the battery bank, a water-pump on/off indicator, and a demand management indicator for the town [8]. XXX - +

Historical data Predicciones Weather climáticas forecasts

SOC Estimate

ESOC

XXX

Photovoltaic PXXX generator PXXX model Wind generator model

PE

Electricity demand forecast

PL

PS PD PUS EMS

Water WC consumption forecast

Measurements of the micro-grid

Unit Commitment + Economic Dispatch

PI

X

- +

Bp Sl

Figure 2. Diagram of the Huatacondo EMS As Figure 2 shows, one EMS input variable is an estimate of available energy in the battery bank, called the ‘State of Charge’ (SOC). It is usually expressed as a percentage, with 100% indicating that the battery bank is fully charged and 0% that it no longer has energy to supply the town. Effective real-time estimations of this indicator will enable the EMS to achieve efficient coordination of the energy produced by the various generation sources. For example, when there is excess energy and the SOC is less than 100%, the EMS will store the surplus energy in the battery bank. Otherwise, the surplus can be used by the water supply system. Conversely, if the SOC is estimated incorrectly, this could lead to inappropriate EMS decisions regarding generation or the set points for the units making up the microgrid, which could damage the battery bank and interrupt power supplies to the town. For example, there may be cases where the real SOC is 0%, but the estimator erroneously informs the EMS that it is 50%. In this case, if there is also an energy deficit in the microgrid, it may cause the EMS to choose to continue extracting energy from batteries, which would damage them and cause a power outage in the town. This shows that in the context of microgrids and for any other systems non-conventional renewable energy systems that use battery banks, knowing the state of charge is essential for effective energy management of the overall system.

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In light of the above, this study addresses the issue of estimating the available energy (SOC) in battery banks. This is a complex issue, because the SOC cannot be measured directly from batteries, but rather must be estimated from other measurable battery variables such as voltage, current and temperature [9] [10]. Therefore, this study has designed and validated a real-time state-of-charge estimator for battery banks, based on fuzzy modeling and the Extended Kalman Filter. Fuzzy modeling made it possible to characterize batteries in the laboratory and then extrapolate this data to model the Huatacondo microgrid battery bank. The Extended Kalman Filter technique thus established was used to estimate the SOC based on the battery model. This filter was chosen because the formulation takes into account the effects of noise in the measurements, and is immune to erroneous initialization of the start state. In other words, when the estimator is implemented in a system for which the SOC is unknown when executing the estimation algorithm, it will converge towards the actual state after a few iterations. This paper is organized as follows: Section 2 presents the state of the art regarding SOC estimation methods; Section 3 presents the proposed estimation method and the experimental system built to test the batteries under study; Section 4 details the findings of the experiment; and, finally, Section 5 presents the main conclusions of this study.

THE STATE OF THE ART: METHODS FOR ESTIMATING AVAILABLE POWER (SOC) FOR BATTERY BANKS This section presents the different methods found in the literature to determine the state of charge for battery banks. It will present the main pros and cons of each method and their main reference works. THE OPEN-CIRCUIT VOLTAGE METHOD An important concept related to batteries is the Open-Circuit Voltage (VOC), defined as the voltage at the battery terminals with no load connected following a rest period (usually 1 hour [11] [12]). This curve can be seen as one of the features of a battery.1 To obtain this curve, an experimental test called ‘voltage relaxation’ is usually required. This entails taking a given load from the battery, then stopping the discharge and waiting for a suitable rest period to ensure that the voltage measured at the terminals is the real VOC [13] [14]. This algorithm is run for different points of the SOC-VOC curve to map it. Once the SOC-VOC curve is obtained, the state of charge can be determined by measuring the open-circuit voltage and assessing the figure obtained in the SOC-VOC curve. Despite its simplicity, this method can only be applied in situations where batteries are subjected to long periods of rest, or their normal operation must be interrupted [15] [16]. Other disadvantages relate to the open-circuit voltage being temperature dependent [23], the history of the battery, and the hysteresis effects [17], which affect SOC-VOC curve mapping and, obviously, the performance of the method. The features of this method are summarized in Table 1.

1

62

Under the relatively constant temperature assumption

Eco_Lógicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.


Table 1. Features of the open-circuit voltage method for determining the SOC Application

All types of batteries

Operating principle Based on the existence of a relationship between the VOC and the SOC Application

Systems in which accumulators have long rest periods

Pros

Easy to implement and, when combined with other techniques, allows continuous SOC estimation

Cons

Cannot be applied alone in real time. Affected by temperature, hysteresis and the accumulator history.

THE INTEGRAL ELECTRIC CURRENT METHOD This method consists of using sensors to record the current flowing in or out of the batteries and integrating it to estimate the capacity that is extracted. Once this is done, the SOC is determined using Equation 1. t

1 SOC = SOC0 — Idt Equation 1 Cn

t0

Where Cn is the accumulator’s nominal capacity and I is the current flowing in or out of it. A Coulomb efficiency factor (ŋi ) is usually added to the expression given by Equation 1, which turns it into the expression given by: t

1 Equation 2 ŋi Idt SOC = SOC0 — Cn t0 Where:

Equation 3 1 para la carga ŋi = para la descarga ŋi <1 The disadvantage of this method is that it is highly sensitive to current measurement errors [15], and requires periodic recalibration [18]. Table 2 summarizes the features of this method.

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Table 2. Features of the integral electric current method for determining the SOC Application

All types of batteries

Operating principle

Based on taking a balance of the current flowing in and out of the battery

Application

Systems in which batteries are always in operation

Pros

It can be applied online and is easy to implement.

Cons

It is very sensitive to current measurement errors and can produce estimation errors of over 50% in applications where measurements are noisy.

Some of the studies that use this estimation method are available in references [19] and [20]. THE IMPEDANCE SPECTROSCOPY METHOD This method is based on the fact that battery impedance provides data on battery status [18] [21], such as SOC, temperature, life cycles, and others. The impedance spectrum is determined using a technique called Electrochemical Impedance Spectroscopy (EIS). The disadvantage of this method is that impedance curves depend heavily on temperature and are only usable in constant temperature applications. Furthermore, implementing the EIS technique requires having enough time to complete the frequency scan [22]. For these reasons, this method is rarely implemented to determine the SOC, and its use for this purpose is still debated [21]. Table 3 summarizes the features of this method. Table 3. Features of the spectroscopic impedance method for determining the SOC Application

Its application to determine the SOC is still being questioned

Operating principle

Based on modeling the frequency response of the accumulator

Cons

The EIS is highly dependent on temperatures

Some of the studies that have used this estimation method are available in references [23] and [24]. THE INTERNAL RESISTANCE METHOD Every battery has an internal resistance, which composed of two parts. The first is ohmic, caused by the inherent resistance of the electrodes, electrolytes, separator, and contacts [16] [21]. The second is polarization resistance, which depends on the electrolyte concentration [21]. Specifically, ohmic resistance relates to certain accumulator features such as state of charge, health status, life cycles, and others [18] [21]. Formulating this method to determine the SOC is based on a relationship between internal resistance and state of charge. Table 4 shows the features of this method.

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Table 4. Features of the internal resistance method for determining the SOC Since resistance variations are in the order of milliohms, it is rarely used to determine the SOC

Application

Operating principle Measuring the internal resistance of the battery This method is the most appropriate to determine the health status of batteries

Application

The SOC estimation methods discussed so far can be seen as traditional, but there are other so-called emerging methods, which include estimating the SOC using the Extended Kalman Filter algorithm, the particle filter algorithm, neural networks, and fuzzy logic. These estimation techniques are detailed below. THE KALMAN FILTER (KF) This filter, developed in 1960 by Rudolf Kalman, determines the non-measurable state of a linear system based on actual input/output measurements and a state variable model that represents the system dynamics, considering that there are Gaussian disturbances in these states and measurements [25]. This filter has been widely used to determine the state of charge in energy accumulators. Implementing it requires an accumulator model that contains the SOC as a state, and the model must be represented in state variables [17]. One advantage of the KF is that the estimator is unbiased and optimal [25], and can be applied in real time with any battery technology. Its main disadvantages lie in how it is implemented, since the parameters relating to noise in states and measurements are set gradually and empirically. Furthermore, if the model used is very complex, it can incur high computational costs, which makes the algorithm slow from a computational viewpoint. Table 5 shows a summary of this method [25] [17]. Table 5. Features of the Kalman Filter method for determining the SOC Application

All types of accumulators

Operating principle

Each Kalman Filter equation is applied to one battery model

Application

Applications in real-time, linear and Gaussian systems. Implementing it with non-linear systems requires linearization at each point of time

Pros

The estimations found are optimal

Cons

It assumes Gaussian noise in measurements and states, and its computational cost depends largely on the system model on which the algorithm is based.

Some of the studies that have used this estimation method are available in references [4], [26], [27], and [28].

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PARTICULATE FILTERS (PF) The Particulate Filter (PF) method is used to estimate the status of a system that varies over time. Proposed in 1993 by N. Gordon, D. Salmond and A. Smith, it works with status probability densities, which it approximates using a set of particles that have a given weight [10]. Similar to the KF, implementing the PF requires an accumulator model that considers the state of charge as a state variable. Its advantage over the Kalman Filter is that it makes no assumptions regarding the disturbance distribution, and can be applied in systems that are not distributed in a Gaussian way. The disadvantage of this filter is that the number of particles must be chosen empirically and grows with the size of the system. Another drawback of this algorithm is that it has a ‘degeneration’ issue in that all relevant problem particles tend towards zero except for one. This obviously reduces the diversity of the solution. Although the degeneration issue can be solved by re-sampling, the PF algorithm may be affected by the re-sampling method used. Table 6 shows a summary of this method. Table 6. Features of the Particle Filter method for determining the SOC Application

All types of batteries

Operating principle

Particulate Filter equations are applied to the battery model

Application

Real-time applications

Pros

Can be implemented with nonlinear and non-Gaussian systems

Cons

Issues with algorithm degeneration and choosing the number of particles

Some of the studies that have used this estimation method are available in references [10] and [29]. NEURAL NETWORKS (NN) With this method, accumulators can be modeled using a neural network in which neural inputs may be voltages, currents and temperatures, and the output may be voltage at terminals or state of charge. Through a learning process, the weights of all neurons in the network are then determined in order to calibrate the model for application to new cases [17]. Neural networks are able to learn, adapt to change and store data, can be used in real time, and allow for system modeling without requiring a complete understanding of their behavior. One major disadvantage of this method is the need for large amounts of experimental data on the system under study in order to train the network. This method can be used in two ways: to determine the SOC directly from the neural network, and in combination with either the Kalman Filter or the Particulate Filter. Table 7 shows a summary of this method.

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Table 7. Features of the neural network method for determining the SOC Application

All types of batteries

Operating principle

Black box type

Application

Both dynamic and static battery applications

Pros

Implementing it does not require a detailed understanding of the system

Cons

The network architecture is empirically determined. Large amounts of data are needed to adjust neural network weights.

Some of the studies that have used this estimation method are available in references [30] and [10]. FUZZY LOGIC Fuzzy logic-based modeling is a powerful tool to address nonlinear problems based on a knowledge of experimental data [31] [32] and has been combined with the EIS to determine the SOC. Its main advantages are that implementing it does not require a detailed understanding of the system, and that it can be used with nonlinear systems. Perhaps the main disadvantage is that large amounts of data are needed to determine the fuzzy sets. This method can be combined with neural networks, which has given rise to hybrid systems called neuro-fuzzy modeling. To estimate the SOC, fuzzy modeling can be used directly or in combination with the Kalman Filter or Particulate Filter, in which case the fuzzy model is used as an observation equation. Table 8 shows a summary of this method. Table 8. Features of the fuzzy logic method for determining the SOC Application

All types of batteries

Operating principle

Black box type

Application

Both dynamic and static battery applications

Pros

Implementing it does not require a detailed understanding of the system

Cons

In some cases, large amounts of data are needed to determine the fuzzy rules

Some of the studies that have used this estimation method are available in references [33], [34], [35], and [36].

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THE PROPOSED SOC ESTIMATION METHOD In view of the literature review presented in the previous section, we proposed developing a SOC estimator based on the Kalman Filter and fuzzy modeling techniques. Since battery behavior is nonlinear [37], fuzzy modeling was used to create a fuzzy-circuit model for the battery bank, which has the distinction of representing its nonlinearities. An additional requirement imposed was that the resulting model should be simple in order to combine it with the Kalman Filter technique. The KF technique makes it possible to develop a real-time estimator that can overcome erroneous algorithm initializations. To accomplish the above proposal, the following milestones were set for this study: (i) designing an experimental system based on power electronics for testing a pilot battery bank; (ii) based on the data generated in the above point, formulating and validating a test model of a fuzzy-circuit battery bank; (iii) in parallel with the above, gathering normal operating data on the Huatacondo microgrid battery bank; and, finally, (iv) developing a methodology to apply the battery model developed under point (ii) to the Huatacondo battery bank without interrupting its normal operations. This must be done because the fuzzycircuit model is based on experimental tests on a pilot battery bank, different from the microgrid battery bank, which requires adapting the model to represent the behavior of the microgrid battery bank correctly. The flowchart in Figure 3 depicts the methodology proposed herein, as described under point (iv) showing two parallel procedures that are then combined to design the final estimator. The first comprises three specific tests on a pilot battery bank, one to determine the SOC-VOC curve, another to determine the internal resistance curve, and a third to identify the parameters of the model developed for that bank. The second method creates a training set and a validation set with the normal operating data from the microgrid battery bank. Both procedures intersect when the pilot battery bank model is adapted to the microgrid battery bank. Test battery bank Experimental tests carried out in a laboratory on a test battery bank

Internal resistance curves in test battery bank

SOC versus VOC curve

Normal operation information of the battery bank of the Huatacondo micro-grid

Training set

Validation set

Battery test bench model Identification of model parameters

Final model for test battery bank

Adaptation of the test battery bank model to the micro mains battery bank Implementation of the SOC estimator based on the Kalman battery model and filter

Figure 3. Flowchart of the proposed estimation method 68

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The model has to be adapted because the two battery banks have different configurations, and the SOCVOC and internal resistance curves are affected by the number of charge/discharge cycles, the use profile, temperatures, and other factors. Therefore, it would be erroneous to think that the microgrid batteries have the same SOC-VOC and internal resistance curves as the pilot battery bank. Therefore, the normal operating data from the microgrid battery bank is used to take into account all of these factors. Below we detail each of the four points discussed above to develop an available energy estimate for the Huatacondo microgrid battery bank. THE EXPERIMENTAL SYSTEM The experimental system shown in Figure 4 was designed and built to generate experimental data for a pilot battery bank consisting of lead-acid batteries, in order to develop and validate the fuzzy-circuit model proposed herein. The parameters of the experimental system are detailed in Table 9. As shown in Figure 4a, when the V2 voltage induced by the semiconductors2 is greater than the battery bank voltage (Vb), power flows from the DC link to the batteries, indicating that the experimental system is charging the batteries. Conversely, if V2 is less than the battery voltage (Vb), power flows from the battery to the DC link, in which case the ‘battery discharge’ branch is activated (see Figure 4a) to dissipate energy from the batteries through the resistor R. In this case, the experimental system discharges the batteries. Figure 4b illustrates both the experimental system and the pilot battery bank, and Figure 4c details the DC/DC converter. In Figure 4a, the ‘battery charge/discharge’ circuit uses a so-called ‘interleaved’ setting [38] to double the ripple frequency, which considerably reduces conduction and ripple losses in the current to and/or from the batteries. For further information on the experimental system, see references [17] and [39]. Rectifier bridge

DC-link

Battery discharge

Battery Charge/Discharge

AC Source & Variac

Battery Bank L2 C

L1 V1

L2 V2

R

Vb

(a) Schematic drawing of the experimental system Resistive bank

Variac

Protection board

DC/DC Converter

PC control

DC-link

Gate drivers

DSP+FPGA

Battery bank

Current sensors Isolation transformer

Inductance

Voltage sensor Fuses

(b) Experimental System Prototype

(c) DC/DC Converter Prototype

Figure 4. The experimental system for battery testing 2

IGBT (Insulated Gate Bipolar Transistors) were used

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Table 9. Features of the experimental system and pilot battery bank Rated power of the experimental system

4kW

Inductance for battery charge/discharge

30mH

Nominal voltage of battery bank

18V

Pilot battery bank

Three Trojan T-105 batteries connected in series

Switching frequency of the experimental system 4kHz Inductance and resistance to discharge batteries

15mH, 1Ί

Control platform

DSK 6713, FPGA ACTEL A3P400

DC link capacitance

Two 75V, 33.000uF capacitors connected in parallel

Semiconductor type used

1200V IGBT collector-emitter and 300A current collector, by TOSHIBA

EXPERIMENTAL TESTS Experimental tests can be classified into two groups, the first made up of specific experimental laboratory tests with the experimental system described in the previous section, and the second comprised of normal operating data on the Huatacondo microgrid battery bank. Note that all experimental tests consist of profiles of the current levels demanded of the batteries and their corresponding response in voltage. LABORATORY TESTS This is a set of three experimental tests on the pilot battery bank as shown in Figure 5. The first test determines the curve3 comparing the state of charge (SOC) to the open circuit voltage (VOC), the second gathers internal resistance data for the battery bank at different current and SOC levels, and the third identifies the parameters of the fuzzy-circuit model to characterize the dynamics of the pilot battery bank. The SOC-VOC Curve As mentioned in paragraph 2.1, the SOC-VOC curve is determined by the stress relaxation test conducted on the pilot battery bank shown in Figure 6. On this basis, we determined the open circuit voltage (VOC) points as a function of the SOC, giving us the SOC-VOC curve shown in Figure 7. The theoretical shape of this curve is shown in Equation 4. 3

70

The SOC-SOC curve

Eco_LĂłgicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.


Figure 5. The pilot bank composed of three Trojan T-105 lead-acid batteries connected in series

Experimental test “Relaxation voltage”

19.5

35

19

30

18.5

25

18

20

17.5

15

17

10

16.5

5

16

0

15.5 0

Current

2

4

6

8

10

Current [A]

Voltage [V]

VOC points

Voltage

12

-5 14

Time [hr]

Figure 6. The stress-relaxation test conducted on the pilot battery bank

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SOC vs. VOC curve

20

19.5

Voltage [V]

19

18.5

18

17.5

17

Tested points "Stress relaxation" Theoretical curve 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

SOC (dado por el método de la integral de la corriente)

Figure 7. The experimental and theoretical SOC-VOC curve

VOC (SOC) = 3.755 • SOC3 - 5.059 • SOC2 + 3.959 • SOC + 17.064 Equation 4

Internal Resistance Curves When conducting the experimental test to map the internal resistance of the pilot battery bank as a function of the discharge current level and the SOC, we obtained the results presented in Figure 8. Equations 5, 6, 7, and 8 show the theoretical expression of the nonlinear functions that represent the internal resistance points shown in Figure 8. Note that the four internal resistance curves shown in Figure 8 were determined for the 10A, 15A, 25A, and 32A levels of constant current. For example, curve R10 (see Figure 8) shows the batteries’ internal resistance variation as a function of the SOC and a constant 10A discharge current.

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0.12

R10 R15 R25 R32

0.11 0.1

Internal resistance [Ohms]

0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

SOC (given by the method of the current’s integral)

Figure 8. The internal resistance curves of the pilot battery bank as a function of current and SOC

Equation 5 R10 (SOC) = 0.0703 • SOC4 - 0.3821 • SOC3 + 0.6187 • SOC2 - 0.3825 • SOC + 0.1176 Equation 6 R15 (SOC) = 0.0665 • SOC4 - 0.3378 • SOC3 + 0.5287 • SOC2 - 0.3156 • SOC + 0.0947 Equation 7 R25 (SOC) = 0.0305 • SOC4 - 0.2187 • SOC3 + 0.391 • SOC2 - 0.2525 • SOC + 0.0794 Equation 8 R32 (SOC) = 0.083 • SOC4 - 0.2837 • SOC3 + 0.3742 • SOC2 - 0.2083 • SOC + 0.063

The Training Test Figure 9 shows the training test used to identify the parameters for the fuzzy-circuit model proposed herein.

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Training test

40

19

30

18

20

17

10

16

0

15 0

Current

5

10

Current [A]

Voltage [V]

20

Voltage

-10 15

Time [hr]

Figure 9. Training Test NORMAL OPERATING DATA FOR THE MICROGRID BATTERY BANKS This set is made up of normal operating data for the microgrid battery bank (see Figure 10). The measured magnitudes are current4 and voltage.5 This data was collected between 03/18/2011 and 03/26/2011, with a 10-second sampling period. We should note that the information had a few errors, such as data loss, data outside the operating range, repeated data, and data sampled at higher frequencies, which made it necessary to pre-process them. The resulting data consisted of 95099 points, which were divided into a training set (see Figure 11) to determine the model parameters and validation set (see Figure 12) with which both the battery model and the SOC estimator were validated. In this figure, note that the points given by the intersection of the vertical lines with the current and voltage graphs were points where the battery was 100% charged, so the estimator at these points should approach 100% if it estimated the state of charge properly. Normal operation of the microgrid battery bank can be divided into three stages (see Figure 11). During the early morning hours (00:00 - 06:00), the bank discharges due to random consumption in the town of Huatacondo. From sunrise and sunset (6:00 to 18:00), the batteries start to charge with the energy provided by the photovoltaic system (see Figure 11). In the evening (18:00 - 00:00), given the lack of solar energy, the diesel generator comes on line to provide energy for the town and the battery bank (see Figure 11), and the energy delivered to the battery bank at this stage is sufficient to charge it fully.

4

Considered an input variable

5

Considered an output variable

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Figure 10. The Huatacondo microgrid battery bank, consisting of Trojan 96 T-105 batteries connected in series

Training set

40

Current [A]

20

0 -20 -40

Discharge 0

Charge/Discharge Charge

10

20

10

20

30

40

50

60

30

40

50

60

700

Voltage [V]

650 600 550 500 0

Time [hr]

Figure 11. Normal operating data for the Huatacondo microgrid battery bank (training set)

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Current [A]

Validation set

Voltage [V]

Points where batteries are charged 100%

Time [hr]

Figure 12. Normal operating data for the Huatacondo microgrid battery bank. Note that the intersections between the graphs and the dotted lines are where the battery is 100% charged (validation set). THE PROPOSED FUZZY-CIRCUIT MODEL Figure 13 is a schematic drawing of the fuzzy-circuit model proposed herein. It shows that the model consists of a voltage source (which depends on the state of charge) in series with an internal resistance that depends on the current and the SOC, as reported in [40] and [41]. The voltage source models the voltage variations within the battery due to SOC variations, while resistance is the internal resistance of the battery and how it depends on the magnitude of the charge/discharge current and the SOC [39]. When solving the circuit in Figure 13, assuming that the current is positive for the discharge process, the voltage at the battery terminals is given by Equation 9. R int( I , SOC )

I

VOC (SOC )

+ -

V

Figure 13. Battery model proposed in this study

Vk = VOC (SOCk) - IkRint (SOCk , Ik) Equation 9

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In Equation 9, the term represents the value of the SOC-VOC curve at a given point in time ‘k’ (obtained experimentally (see Figure 7) and given by Equation 4), while the term Rint (SOCk , Ik) represents the internal resistance in the battery bank as a function of SOC and the current level at that same point in time. As shown in Figure 8 (section 3.2.1), internal resistance data is only available for four current levels, so to obtain internal resistance data for any power level and any SOC level, we propose combining these four curves using fuzzy logic to approximate the internal resistance of the batteries, based on available data, for power levels not provided by the experimental data. The internal resistance (Rint) is now a fuzzy resistance which, based on the four curves above (Equations 5, 6, 7, and 8), combines data (using fuzzy rules) to obtain internal resistance values ​​at other current levels for which there is no data. The fuzzy rules are set out below:6 Regla 1: If Ik is A10,1 then Rint = 0.070 • SOC 4k - 0.382 • SOC 3k + 0.619 • SOC 2k - 0.383 • SOCk + 0.118 1

Regla 2: If Ik is A15,2 then Rint = 0.067 • SOC 4k - 0.338 • SOC 3k + 0.529 • SOC 2k - 0.316 • SOCk + 0.095 2

Regla 3: If Ik is A25,3 then Rint = 0.031 • SOC 4k - 0.219 • SOC 3k + 0.391 • SOC 2k - 0.253 • SOCk + 0.079 3

Regla 4: If Ik is A32,4 then Rint = 0.083 • SOC 4k - 0.284 • SOC 3k + 0.374 • SOC 2k - 0.208 • SOCk + 0.063 4

Fuzzy sets A10,1, A15,2, A25,3, A32,4 and are defined as Gaussian functions centered on the current levels for which internal resistance data is available (10, 15, 25, and 32 Amperes, respectively), so the only free parameters of the model thus established are the standard deviations of each of these Gaussian functions. The values of each of these standard deviations are determined in the model training process (using the experimental test shown in Figure 9). Finally, the battery terminal voltage is expressed using Equation 9 in which internal resistance is modeled as fuzzy resistance (based on the fuzzy rules above), the mathematical formulation for which is given by Equation 10. In this equation, wj is the degree of activation of the rule ‘j’ for the current flowing in or out of the battery at point of time ‘k’. ∑4j =1 wj (Ik ) Rint (SOCk ) j Equation 10 Rint (SOCk , Ik) = 4 4 ∑ j =1 wj (Ik ) ADAPTING THE FUZZY-CIRCUIT MODEL TO THE HUATACONDO MICROGRID BATTERY BANK The battery model described in the above section was developed for the pilot battery bank consisting of three Trojan T-105 batteries in series (see Figure 5). Therefore, the next question was how to adapt this model to the Huatacondo microgrid battery bank, which consists of 96 batteries of the same type connected in series (see Figure 10). To do this, we considered two key aspects. The first was to move the internal resistance curves (shown in Figure 8) and the SOC-VOC curve (Figure 7) from three batteries in series to 96 batteries in series, and the second was to quantify the ‘age’ effect of the microgrid batteries in these curves.7 The first issue could be solved easily by assuming that battery behavior is relatively similar and multiplying each curve by a factor of 96/3 for a good approximation of the microgrid battery bank. 6

One for each internal resistance curve for which data is available (see Figure 8)

7

Bear in mind that battery age, i.e., the number of charge/discharge cycles, affects the amplitude of these curves

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The second issue could be solved by multiplying each curve8 by a factor (a0, a1, a2, a3, a4) to take into account the impact of battery age on them. Then the value of each factor associated with each curve was determined with the normal operating data for the microgrid battery bank (training test shown in Figure 11). Given the above, the model for the Huatacondo microgrid battery bank is as shown below. Equation 9 is maintained, so the terminal voltage of the battery bank is given by: ∑4=1 wj (Ik ) Rint (SOCk ) j Vk = VOC (SOCk ) — Ik Rint (SOCk , Ik ) = VOC (SOCk ) — Ik Equation 11 4 ∑ j =1 wj (Ik ) The curve relating the open-circuit voltage to the state of charge, given by Equation 4, undergoes certain changes and results as follows: 96 VOC (SOC ) = • (3.755SOC3 — 5.059SOC2 + 3.959SOC + 17.064) • a0 3 Equation 12 The fuzzy rules presented above undergo certain changes relating to internal resistance polynomials, and result as follows: Regla 1: If Ik is A10,1 then Rint = 0.070 • SOC 4k - 0.382 • SOC 3k + 0.619 • SOC 2k - 0.383 • SOCk + 0.118 1

Regla 2: If Ik is A15,2 then Rint = 0.067 • SOC 4k - 0.338 • SOC 3k + 0.529 • SOC 2k - 0.316 • SOCk + 0.095 2

Regla 3: If Ik is A25,3 then Rint = 0.031 • SOC 4k - 0.219 • SOC 3k + 0.391 • SOC 2k - 0.253 • SOCk + 0.079 3

Regla 4: If Ik is A32,4 then Rint = 0.083 • SOC 4k - 0.284 • SOC 3k + 0.374 • SOC 2k - 0.208 • SOCk + 0.063 4

Since fuzzy rules A10,1, A15,2, A25,3, and A32,4, were set using the pilot battery bank data, we need only to determine the parameters a0, a1, a2, a3 and a4 to adapt the fuzzy-circuit model for the pilot bank to the Huatacondo microgrid battery bank. These parameters are determined with the training set shown in Figure 11. Finally, using this simple procedure we can adapt the model for the pilot bank to the microgrid battery bank. This model will then be used to formulate the Kalman Filter algorithm and estimate the state of charge of the battery bank. KALMAN FILTER-BASED ESTIMATOR Due to the non-linear nature of the problem of estimating the state of charge, we use a variant of the Kalman Filter called the Extended Kalman Filter (EKF), which simply linearizes the problem at each point in time around its operating point, and then applies the Kalman Filter algorithm. To use the Extended Kalman Filter method, we propose a process model using state variables.9 We will assume Gaussian white noise in both the measurements and states of the system. The algorithm for the Extended Kalman Filter is below. 8 Given by equations 4, 5, 6, 7, and 8 9 The microgrid battery bank model in this case

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The following system is expressed in state variables: x (k + 1) = f ( x (k), u (k), v (k)) y (k) = g ( x (k), n (k)) Where the variable ‘x’ represents the internal state of the system (in this case, the state of charge), ‘u’ represents the input to the system (current in this case), ‘y’ represents the system output (voltage, given by the fuzzy-circuit model for the microgrid battery bank), and finally ‘v’ and ‘n’ represent the state and measurement noise, respectively. Both noises are distributed according to a normal zero mean and an R and Q covariance matrix, respectively. We further assume that the noise is correlated neither with the state nor with the output. Based on the above, the EKF algorithm would be as follows: Extended Kalman Filter Algorithm Prediction Stage 1. Forward projection of the state: ˆx (k) = f ( x (k - 1), u (k - 1)) 2. Forward projection of the covariance: P̂ (k) = A(k) P (k - 1) A(k)T + R(k) Updating Stage 3. Computation of the Kalman gain: ˆ C (k)T [ C(k) P(k) ˆ C(k)T + Q(k)]-1 G (k) = P(k) 4. Updating the state with the measurement y(k): x (k) = ˆx (k) + G (k) [ y (k) - g (x (k))] 5. Updating the covariance of the error: ˆ (k) P (k) = [ I - G (k) C (k)] P Produces x(k), P(k) (k) C = dg In the above algorithm, we have A = dfdx(k+1) and (k) dx (k) starting point (start state of the filter).

, and it is necessary to give the filter a

As mentioned in this section, using the Extended Kalman Filter algorithm requires expressing the battery bank model in state variables, a formulation that is given by Equation 13 and 14. ŋ (Ik) TIk SOCk+1 = SOCk — Equation 13 Cn ∑4j =1 wj (Ik ) Rint (SOCk ) j Vk = VOC (SOCk ) — Ik Equation 14 ∑4j =1 wj (Ik )

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Where Cn is the nominal capacity10 of the battery bank, T is the sampling period (10 [s]) and ŋ (Ik) is the Coulomb efficiency reported in [4]. Based on this system, the Extended Kalman Filter algorithm is then developed to design the charge status estimator. The Kalman Filter algorithm based on the fuzzy-circuit model was programmed using the MATLAB® numerical computing software. Note: For practical purposes, this study assumes the following equation in the nomenclature used: SOCk+1 = SOC (k+1) (analog for all variables).

FINDINGS OF THE EXPERIMENT This section presents the findings obtained from the various steps described in the previous section. The findings will be presented in three sections: the first shows the value of the model parameters found for the pilot battery bank, the second gives the parameter values for adapting the pilot bank model to the microgrid battery bank, and the third offers the findings of the state-of-charge estimator applied to the validation set (test shown in Figure 12). THE BATTERY MODEL PARAMETERS The parameters of the battery model proposed herein is obtained through a process of training the model using data from the experimental test shown in Figure 9. The parameter identification process gives the following results for the standard deviations of the fuzzy sets: σ10 = 2.444, σ15 = 2.031, σ25 = 4.142, σ32 = 6.690. Finally, each of these standard deviations is associated with each of the fuzzy sets described in section 3.3 (A10,1, A15,2, A25,3, A32,4). The graphic form of these fuzzy sets is shown in Figure 14. Fuzzy sets

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Figure 14. Fuzzy sets for the battery model used for the pilot bank 10 185 [AH] according to the manufacturer [25]

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ADAPTING THE FUZZY-CIRCUIT MODEL TO THE HUATACONDO MICROGRID BATTERY BANK The model developed for the pilot battery bank should be extended to work with the microgrid battery bank, for which we determines the parameters a0, a1, a2, a3 and a4 (which show the impact of battery ‘age’), as described in section 3.4. To accomplish this, we used the battery model specified in the previous section, taking into account the variations discussed in section 3.4. To train this model with the training set shown in Figure 11, we determined the following values for these parameters: a0 = 1.074, a1 = 0.1141, a2 = 0.3471, a3 = 0.4457 and a4 = 0.2297. With these parameters known, the model for the microgrid battery bank is complete and we only need to validate its performance with the validation set shown in Figure 12, which gives the results shown in Figure 15. This figure shows the actual battery bank response to the current demanded by the microgrid (in blue), and the model response to the same demanded current profile (in green). The root mean square error (RMSE) between the actual output and the fuzzy-circuit model output is approximately 8 [V], which is small given the magnitude of the battery bank’s working voltage. RMSE:7.9749 [V]

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Figure 15. Battery model performance in the validation set Having tested the effectiveness of the battery model created for the Huatacondo microgrid bank, we can use this model with the Extended Kalman Filter algorithm described in Section 3.5 to implement the SOC estimation algorithm. Note that because of the way the fuzzy-circuit model is designed, it is possible to obtain a battery model that faithfully reproduces the dynamics of the microgrid battery bank. Furthermore, the proposed model has the advantage of not interrupting the normal microgrid operations when performing specific tests to determine battery model parameters. This is because the specific bank tests are conducted on a pilot battery bank and then extrapolated from fuzzy modeling and normal operating data for the microgrid to represent the dynamics of the microgrid battery bank effectively.

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PERFORMANCE OF THE PROPOSED STATE-OF-CHARGE ESTIMATOR The state of charge (SOC) estimator bases its formulation on the Extended Kalman Filter algorithm described in section 3.5 and the model for the microgrid battery bank described in section 3.4, which uses the experimentally determined parameters reported in sections 4.1 and 4.2. The entire estimation algorithm is programmed using the MATLABÂŽ numerical computation software. Figure 16 shows the estimator performance with the validation set (test shown in Figure 12) for proper initialization of the algorithm starting point. Figure 16 also shows the battery bank voltage and the vertical lines indicate the points at which batteries are known to be fully charged. These points show that the estimator is close to 100%, which qualitatively validates its performance. During the actual implementation of the Huatacondo microgrid, the estimator output (the pink curve in Figure 16) is a permanent input into the EMS. Based on this estimate and others (see Figure 2), the EMS dispatches energy from the power generation units of the microgrid. Note that SOC estimation is performed in real time. Figure 17 shows a comparison between the proposed estimator and an estimator based on the integral electric current11 (described in Section 2.2). As shown in this figure, the integral electric current estimator is seriously hampered by current measurement noise and by the number of charge/discharge cycles, which in time move the estimator between 0% and 100%, yielding inconsistent estimation results. This is not seen with the proposed estimator, as it is not influenced by noise in measurements or in charge/discharge cycles. Finally, Figure 18 shows the performance of the proposed estimator with various degrees of error in its initialization. As shown in this figure, although the algorithm was initialized incorrectly, it starts to converge on the real state of charge values in time.

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Figure 16. Estimator performance with the validation set. Prior to the SOC estimator, it was only possible to know when the batteries were fully charged. 11 Both estimators with the start state correctly initialized 82

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Figure 18. Performance of the proposed estimator with different degrees of error at the algorithm starting point

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Finally, it is important to note that prior to developing this estimator, the EMS could not be run on the microgrid because without the SOC estimation, it was only possible to know when the batteries were fully charged (see Figure 16, top panel). Therefore, it was not clear how much energy was available at intermediate points, which made in impossible to dispatch the generation units of the microgrid. By contrast, with the development of this estimator, it is now possible to know the SOC at all times (see Figure 16, lower panel). CONCLUSIONS The experimental system that was designed and built worked properly and generated numerous experimental tests, which were used to train and validate the fuzzy-circuit model proposed herein. Given the formulation of the fuzzy-circuit model (based on fuzzy rules), it can easily be extended to take into account the effects of temperature and other battery technologies. In addition, with limited amounts of internal resistance curve data, the fuzzy-circuit model can interpolate good variance profiles over time. The methodology proposed was effective in adapting the battery model developed for a pilot bank to a battery bank in a real application (in this case, the Huatacondo microgrid). This provides a model that faithfully represents the dynamics of the microgrid bank with no need to suspend normal functioning in order to perform specific tests to determine the model parameters. All of the above served to develop and validate a state-of-charge estimator for a microgrid based on renewable energy. The proposed estimator has the feature of not interrupting the normal microgrid operations to set the parameters for the fuzzy-circuit model and the Kalman Filter. Another important point of the proposed estimation algorithm is that its simplicity makes it possible include it in an embedded system. We also found that the proposed algorithm is highly immune to noise from the sensors and can recover from erroneous initializations in the algorithm starting point. Finally, we should mention that while the algorithm uses a series of mathematical equations that may seem complicated, they are only additions, multiplications and divisions. Therefore, the entire algorithm can be easily programmed into a physical device equipped with a microprocessor12 that runs the algorithm proposed herein based on voltage and current readings from the actual system and that delivers the system’s state of charge in real time. The above has two interesting commercial implications. The first is to build a prototype to run the estimator algorithm, and the second is to conduct specific experimental tests on the batteries to identify the relevant parameters of the algorithm.

12 For example, a DSP (digital signal processor).

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REFERENCES R. Bhoyar y S. Bharatkar, «Potential of MicroSources, Renewable Energy sources and Application of Microgrids in Rural areas of Maharashtra State India,» Energy Procedia, nº 14, p. 2012 – 2018, 2012. «http://www.die.uchile.cl/2010/,» de último acceso 27 junio del 2015. «http://www.centroenergia.cl/ce-fcfm/?page_id=1004,» último acceso 20 de junio del 2015. B. Severino, «Modelagem de um sistema fotovoltaico y um banco de baterias de plomo ácido como elementos de una micro-rede,» Tesis para optar al grado de ingeniero cicil electricista, Universidad de Chile, Santiago, Chile, 2011. T. S. Ustun, C. Ozansoy y A. Zayegh, «Recent developments in microgrids and example cases around the world—A review,» Renewable and Sustainable Energy Reviews, nº 15, pp. 4030-4041, 2011. M. Soshinskaya, W. H. Crijns-Graus, J. M. Guerrero y J. C. Vasquez, «Microgrids :Experiences, barriers and success fators,» Renewable and Sustainable Energy Reviews, nº 40, pp. 659-672, 2014. R. Palma-Behnke, C. Benavides, F. Lanas, B. Severino, L. Reyes, J. Llanos y D. Sáez, «A Microgrid Energy Management System Based on the Rolling Horizon Strategy,» Smart Grid, IEEE Transactions on, vol. 4, nº 2, pp. 996 - 1006, 2013. F. Á. Swinburn, «Diseño de um sistema de gestión de demanda baseado en lógica difusa para micro-redes,» de Tesis para optar al grado de Magíster en Ciencias de la Ingeniería, mención Eléctrica, Santiago, Chile, 2013. B. Pattipati, C. Sankavaram y K. R. Pattipati, «System Identification and Estimation Framework for Pivotal Automotive Battery Management System Characteristics,» Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol. 41, pp. 869 - 884, 2011. B. E. Olivares, M. A. Cerda, M. E. Orchard y J. F. Silva, «Particle-filtering-based Prognosis Framework for Energy Storage Devices with a Statistical Characterization of State-of-Health Regeneration Phenomena,» Instrumentation and Measurement, IEEE Transactions on, vol. 62, pp. 364 - 376, 2013. L. Ran, W. Junfeng, W. Haiying y L. Gechen, «Prediction of state of charge of Lithium-ion rechargeable battery with electrochemical impedance spectroscopy theory,» de Industrial Electronics and Applications (ICIEA), 2010 the 5th IEEE Conference on, Taichung, 2010. H. He, R. Xiong, X. Zhang, F. Sun y J. Fan, «State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model,» Vehicular Technology, IEEE Transactions on, pp. 1461- 1469, 2011. H. Zhang y M.-Y. Chow, «On-line PHEV battery hysteresis effect dynamics modeling,» de IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society, Glendale, AZ, 2010. E. Aranda, «Desarrollo de estrategia para el uso óptimo de la energía en um vehiculo solar,» de Tesis para optar al grado de ingeniero civil electricista de la Universidad de Chile, Santiago, Chile, 2008.

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G. L. Plett, «Extended Kalman filtering for battery management systems of LIPB-based HEV battery packs Part 2. Modeling and identification,» Journal of Power Sources, nº 134, pp. 262-276, 2004. E. Bianchi, «Elementos de electroquimica: electrolisis y acumuladores reversibles,» Apunte del curso: Aplicaciones industriales de la energía eléctrica, Departamento de Ingeniería Eléctrica, Universidad de Chile, Santiago, Chile, 2011. C. Burgos, «Estimativa del estado de carga para um banco de baterias baseada en modelagem difusa y filtro extendido de Kalman,» de Tesis para optar al grado de Magíster en Ciencias de la Ingeniería, mención Eléctrica, Santiago, Chile, 2013. S. Piller, M. Perrin y A. Jossen, «Methods for state of charge determination and their applications,» Journal of Power Sources, vol. 96, pp. 113-120, 2001. F. Codecà, S. M. Savaresi y G. Rizzoni, «On battery State of Charge estimation: A new mixed algorithm,» de Control Applications, 2008. CCA 2008. IEEE International Conference on, San Antonio, TX, 2008. J. Xu, M. Gao, Z. He, Q. Han y X. Wang, «State of Charge Estimation Online Based on EKF-Ah Method for Lithium-Ion Power Battery,» de Image and Signal Processing, 2009. CISP ‘09. 2nd International Congress on, Tianjin, 2009. A. Shafiei, A. Momeni y S. Williamson, «Battery modeling approaches and management techniques for Plugin Hybrid Electric Vehicles,» de Vehicle Power and Propulsion Conference (VPPC), 2011 IEEE, Chicago, IL, 2011. J. L. V. Gutiérrez, «Empleo de la técnica de espectroscopía de impedâncias electroquímicas para la caracterización de biomateriales. Aplicação a una aleación biomédica de Co-Cr-Mo,» de Tesis para optar al grado de magíster en seguriad industrial y medio ambiente de la universidad politécnica de Valencia, Valencia, España, 2007. J. Lee, O. Nam, J. Kim, B. H. Cho, H.-S. Yun, S.-S. Choi, K. Kim y S. Jun, «Modeling and Real Time Estimation of Lumped Equivalent Circuit Model of a Lithium Ion Battery,» de Power Electronics and Motion Control Conference, 2006. EPE-PEMC 2006. 12th International, Portoroz, 2006. L. Ran, W. Junfeng, W. haiying y L. Gechen, ««Prediction of state of charge of Lithium-ion rechargeable battery with electrochemical impedance spectroscopy theory,» de Industrial Electronics and Applications (ICIEA), 2010 the 5th IEEE Conference on, Taichung, 2010. K. Astrom y B. Wittenmark, Computer-controlled Systems: Theory and Design, Prentice Hall, 1996. H. Dai, Z. Sun y X. Wei, «Online SOC Estimation of High-power Lithium-ion Batteries Used on HEVs,» de Vehicular Electronics and Safety, 2006. ICVES 2006. IEEE International Conference on, Beijing, 2006. X. Liu, Y. He y Z. Chen, «State-of-Charge estimation for power Li-ion battery pack using Vmin-EKF,» de Software Engineering and Data Mining (SEDM), 2010 2nd International Conference on, Chengdu, 2010. H. He, R. Xiong, X. Zhang, F. Sun y J. Fan, «State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model,» Vehicular Technology, IEEE Transactions on, vol. 60, pp. 1461 - 1469, 2011.

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M. Gao, Y. Liu y Z. He, «Battery state of charge online estimation based on particle filter,» de Image and Signal Processing (CISP), 2011 4th International Congress on, Shanghai, 2011. M. Charkhgard y M. Farrokhi, «State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF,» Industrial Electronics, IEEE Transactions on, vol. 57, pp. 4178 - 4187, 2010. A. J. Salkind, C. Fennie, P. Singh, T. Atwater y D. E. Reisner, «Determination of state-of-charge and state-ofhealth of batteries by fuzzy logic methodology,» Journal of Power Sources, nº 80, p. 293 – 300, 1999. R. Babuska, Fuzzy Modeling for Control, United States: Kluwer Academic Publishers, 1998. P. Singh y A. Nallanchakravarthula, «Fuzzy logic modeling of unmanned surface vehicle (USV) hybrid power system,» de de Intelligent Systems Application to Power Systems, 2005. Proceedings of the 13th International Conference on, Arlington, VA, 2005. B. Sun, L. Wang y C. Liao, «SOC estimation of NiMH battery for HEV based on adaptive neuro-fuzzy inference system,» de Vehicle Power and Propulsion Conference, 2008. VPPC ‘08. IEEE, Harbin, 2008. P. Singh, C. Fennie y D. Reisner, «Fuzzy logic modelling of state-of-charge and available capacity of nickel/ metal hydride batteries,» Journal of Power Sources, vol. 136, nº 2, pp. 322-333, 2004. P. Singh, R. Vinjamuri, X. Wang y D. Reisner, «Design and implementation of a fuzzy logic-based state-ofcharge meter for Li-ion batteries used in portable defibrillators,» Journal of Power Sources, vol. 162, nº 2, pp. 829-836, 2006. D. G. Murillo, «Modelado y análisis de sistemas fotovoltaicos,» de Tesis doctoral, Barcelona, España, 2003. D. S. Newlin, R. Ramalakshmi y M. .. Rajasekaran, «A performance comparison of interleaved boost converter and conventional boost converter for renewable energy application,» de Green High Performance Computing (ICGHPC), 2013 IEEE International Conference on, Nagercoil, 2013. C. Burgos, D. Sáez, M. E. Orchard y . R. Cárdenas, «Fuzzy modelling for the state-of-charge estimation of lead-acid batteries,» Journal of Power Sources, vol. 274, pp. 355-366, 2015. J. Copetti y F. Chenlo, «Lead/acid batteries for photovoltaic applications. Test results and modelling,» Journal of Power Sources, vol. 47, pp. 109-118, 1994. J. Copetti, E. Lorenzo y F. Chenlo, «A general battery model for PV system simulation,» Progress in Photovoltaics: Research and Applications, vol. 1, pp. 283-292, 1993. S. Piller, M. Perrin y A. Jossen, «Methods for state of charge determination and their applications,» Journal of Power Sources, pp. 113-120, 2001. R. A. Huggins, Energy Storage, New York: Springer, 2010. G. L. Plett, «Extended Kalman filtering for battery management systems of LIPB-based HEV battery packs Part 2. Modeling and identification,» Journal of Power Sources, nº 134, pp. 262-276, 2004.

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A. J. Salkind, C. Fennie, P. Singh, T. Atwater y D. E. Reisner, «Determination of state-of-charge and state-ofhealth of batteries by fuzzy logic methodology,» Journal of Power Sources, nº 80, p. 293 – 300, 1999. «http://www.trojanbatteryre.com/PDF/datasheets/T105_TrojanRE_Data_Sheets.pdf,» de último acceso 20 de enero del 2015.. «http://www.trojanbattery.com/es/product/t-105_plus/,» de último acceso 20 enero 2015

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COLOMBIA

NEW CONTRIBUTIONS TO THE DESIGN OF PHOTOVOLTAIC BUILDINGS FOR SUSTAINABLE CITIES LUIS FERNANDO MULCUE NIETO Orientation: Professor Llanos Mora Lรณpez

ABSTRACT PV systems in Latin America are still scarce. There are very few technical regulations that allow integrate architecturally generators to buildings, and there are not rigorous methods to facilitate the development of the sector. This research proposes models and technical standards that can be used for the development of Building Integrated Photovoltaics (BIPV) in Latin America and the world. The structure of the research was divided into two parts: In the first part, a methodology is proposed to establish technical standards worldwide. This advance allows limiting the energy losses due to shading and orientation of the building surfaces so that buildings have energy efficiency in the design stage. The case study to various cities in Colombia was also performed by a comparative analysis of the photovoltaic potential available in the facades and roofs. In the second part, the issue of predicting the energy generated by a building with BIPV technology is developed. For this, a simple and reliable model to estimate the Performance Ratio (PR) of the system, it was developed. This model needs only 4 input parameters: The average temperature of the city, latitude, and the angles of inclination and orientation of the PV array. This model has a high degree of precision, avoiding the complex simulation of over 20,000 operations. Finally, it was developed the analysis of the angular and dirt losses, temperature losses, losses DC-AC conversion, and system performance ratio (PR) to several cities in Colombia. With these results, anyone can make decisions in the implementation of projects with self-sustainable buildings, and contribute to the model city of the future. KEYWORDS: Photovoltaic Buildings, energy produced by a photovoltaic system, Building integrated photovoltaics

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INTRODUCTION Photovoltaic solar energy is an excellent option to meet the energy demands of the world population, by generating electricity in a distributed manner (1) modern society is approaching physical limits to its continued fossil fuel consumption. The immediate limits are set by the planet’s ability to adapt to a changing atmospheric chemical composition, not the availability of resources. In order for a future society to be sustainable while operating at or above our current standard of living a shift away from carbon based energy sources must occur. An overview of the current state of active solar (photovoltaic, PV . Therefore, many generators throughout the world have been installed. In urban areas, prevail the so-called gridconnected photovoltaic systems (SFCR), which supply the energy needs of the building or house, while excess electricity is fed into the grid. Moreover, due to the economic and room constraints, it has become necessary to install photovoltaic panels on the surfaces of buildings. This resulted in a sector of great importance and development: Building integrated photovoltaics (BIPV), replaced several construction elements such as roofs, walls, windows, etc., by photovoltaic modules. An example of using BIPV architectural design was shown in Figure 1.

Fig 1. Example of a house designed using BIPV. Taken from: https://onyxgreenbuilding.wordpress.com/tag/ceu-university/

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One of the main goals in the field of BIPV is to achieve optimal solutions to aesthetic, economic and technical level. Ensuring that all new buildings are “zero energy buildings” (ZEB) (2) . To achieve this, it is necessary to analyze two aspects of great importance: The need for energy efficiency regulation in photovoltaic buildings First, to increase energy efficiency, it is necessary to maximize the amount of solar radiation incident on the generator. However, most times the above it is not possible, due to architectural and engineering factors involved in construction. For example, in countries around the Ecuador line, households’ ceilings receive much radiation per square meter of the surface than the facades. On the other hand, replacing a building material of a wall by a photovoltaic generator may be economically feasible. These facts make it necessary to make the following question: To what extent is advisable to implement the photovoltaic energy in any area of the ​​ building? In 2009, Spain became one of the first countries to respond to the previous question when it enacted the socalled Technical Building Code (CTE) (3) . In this document, limits are put on the losses caused by shading and orientation of the PV array. This regulation has contributed very successfully to amass architectural integration of photovoltaics in that nation. However, very few countries have technical regulations that optimize performance and energy efficiency in BIPV. In this regard, it is noteworthy that the worldwide it is necessary to unify criteria to enable the development of joint projects, technology transfer of materials and supplies, technically adapt the systems to each region, and reduce the environmental impact of waste. In the case of Colombia, it is estimated that the photovoltaic market sells about 300 kWp per year, mainly to isolated network systems (4) . If this figure it is extrapolated to the 30 years the sector has in the country; the total installed capacity would be around 9MWp (5) . This figure is very low if one considers the high levels of solar radiation available. Currently, the national government is promoting renewable energy, but unfortunately, there is still no technical standards for regulating development in the sector. A similar situation exists in most countries in Latin America. This research proposes a methodology for establishing technical standards, limiting losses by shading and orientation of photovoltaic systems in building’s ceilings. This contributes to the environmental sustainability of cities of the future. The requirement for new models that facilitate the design of photovoltaic buildings The second relevant aspect is the fact that it is essential to predict the amount of electricity produced by the installation; the net energy balance can be made. This calculation must be done at one of the stages of the system’s design by engineers and architects. In 1998, the International Electro technical Commission (IEC) published the International Standard IEC 61724. This standard describes the recommendations for the analysis of the electrical behavior of photovoltaic systems. One of the characteristic parameters that constitute the annual energy produced. This can be calculated in the Photovoltaic Systems Connected to Grid (6) according to the equation: EPV = Ga (β,α) • Ppeak • PR Equation 1 GSTC

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Where Ga(β,α) the annual solar irradiation on the generator surface, Ppeak PV peak power is installed, PR annual performance of the installation called “Performance Ratio“and GSTC solar irradiance under standard conditions of measurement equal to 1 kW / m2 . The value of Ga to (β,α) it can be easily obtained through graphics of the Irradiation factor or FI (7), (8). Therefore, the problem of calculating the electricity produced is reduced mainly to determine the value of PR. However, this task has not been easy because performance depends on several factors such as solar radiation available on the geographical location of the facility, climate, orientation and inclination of the surfaces used, proper system design and the quality of the components that comprise it, among others. This fact has hampered architects in making their designs, and in most cases, the engineers use specialized software. As a result, there is a delay in the development of Building Integrated Photovoltaic (BIPV), with a corresponding impact on the environmental sustainability of cities. To solve the above problem, several methods have been proposed to try to predict the influence of different variables on the amount of electricity generated. Some of them are analytical, such as those employed by Osterwald (9) Araujo (10) or Green (11) ; that calculate temperature losses. There have also been proposed other methods including more variables based on artificial neural networks (12) (13) . However, most of these are very hard to implement, while others do not take into account all the characteristics of the system. Another way proposed to solve the problem is to introduce a standard performance PR = 0.75 for any photovoltaic system (14) which is not suitable because place’s own variables must be taken into account. For example, PR studies have been reported in 8 countries, obtaining values between 0.42 and 0.81 (15) . This is consistent since the performance of photovoltaic modules depends on the temperature of the place. Likewise, latitude plays an important role, as its effect on the solar radiation causes the power delivered to the input of the inverter to reach very low levels within certain periods, thus reducing the efficiency of DC-AC conversion. As mentioned, the great number of factors make it very difficult to predict the performance of the integrated PV system in a (BIPV) building, so it is necessary to implement a simple method that can be used by architects and engineers. This is important, since many countries need to expand photovoltaics. In Colombia, for example, 52% of the country is made up of not connected areas, that is, places with no access to electricity services through the National Interconnected System (16) . Likewise, it is advisable to implement BIPV in the cities in order to obtain environmental and economic benefits. This paper proposes a simple and reliable expression to estimate the PR, which can be used in low-latitude countries, where Colombia would be the case study. This paper proposes a simple and reliable expression to estimate the PR, which can be used in low-latitude countries, where Colombia would be the case study.

METHODOLOGY METHODOLOGY FOR ENERGY EFFICIENCY REGULATIONS IN PHOTOVOLTAIC BUILDINGS The following procedure was proposed to set loss limits for orientation and shading to different cities in Colombia, and can be used for any other country. As a convention, each of the cities to is studied in the country (Colombia), were named as “site 2”. Likewise, “site 1” refers to the country, in this case, Spain, but it can be any other.

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We first proceeded to calculate the average annual amount of radiation of a surface depending on its inclination and azimuth. Then the maximum amount compared incident in site 2 was compared with a corresponding one in the worst facade on site 1. As a result, we can see the limit losses percentage due to orientation and inclination per city. This approach is useful because: A. Universal limits are not set, considering that the solar resource is different in each region. This is important because equal proportions of global radiation, may correspond to very different values of ​​ solar radiation on the surfaces. B. The fact that the amount of solar energy received is the same per square meter, it means that countries that receive more annual radiation have more variety of possibilities of architectural integration. Conversely, if a universal rate were adopted in equatorial countries it would not be possible to use any facade BIPV (Concerning Spain). C. From the economic and environmental point of view it is more beneficial. This is because replacing building materials for photovoltaic modules is more advantageous in countries with the highest number of annual irradiation. Moreover, to find the limits of shading losses in site 2 (Colombia), it was taken into account that the diffuse fraction is different than at site 1 (Spain). Thus, the loss limit percentage equals a fraction of the maximum irradiation, which is physically possible to be loss due to shading. The main idea was to match that fraction for both sites. For example, if Spain loses a third of the maximum radiation, Colombia will keep the same fraction.

Calculation of the maximum loss allowed by orientation and inclination of the generator To set the maximum percentage of losses in each surface. It was established a 100% reference, i.e., the maximum annual solar irradiation G to (Β opt). After obtaining G a (Β opt) we proceeded to calculate the minimum amount of annual solar irradiation for every city in Colombia G a, MIN (90.0) a facade in Spain. According to the CTE, losses for orientation and tilt in any surface for BIPV cannot exceed 40%. This surface was appointed as the worst permissible facade. Then this facade was “Moved” to the city in question in Colombia. Therefore, the allowable rate for location 2 is then given by:

(

)

Ga, MIN, 1 (90,0) Equation 2 Lβ, α, MAX, 2 = 100 1 — Ga, 2 (βopt ) Where the subscripts 1 and 2 refer to sites 1 and 2 respectively. Calculate the maximum loss allowed by shading generator Similarly, the maximum allowable shading losses for each reference city in Colombia were calculated. In Spain, the CTE puts a limit to 20% for BIPV. To move the equivalent of this percentage to Colombia, it was calculated an equivalent fraction to this 20%, regarding the permanent shading situation. In this hypothetical case, the radiation not perceived would be the same as the direct radiation Ba(0), plus diffuse circumsolar DaC(0); both measured on a horizontal surface.

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Considering the above, we proceeded to calculate the diffuse fraction of the main cities of Spain. To this end, there were use the data from the Atlas of Solar Radiation in Spain (17) , published by the State Meteorological Agency (18) . The average value of the previous parameter representing the country it was found, and the remaining fraction is direct radiation. We then proceeded to compare this value with the maximum stipulated according to the reference tables published by the CTE. From this comparison, the representative values ​​of Ba were deducted to (0) and DaC(0) to site 1. It is assumed that the country has a reference standard with maximum shading losses Lshading, MAX 1. The fraction corresponding to this percentage, relative to the maximum physically possible loss irradiation, was determined for site 1: Lshading, MAX, 1 ƒMAX, losses, 1 = Equation 3

(

C 100% Ba,1 (0) + Da,1 (0) Ga, 1 (0)

)

Where the subscript 1 indicates the irradiation the country, while Lshading, MAX 1 was taken as 20%, as set by the Spanish regulations. The fraction of the equation [3] is tied to its equivalent in Colombia. Thus, the maximum shading losses to site 2 were calculated using:

(

)

C Lshading, MAX, 2= 100% Ba,2 (0) + Da,2 (0) ƒMAX, losses, 1 Equation 4 Ga, 2 (0)

Where subscript 2 indicates the irradiation of each city of Colombia. METHODOLOGY FOR THE DEVELOPMENT OF PHOTOVOLTAIC ENERGY PRODUCTION MODEL The following procedure is proposed to find a simple expression of PR for low-latitude countries, but can be used to extend the model to other regions worldwide, properly assigning parameters to adjust the results. We first proceeded to calculate the annual average amount of radiation of a surface depending on its inclination and azimuth. Then the angular and dirt losses were calculated. With the corrected amount of irradiance and environment temperature, the input power in each photovoltaic module was calculated, thereby determining temperature losses. Then the investor losses were calculated using the equation of their performance characteristic curve. Next, PR contour diagrams were constructed according to the inclination and orientation for each city. Finally, a careful analysis was carried out, so that a simple equation that allows reproducing the results obtained by the process described in the preceding paragraph was performed. The following method is described below. Getting irradiation and temperature data The first step was to have global solar radiation data for different cities of Colombia. The source for this information was the website specialized in renewable energy projects named International RETScreen (19) , with support provided by 6,700 ground weather stations and NASA satellites. As a result of this step, the 12 monthly average daily values ​​of global solar radiation on a Gdm(0) horizontal surface were found. Similarly, the temperature data were obtained from the website of the World Meteorological Organization

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(20) , Whose overall Climatological information is based on monthly averages 30 years, between 1971 and 2000. Thus, the 12 monthly values of ​​ minimum and maximum temperature of each city were obtained. Calculation of annual solar radiation on inclined surfaces in Colombia Taking figures Gdm(0) as a starting point, we proceeded to break every value in diffuse radiation Ddm(0) and direct Bdm(0). It was taken into account the fact described by Liu and Jordan (21) according to which the relationship between the clearness K index Tm and diffuse fraction KDm is independent of the latitude. As dependence on these parameters the equation proposed by Page (22) Valid for latitudes between 40 ° N and 40 ° S. To calculate the extraterrestrial solar irradiation on a horizontal surface, it was used the term in the proposal (23). Also, for the angle of solar declination, Spencer (24) expression was used. Once obtained the daily global radiation components, Ddm(0) and Bdm(0), the respective time values ​​were calculated, Dh(0) and Bh(0). This was done using the terms proposed by Necklaces - Pereira and Rabl (25). The next step was to calculate the hourly global irradiation on the surface of the generator Gh(Β, α). To this it was taken the model of the three components, which has proven quite accurately (26) , and it states that the incident radiation consists of direct radiation Bh(Β, α), diffuse Dh(Β, α), and reflected Rh(Β, α). The time interval Δt is taken equal to 0.25h. To calculate the diffuse component of the inclined surface, in the literature, there are more than 20 models. The isotropic model - Davies (27) was selected because in several comparative studies it is noted for its high accuracy and simplicity (28)(29)(30)(31). This considers the diffuse radiation consisting of two parts; one circumsolar component DC (Β, α) coming directly from the sun, and another isotropic component DI (Β, α) from the entire celestial hemisphere. To calculate the reflected component or albedo, it was assumed that it is an infinite extent horizontal ground, and that reflects light isotropically. The reflectivity of the ground was generally taken as ρ = 0.2. Calculation of angular losses and dirt Even when there have been proposed several expressions for calculating the angular losses (32) (33) (3. 4) it was used the Martin-Ruiz (35) model because it reproduces actual results (36) and it is relatively simple. Thus, the global irradiation was corrected taking into account both losses as the angular dirt. Calculation of losses due to temperature The room temperature varies throughout the day, but initially, there were only two available data: the average minimum temperature T A.M. and the maximum T A.M. To take this into account, a model assuming the following was used (2.3): a. The minimum room temperature always occurs at sunrise, i.e. when ω = ωs. (ω represents the angle solar time) b. The maximum environment temperature occurs two hours after solar noon, i.e. when ω = π / 6. c. Throughout the day the room temperature varies with two half cycles of cosine functions, depending on the solar time ω. The nominal operating temperature of the generator (TNOC) was taken equal to 46 ° C, a typical value issued by the manufacturers of photovoltaic modules. With this value, and the equation proposed by

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Osterwald (9) it was found the maximum output power Pmáx. Thus, they were determined instantaneous temperature due to losses, according to the equation proposed by Caamafio (37) . Losses calculating for DC-AC conversion With the power found in the previous point and the model proposed by Schmidt (38) the instantaneous efficiency of the inverter is calculated. Then the instantaneous power output was obtained; the total losses were determined from a DC-AC conversion. Determination of other types of losses Regarding other types of losses, same losses were taken from to the average values ​​reported in the literature, (39) (40) (41) analyses and evaluation of residential PV systems in the Japanese Monitoring Program, on which JQA was subsidized by NEDO (New Energy Development and Industrial Technology Organization (42): Lost by differences with the rated power of 5% decoupling losses 3%, tracking errors missed the point of maximum power of 6%, the ohmic losses of 1%, and shading losses of 7%. Calculation of the overall system performance - PR The final performance ratio (PR) of the installation was calculated with equation (1). This procedure is cyclically repeated, so the value of PR for each pair of coordinates (β, α) of the city in question was obtained. The inclination β was varied between 0 ° and 90 °, taking Δβ = 5 °; α and orientation between -180 ° and 180 °, taking Δα = 5 °. This way, it was possible to cover all possible surfaces of the photovoltaic building. Finally, the process was used again on 16 cities in Colombia located between latitudes 4 ° S and 12 ° N. Some of these cities are shown in Figure 2.

Fig. 2. Location of some of the cities studied. Image used with the authorization of IGAC (43). There were also taken into account a few cities in Central America.

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RESULTS AND DISCUSSION TECHNICAL REGULATIONS FOR ENERGY EFFICIENCY IN PHOTOVOLTAIC BUILDINGS

Limit losses due to orientation and tilt (%)

To propose a simple expression to calculate the limits of losses due to orientation and tilt for any country, Figure 3 was performed. We can see that the values go between 30% and 60%, depending on the maximum solar radiation available in the place. Accordingly, for the maximum allowable value of the losses for this concept, it is only necessary to place the value of Ga(βopt) from the city. 70 65 60 55 50 45 40 35 30 25 20 15 10 5 0

1300

1400

1500

1600

1700 1800 1900 2000 Ga (βopt) (kWh / m2 year)

2100

2200

2300

2400

Fig 3. Limit losses due to orientation and inclination, depending on the maximum solar irradiation of the City

Shading losses limits (%)

Similarly, to find the limits of shading losses Figure 4 was performed. It can be seen that the values go​​ between 10% and 25%, depending on the fraction of scattered radiation place. 40 35 30 25 20 15 10 5 0

Spain

0.20

0.25

0.30

0.35 0.40 0.45 Difussed fraction (DF)

0.50

0.55

0.60

Fig 4. Shading losses limits, depending on the fraction of diffusely

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These tools form the proposed technical regulations and are very useful to find the maximum permitted losses due to shading, orientation, and inclination. They can be used to ensure an energy efficient design in photovoltaic buildings, just by knowing the diffuse fraction of the project’s site and its maximum annual solar radiation. ANGULAR LOSSES AND DIRT The results of the angular losses for the 16 cities in Colombia were recorded in Table 1. We can see that the minimum values ​​of this variable are ranging from 4% to 5% while the maximum are between 11% and 15%. This behavior differs slightly from that reported for some cities in Europe (35) according to which the maximum losses were 8% to 90 ° slope. This can be explained by the fact that in the equatorial countries, the facades oriented south receive less irradiation than those in high latitudes. Table 1. Results obtained for the angular losses.

98

-4.2

Minimum angular losses 5%

Minimum angular losses 12%

Pasto

1.2

5%

11%

Tumaco

1.8

4%

12%

Popayán

2.5

5%

12%

Neiva

3

5%

12%

Cali

3.6

5%

12%

Villavicencio

4.2

5%

11%

Bogotá

4.7

4%

13%

Manizales

5.1

5%

12%

Medellín

6.2

5%

12%

Barrancabermeja

0.5

4%

14%

Cúcuta

7.9

4%

13%

Montería

8.8

4%

13%

Valledupar

10.5

4%

14%

Barranquilla

10.9

4%

14%

San Andrés

12.6

4%

15%

City

Latitude φ (°)

Leticia

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Table 1 also shows that there is a rough tendency of increase of 1% in the maximum losses, for every 3 ° latitude. This is logical, since such losses are given for vertical surfaces facing north, which receive less radiation with increasing latitude. Figure 5 was prepared to better understand the behavior of angular losses on surfaces facing south, depending on their angle.

A. Angular losses for facades facing south Pasto

Bogotá

Cúcuta

Barranquilla

San Andrés

15 10 5 0

Leticia

Angular losses (%)

Angular losses (%)

Leticia

B. Angular losses for facades facing north

0

10

20

30

40

50

Tilt (º)

60

70

80

90

Pasto

Bogotá

Cúcuta

Barranquilla

San Andrés

20 15 10 5 0

0

10

20

30

40

50

Tilt (º)

60

70

80

90

Fig. 5. Annual losses versus angular inclination angle, for facing surfaces: A. the South. B. the North Looking at the figure 5.A, we see that the angular losses grow with inclination. However, every curve has a minimum, which is given to the optimum angle that maximizes the annual global irradiation. This trend can be better seen with greater latitude of the place, in this case here is San Andrés, the minimum occurs at approximately 15 °. To find the minimum and maximum losses on the ceilings (0 <β <30 °), Figure 5. B, shows the case of north-facing surfaces. When contrasting this with the figure 5.A, we can conclude that at least 4% will be lost on the ceilings due to angular concepts. Also, the maximum losses do not exceed 8% when the roof is oriented north. This is relatively good for the final performance of the system. The problem occurs in the facades, which rise from 11% to 15%. In order to meet the guidelines of the facades that allow increasing the yield of the photovoltaic system, Figure 6 was drawn, where we can see the angular losses as a function of azimuth. It can be seen that the optimum facades are oriented east and west, with angular losses between 6% and 7% for San Andres and Pasto, respectively. The reason for this is that the sun’s rays strike in a perpendicular direction on these surfaces on countries close the Ecuador.

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Angular losses on the facades Leticia

Pasto

Bogotá

Cúcuta

-30

0

Barranquilla

San Andrés

16

Angular losses (%)

14 12 10 8 6 4 2 0 -180

-150

-120

-90

-60

30

60

90

120

150

180

Azimuth (º) Fig. 6. Annual angular losses versus azimuth angle, for different types of facades.

TEMPERATURE LOSSES The results of temperature losses were recorded in Table 2. We can see here that the minimum values ​​of this variable range go between 3% and 5%, and are given on high inclinations. Moreover, the maximum range goes between 2% and 11%, and are obtained for slightly inclined surfaces. The latter are within expected ranges (39) . It also shows that the maximum losses are prone to increase according to the average room temperature, which is logical. Moreover, the losses do not vary more than 5%, approximately within each city. Table 2. Minimum and maximum temperature losses for every city in Colombia.

100

City

Average temperature Ta (° C)

Minimal losses

Maximum losses

Bogotá

11,7

-2,9%

2,2%

Pasto

13,3

-2,4%

1,6%

Manizales

16,6

-0,8%

4,0%

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Popayán

18,8

0,4%

5,1%

Medellín

22,3

1,8%

6,7%

Cali

24,4

2,6%

6,9%

Tumaco

26,2

3,3%

8,0%

Villavicencio

26,2

3,4%

8,0%

Leticia

26,3

3,5%

8,2%

Cúcuta

27,2

3,8%

9,1%

Barrancabermeja

27,6

4,0%

9,8%

San Andrés

27,6

3,4%

10,2%

Neiva

27,7

4,2%

8,8%

Montería

27,9

4,1%

9,5%

Barranquilla

28,3

4,1%

10,4%

Valledupar

29

4,6%

11,1%

Figure 7.A presents the variation of temperature losses for surfaces facing south. This shows a decreasing parabolic behavior, depending on the inclination. Between 0 ° and 20 ° can be assumed as constant, and then they decrease with the slope at a rate of 1% per 15 °. This decrease is due to the annual insolation received, it is less for more vertical surfaces, which causes less heat on the cells. Therefore, the facades have the best performance depending on the temperature. The cities of Pasto and Bogota obtained had losses. In about 2% for roofs, we can see that losses become negative for inclinations greater than 50 °. This means that final performance can be greater than the theoretical, by only using facades in those cities. To find out that what facades’ directions are optimal, Figure 7B shows where temperature losses are as a function of azimuth. It can be seen that the optimum facades are oriented towards the north, while those oriented towards the west have the highest losses. The reason for this is that after noontime, the maximum ambient temperature is reached, when the sun is to the west, so generators that point in that direction achieve greater heat.

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A. Temperature losses for surfaces facing south

B. Temperature losses for facades

Leticia (Tm=26ºC)

Pasto (Tm=13ºC)

Bogotá (Tm=12ºC)

Cúcuta (Tm=27ºC)

Barranquilla (Tm=28ºC)

San Andrés (Tm=28ºC)

Manizales (Tm=17ºC)

Popayán (Tm=19ºC)

Pasto

Bogotá

Cúcuta

Barranquilla

San Andrés

8

12

Temperature losses (%)

10

Angular losses (%)

Leticia

8 6 4 2 0

6 4 2 0 -2

-2 -4

-4

Tilt (º)

Azimuth (º)

Fig. 7. Annual temperature losses versus azimuth angle for: A. surfaces facing south. B. Facades

Temperature losses (%)

Figure 8 shows a mathematical relationship between the average room temperature of the place and the maximum temperature losses. This represents the data for generators with a low slope. 11 10 9 8 7 6 5 4 3 2 1 0

10

13

16

19 22 25 Average ambient temperature (ºC)

28

Fig. 8. Maximum losses for annual temperature according to the average ambient temperature. By making linear regression points establish a relationship of type (R 2 = 0.96): Equation 5 Ltemperatura, MAX (Ta ) = 0.493Ta - 4.405

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This expression means that for every 2 ° increase in average ambient temperature of the place, the maximum losses increase by about 1%. From this equation, we conclude that for a city of 9 ° C average temperature, there won’t be losses regarding this concept. Thus, one could find the relationship between the maximum equivalent operation temperature and ambient temperature: TOEmax = 1.12Ta + 15 Equation 6

LOSSES IN THE INVERTER Values ​​losses DC-AC conversion was shown in Table 3. There you can see that its minimum value is approximately 11%, which is explained by the choice of parameters k 0 , K 1 Y k 2 that characterize the inverter efficiency curve. Moreover, the maximum is between 19% and 22%. This indicates that the greater the slope of the surfaces, the greater the losses, which increase to become twice. Figure 9 shows the above behavior on surfaces facing south. Between β = 0 ° to β = 40 ° we can see that the loss of DC-AC conversion are approximately constant (11%). After this slope they tend to grow to 15% or 20%, depending on latitude. You can also see that San Andres has fewer losses than Leticia, due to the amount of solar radiation received by these surfaces that increase according to the latitude. Therefore, there will be greater powers to the inverter input, and greater efficiency. Table 3. Minimum and maximum DC-AC conversion losses in every city in Colombia. Ciudad

Temperatura media Ta (°C)

Pérdidas mínimas

Pérdidas máximas

Leticia

-4.2

11.1%

19.9%

Pasto

1.2

11.2%

18.8%

Tumaco

1.8

10.1%

19.1%

Popayán

2.5

11.0%

18.9%

Neiva

3

11.2%

19.9%

Cali

3.6

11.4%

20.0%

Villavicencio

4.2

11.2%

20.4%

Bogotá

4.7

10.8%

18.6%

Manizales

5.1

11.0

19.1%

Medellín

6.2

11.0

19.8%

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Barrancabermeja

0.5

10.8%

20.0%

Cúcuta

7.9

10.9%

20.4%

Montería

8.8

10.9%

20.8%

Valledupar

10.5

10.7%

21.3%

Barranquilla

10.9

10.8%

21.4%

San Andrés

12.6

10.7%

21.9%

The behavior observed in the losses in the inverter (Figure 9) is similar to that exhibited by the angular losses (Figure 5.a). This is explained by the fact of a strong dependence of the efficiency of the inverter as a function of the input power. According to this, for the north facing surfaces, losses grow with increasing tilt, in particular to values ​​between 19% and 22%. Similarly, the facades facing east or west have lower losses than the rest, which is about 13%. Leticia (φ = —4.2º )

Pasto (φ = 1.2º)

Bogotá (φ = 4.7º)

Cúcuta (φ = 7.9º)

Barranquilla (φ = 10.9º)

San Andrés (φ = 10.9º)

25

Losses in the inverter (%)

20

15

10

5

0 0

10

20

30

40

Tilt (º)

50

60

70

80

90

Fig. 9. Annual conversion losses against the inclination for south-facing surfaces.

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SYSTEM PERFORMANCE The minimum and maximum PR values ​​obtained for each city are shown in Table 4. The values ​​range between 0.51 and 0.65. Overall, the range of variation was greater than 20%, while within each city it can be up to 15%. These results are against the usual practice; by always assign the same “standard” value of PR on different locations or types of surfaces. Table 4. Minimum and maximum Ratio performance in every city in Colombia. The data represent the behavior of an “average” photovoltaic system. City

Average Ta (°C)

PR min

PR max

Bogota

11,7

0,58

0,650

Pasto

13,3

0,58

0,646

Manizales

16,6

0,56

0,635

Popayán

18,8

0,56

0,628

Medellín

22,3

0,54

0,618

Cali

24,4

0,54

0,611

Tumaco

26,2

0,54

0,608

Villavicencio

26,2

0,54

0,606

Leticia

26,3

0,54

0,606

Cúcuta

27,2

0,53

0,604

Barrancabermeja

27,6

0,53

0,602

San Andrés

27,6

0,51

0,599

Neiva

27,7

0,54

0,602

Montería

27,9

0,53

0,601

Barranquilla

28,3

0,52

0,599

Valledupar

29

0,51

0,597

Mainly, the maximum PR strongly depends on the average ambient temperature of the place in a decreasing watt. This trend is best seen in the regression line of Figure 10, with a degree of adjustment R2 = 0.9967.

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0.660 0.650

PR max

0.640 0.630 R2 = 0,9967

0.620 0.610 0.600 0.590 0.580 10

13

16

19

22

25

28

31

Average temperature (º) Fig. 10. Annual maximum system performance against the average room temperature. The equation of the straight line representing the data is: PRmax = 0,686 - 0,0031 • Ta Equation 7 This expression is useful because it allows evaluating the maximum system performance by just having the ambient temperature of the city. The results obtained in this model corresponds to a SFCR average on fixed surfaces. However, it is possible to obtain higher values ​​for PR assuming that the system is well designed. With this, equation [7] may be simplified for practical purposes, introducing a constant ksyst that depends on the type of system as well: PRmax = ksist • [1 + γ (1,12 • Ta — 10)] Equation 8 Where Ta is the average ambient temperature in ° C City, and γ is the coefficient of variation of the maximum power point with temperature. For crystalline silicon may be used γ = -0.0044 ° C -1. This equation reproduced the results obtained with very high accuracy (R2 = 0.992). In order to test the validity of this expression in the equatorial countries, the term temperature was isolated and losses for near-optimal inclinations were calculated as follows: Ltemperatura, max = — γ (1,12 • Ta — 10) Equation 9 The results obtained for some monitored real systems are shown in Table 5. Thus, the ​​reported values are consistent with those reported for photovoltaic systems installed in homes, so the expression [9] has universal validity. However, it is important to stress that these losses could be greater in the case of BIPV, if the final design does not take into account adequate ventilation of the modules.

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Table 5. Exchange losses for inclined temperature near its optimum angle generators. City

Country

Number of Systems

Accounted losses

Reference

Estimated losses

Tokyo

Japan

100

4%

(41)

4%

Dublin

Ireland

1

0%

(44)

0%

Sukatani

Indonesia

101

8%

(45)

8%

Figure 11 shows the possible range of maximum system performance by the city. Here we can see that an optimal system can reach values ​​of PR comprised between 0.74 and 0.81, it depends on the type of city. Bogota is the city that best could play the hypothetical system (PRmax = 0.81), whereas in Valledupar performance would be lower (PRmax = 0.74). However, to calculate annual energy produced in the optimal location we should use the values of ​​ an “average” system. Maximum PR for each city

0.85

Average System

0.80

PR max

0.75 0.70 0.65 0.60 0.55

Valledupar

Barranquilla

Montería

Neiva

San Andrés

Barrancabe

Cúcuta

Leticia

Villavicencio

Tumaco

Cali

Medellín

Popayán

Manizales

Pasto

Bogotá

0.50

Fig. 11. The maximum annual performance of a system, for each city depending on the type of system. PR VARIATION OF THE INCLINATION AND ORIENTATION It is important to remember that equation [8] is used to calculate the PR in the case of inclinations and directions near the optimum one. However, this value can decrease by up to 15% depending on the type of surface on which the modules are located. This would imply a corresponding error in the calculation of the annual energy produced. This can occur when it comes to building integration (BIPV). Given this, the PR contour maps were constructed based on the orientation and tilt of each city. Some of the results are shown for the cities of Leticia (Fig. 12), Pasto (Fig. 13), Bogotá (Fig. 14), Cúcuta (Fig. 15), and Barranquilla (Fig. 16).

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PR for Pasto

Tilt (ยบ)

Tilt (ยบ)

PR for Leticia

Azimuth (ยบ)

Azimuth (ยบ)

Fig. 12.

Fig. 13.

PR for Bogotรก

Tilt (ยบ)

Tilt (ยบ)

PR for Cรบcuta

Azimuth (ยบ)

Azimuth (ยบ)

Fig. 14.

Fig. 15.

Tilt (ยบ)

PR for Barranquilla

Azimuth (ยบ)

Fig. 16. 108

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In Figures 12-16, it can be seen that all surfaces with inclinations, under 30 °, regardless of their orientation, have a PR approximately equal to the maximum of that city. This implies that for all ceilings, PR=PRmax can be taken. You may also notice that in all the graphs, there are two peaks performance for guidance approximate -90 ° and 90 °. This is because both the angular conversion losses are minimal for surfaces oriented westward and eastward. It is also seen that the peak of PR in α = -90 ° is greater than for α = 90 °. The reason for this is that in the mornings, when the sun is in that direction, the ambient temperature is lower, resulting in a minor loss for this item. Moreover, in cities located in negative latitudes, lower yields are observed for south-facing vertical generators (α = 0 °). The opposite is observed in cities located above the Ecuador line (α = 180 °). These behaviors are due to the high angular losses and inverter in such cases. SIMPLIFIED MODEL FOR PR CALCULATION All graphs obtained in this article are useful to make a detailed study of losses in the future photovoltaic building. In particular, the contour maps presented in Figures 12-16 allow to visually identify the PR system. But every city has a different contour map, characterized by its average ambient temperature and latitude. Therefore, it would be necessary to use a long procedure each time you want to predict the behavior of a system. Actually, it is not technically feasible when a photovoltaic project is proposed. This is the reason why many designers choose to assign a “standard value” of 0.75 when you want to predict the energy produced. But as shown above, the values ​​obtained from the PR may vary depending on the city and the type of surface, so that this practice could induce an error above 45% in calculating the annual electricity, in the worst case scenario. As a proposal to solve the problem, it was thought to find an equation that would fit the contour maps obtained. If we think this way, we found that, for the same PR, the dotted curve describes an approximate of a sum of two Gaussian functions. The breadth and width of these functions vary with the latitude. Furthermore, the values ​​obtained for the PR in each curve are characteristic of the average temperature of the place. Accordingly, the following model for the calculation of PR is proposed: α+90 2 α-α 2 Equation 10 PR = 0,0011 A1 • e -2 ( W 0 ) + A2 • e -2 ( W ) ― β ― 50 + 1,117 • PRc

Where: Equation 11 A1 = ― 1,1• |φ|+ 60 Equation 12 A2 = ― 0,1• |φ|+ 65 Equation 13 W = ― 1,1• φ + 92 Equation 14 α0 = ― 1,4• |φ|+ 92 Equation 15 PRc = PRmax + 0,0006 • Ta ― 0,017

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β being the angle of inclination,α is the azimuth angle and Ф the latitude of the city, all in degrees. Ta to is the average ambient temperature in ° C in the City. The procedure for using the equation [10] is: a. The PRc value was calculated according to equation [15]. b. PR is calculated by the equation [10]. If PR > PRc then it is taken as the performance value PR = PRc. Otherwise we keep the figure just obtained. Thus, an expression that needs only 4 input parameters are obtained. Two of them belong to the city where the photovoltaic system will be installed: The average ambient temperature Ta, and the latitude Ф. The other two characterize the surface of the Generator type. Its angle of inclination β, and orientation α. DEGREE OF ACCURACY OF THE MODEL In an effort to verify the degree of accuracy of the model, Figure 17, was made where two contour plots of PR are shown. One made by the long and tedious process described in the methodology, and the other calculated by the proposed equation, both for the city of Bogota. PR calculated with model

Tilt (º)

PR for Bogotá

Azimuth (º)

Azimuth (º)

Fig. 17. Contours of the PR to Bogota, calculated using the complete simulation (left) and by the proposed model (right). Likewise, Figure 18 shows the error rate at each point on the graph. It can be seen as in most of the diagram the mistake is less than 1%. This error increases slightly with temperature and latitude. For example, to Tegucigalpa Guatemala (φ = 14.1); the error in most of the points is 3%. These results indicate the excellent degree of accuracy of the proposed model, despite its simplicity. Finally, to get an idea of ​​the work saved by using the equation [10] for calculating PR, the following is described below: To find each point of the contour diagram on the left side of Figure 17, it was necessary to use more than 40 equations in a computer algorithm that performed more than 20,000 operations. In contrary, each point on the graph on the right of the same figure, only two equations were used: the PRmax, Equation [8], and the proposal to our model, equation [10].

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Tilt (ยบ)

Error of the model for Bogotรก

Azimuth (ยบ)

Fig. 18. Percentage of error in the proposed model for the PR Bogota

CONCLUSIONS This work has compiled great contributions to the design of photovoltaic buildings. Firstly, a technical regulation was proposed worldwide, which allow us to easily calculate the maximum allowable energy loss by orientation and shading. The procedure can be used anywhere in the world, just by knowing the fraction of diffuse and maximum solar radiation. This will take into account the energy efficiency of the photovoltaic system at the stage of architectural design of the building. There were also analyzed in detail the possible energy losses that affect the performance of a photovoltaic grid connected system. The procedure was done for 16 cities in Colombia, in all possible orientations and inclinations of the plane of the generator. In addition, an equation was proposed to calculate the maximum temperature losses anywhere in the world. This expression was validated using actual data monitored systems. The second major contribution is a simple and validated prediction model of the energy produced by a photovoltaic system for low-latitude countries. This model is of great value worldwide, as it avoids the use more than 40 equations on an algorithm that performed more than 20,000 operations. The input variables are only four: the temperature, the latitude of the city, and the orientation and tilt of the PV generator. This research is a valuable support for architects and engineers in Latin America, since it considerably facilitates the design of a photovoltaic building. This contributes to the development of Building Integrated Photovoltaic (BIPV) in the region and the construction of environmentally sustainable cities.

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REFERENCES Pearce JM. Photovoltaics — a path to sustainable futures. Futures. septiembre de 2002;34(7):663-74. Kanters J, Horvat M. Solar Energy as a Design Parameter in Urban Planning. Energy Procedia. 2012;30:114352. Gobierno de España. Código Técnico de la Edificación – HE5 Ahorro de Energía – Contribución Fotovoltaica Mínima de Energía Eléctrica. 2009. G EC, E JD. El Sector Solar Fotovoltaico en el Caribe Colombiano: Análisis Técnico y de Mercado. Sci Tech. 1 de agosto de 2012;2(51):87-91. Rodríguez Murcia H. Desarrollo de la energía solar en Colombia y sus perspectivas. Rev Ing. noviembre de 2008;(28):83-9. The International Electro technical Commission (IEC). IEC 61724 - 1998, Photovoltaic system performance monitoring - Guidelines for measurement, data exchange and analysis. Multiple. Distributed through American National Standards Institute; 1998. Thomas R. Photovoltaics and Architecture. London: Taylor & Francis; 2012. 169 p. Roberts S, Guariento N. Building Integrated Photovoltaics: A Handbook. Switzerland: Springer; 2009. 175 p. Osterwald CR. Translation of device performance measurements to reference conditions. Sol Cells. septiembre de 1986;18(3–4):269-79. Araujo GL, Sánchez E, Martí M. Determination of the two-exponential solar cell equation parameters from empirical data. Sol Cells. enero de 1982;5(2):199-204. Green MA. Solar Cells: Operating Principles, Technology and System Applications. University of New South Wales; 1998. 274 p. Almonacid F, Rus C, Pérez PJ, Hontoria L. Estimation of the energy of a PV generator using artificial neural network. Renew Energy. diciembre de 2009;34(12):2743-50. Rodrigo P, Rus C, Almonacid F, Pérez-Higueras PJ, Almonacid G. A new method for estimating angular, spectral and low irradiance losses in photovoltaic systems using an artificial neural network model in combination with the Osterwald model. Sol Energy Mater Sol Cells. enero de 2012;96:186-94. Markvart T, Castaner L. Practical Handbook of Photovoltaics: Fundamentals and Applications. Elsevier; 2003. 1012 p. Mondol JD, Yohanis Y, Smyth M, Norton B. Long term performance analysis of a grid connected photovoltaic system in Northern Ireland. Energy Convers Manag. noviembre de 2006;47(18–19):2925-47. Senado de la República de Colombia. Ley 855 de 2003. Definición de Zonas No Interconectadas al SIN. Ley 855 de 2003 dic 18, 2003.

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Sancho Ávila JM, Riesco Martín J, Jiménez Alonso C, Sánchez de Cos Escuin MC, Montero Cadalso J, López Bartolomé M. Atlas de Radiación Solar en España [Internet]. Red Radiométrica Nacional, AEMET; 2012 [citado 1 de octubre de 2013]. Recuperado a partir de: http://www.humboldt.edu/moved/account-moved. php?old=ccat&new=ccat Agencia Estatal de Meteorología - AEMET. Gobierno de España [Internet]. http://www.aemet.es. 2013 [citado 1 de octubre de 2013]. Recuperado a partir de: http://www.aemet.es Government of Canada NRC. RETScreen International. [Internet]. http://www.retscreen.net/. 2013 [citado 24 de agosto de 2013]. Recuperado a partir de: http://www.retscreen.net/ Organización Meteorológica Mundial. http://wwis.aemet.es/. 2013. Liu BYH, Jordan RC. The interrelationship and characteristic distribution of direct, diffuse and total solar radiation. Sol Energy. julio de 1960;4(3):1-19. Page J. The estimation of monthly ea values of daily total short wave radiation on vertical and inclined surfaces from sunshine records for latitudes 40°N–40°S. Proc UN Conf New Sources Energy. 1961;4(598):378-90. Luque A, Hegedus S. Handbook of Photovoltaic Science and Engineering. 2.a ed. United Kingdom: Wiley; 2011. 1166 p. Spencer JW. Fourier series representation of the position of the sun. Search. 1971;2(5):172. Collares-Pereira M, Rabl A. The average distribution of solar radiation-correlations between diffuse and hemispherical and between daily and hourly insolation values. Sol Energy. 1979;22(2):155-64. Haberlin H. Photovoltaics: System Design and Practice. United Kingdom: John Wiley & Sons; 2012. 957 p. Hay JE. Study of Shortwave Radiation on Non-horizontal Surfaces. Atmospheric Environ Serv. 1979;Report No 79-12. Denegri MJ, Raichijk C, Gallegos HG. Evaluación de diferentes modelos utilizados para la estimación de la radiación fotosintéticamente activa en planos inclinados. Av En Energ Renov Medio Ambiente. 2012;16:915. Noorian AM, Moradi I, Kamali GA. Evaluation of 12 models to estimate hourly diffuse irradiation on inclined surfaces. Renew Energy. junio de 2008;33(6):1406-12. Diez-Mediavilla M, de Miguel A, Bilbao J. Measurement and comparison of diffuse solar irradiance models on inclined surfaces in Valladolid (Spain). Energy Convers Manag. agosto de 2005;46(13–14):2075-92. Souza AP de, Escobedo JF. Estimates of hourly diffuse radiation on tilted surfaces in Southeast of Brazil. Int J Renew Energy Res IJRER. 19 de marzo de 2013;3(1):207-21. Preu, R. PV-module reflection losses: Measurement, simulation and influence on energy yield and performance ratio. Thirteenth European Photovoltaic Solar Energy Conference 1995 Proceedings Vol2. 1995. p. 1465-8.

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Krauter S, Grunow P. Optical Modelling and Simulation of PV Module Encapsulation to Improve Structure and Material Properties for Maximum Energy Yield. Conference Record of the 2006 IEEE 4th World Conference on Photovoltaic Energy Conversion. 2006. p. 2133-7. Engineers AS of H, Refrigerating and Air-Conditioning. ASHRAE Standard Methods of Testing to Determine the Thermal Performance of Solar Collectors. ASHRAE; 1978. 56 p. Martin N, Ruiz JM. Calculation of the PV modules angular losses under field conditions by means of an analytical model. Sol Energy Mater Sol Cells. 1 de diciembre de 2001;70(1):25-38. Zang J, Wang Y. Analysis of Computation Model of Particle Deposition on Transmittance for Photovoltaic Panels. Energy Procedia. 2011;12:554-9. Caamaño Martín E. Edificios fotovoltaicos conectados a la red eléctrica: caracterización y análisis [Internet] [phd]. E.T.S.I. Telecomunicación (UPM); 1998 [citado 24 de noviembre de 2013]. Recuperado a partir de: http://oa.upm.es/1322/ Jantsch M, Schmidt H, Schmid. Results on the concerted action on power conditioning and control. 11th European photovoltaic Solar Energy Conference. Montreux; 1992. p. 1589-92. Almonacid F, Rus C, Pérez-Higueras P, Hontoria L. Calculation of the energy provided by a PV generator. Comparative study: Conventional methods vs. artificial neural networks. Energy. enero de 2011;36(1):37584. Baltus CWA, Eikelboom JA, Zolingen RJCV. Analytical Monitoring of Losses in PV Systems. 14th European Photovoltaic Solar Energy Conference. Barcelona; 1997. Sugiura T, Yamada T, Nakamura H, Umeya M, Sakuta K, Kurokawa K. Measurements, analyses and evaluation of residential PV systems by Japanese monitoring program. Sol Energy Mater Sol Cells. 1 de febrero de 2003;75(3–4):767-79. Alonso-Abella M, Chenlo F. A model for energy production estimation of PV grid connected systems based on energetic losses and experimental data on site diagnosis. 19th European Photovoltaic Solar Energy Conference. Paris; 2004. p. 2447-50. INSTITUTO GEOGRÁFICO AGUSTÍN CODAZZI - IGAC. http://www.igac.gov.co/igac. 2013. Ayompe LM, Duffy A, McCormack SJ, Conlon M. Measured performance of a 1.72 kW rooftop grid connected photovoltaic system in Ireland. Energy Convers Manag. febrero de 2011;52(2):816-25. Reinders AHME, Pramusito, Sudradjat A, van Dijk VAP, Mulyadi R, Turkenburg WC. Sukatani revisited: on the performance of nine-year-old solar home systems and street lighting systems in Indonesia. Renew Sustain Energy Rev. marzo de 1999;3(1):1-47.

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MEXICO DEVELOPMENT OF AN AUTONOMOUS MOBILE EMERGENCY SYSTEM FOR WATER PURIFICATION DULCE KRISTAL BECERRA PANIAGUA Advisor: Joel Pantoja Enriquez

SUMMARY A large portion of the world’s population lacks access to clean water. In Mexico and in developing countries, purified water scarcity is a serious issue that undermines thousands of people in rural and disaster areas, causing diseases transmitted by unsafe water consumption, as well as infant mortality. This paper presents the design, construction and evaluation of an autonomous system to purify H2O. The unit consists of a photovoltaic system that supplies the energy necessary for purification, adsorption filters, semipermeable membrane, germicidal lamp, ion exchange filters, clarification and chlorination steps. The prototype was tested with different types of raw water to determine the chemical and microbiological parameters through quality analysis of different areas of Chiapas, where the purifier can be implemented. The results of the analysis indicate that the prototype is able to completely eliminate and remove contaminants in natural waters and get water that meets the guidelines that of the Mexican Official Standards for purified water. This portable device is designed to produce drinking water, from groundwater, surface water with heavy metals, in places where there is no access to the electricity grid. This research helps to solve problems in marginalized communities and natural disaster areas where they do not have access to purified water, electricity and suffer from waterborne diseases. KEYWORDS: Purified water, autonomous system, photovoltaic system.

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INTRODUCTION The shortage of clean water in the world is a serious issue that adversely affects thousands of people in rural and disaster areas. Latin America, a region rich in water resources, has almost 31% of the fresh water in the world, furthermore access to potable and purified water is insufficient and their quality is inadequate (UNICEF, 2006). That is a negative impact on public health. The limited financial capacity of the bodies responsible for providing these services and the weak institutions of the sector, are factors which limit the possibilities for improving access, quality of water and sanitation on the continent. According to the “ WHO and UNICEF joint monitoring programme for water supply and sanitation�, in 2004, 50 million people or 9% of the population of Latin America and the Caribbean did not have access to a clean water sources and 125 million people or 23% lacked access to adequate sanitation. In Central American countries, 80% of infectious diseases, gastrointestinal parasites and a third of the mortality rate is due to the consumption of unsafe water (WHO, 2006). About 1.4 of children under five, die every day in Latin America, victims of diarrheal diseases associated with lack of access to water for human consumption (UNICEF, 2006). Mexico is one of the Latin American countries with a larger supply of fresh water in the world; similarly almost 0.1% of drinking water and electricity are inefficient for the population, especially for marginalized communities. This is added to the many destructive natural hazards such as tropical storms, hurricanes and seismic activity to which it is exposed. About 22 million Mexicans lack access to safe water. Of that amount, 9 million people lack drinking and purified water; about 13 million people living in rural or urban areas, despite having the service, have received contaminated water in their homes (CONAGUA, 2010). The South-east is the region of the country where water safety is less developed, including the states of Chiapas, Oaxaca, Tabasco and State of Mexico, among others, presenting an increased risk of diseases transmitted by consumption of water that does not meet the standards of the Mexican Official Standards of purified drinking water. Chiapas is the State with the largest amount of fresh surface water across the country. Despite that, less than 50% of the population can stock up on drinking water (INEGI, 2012). Most rural communities of the State do not have access to clean water, electricity and suffer from waterborne diseases, and children are the most affected population with high mortality rates. From this, new concepts of purification systems in combination with renewable energy sources have been developed to meet the needs of clean water in places where they have no access to this service and the grid, using surface water supply, salt and ground water to solve this problem. Innovations in the development of new technologies using a variety of artificial and natural materials have applications in water treatment, helping to improve their quality and use. As they have shown to have strong antimicrobial properties for water disinfection and removal of physicochemical pollutants. These materials include mineral resins, activated carbons, and polymeric resins, among others. In order to contribute to solving the problems above mentioned, the aim of this study was to design, construct and evaluate a self-sufficient /purifying device to treat natural ground and surface waters with different chemical and microbiological contaminants at high concentrations, which also uses solar energy as a power, supply to operate the portable prototype. It should be noted that the prototype was made in order to be implemented in rural areas and disaster, contributing to public health, with an extensive range of contaminant removal that is useful to any region of Mexico and Latin America.

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METHODOLOGY PROTOTYPE DESIGN. Selection criteria of the stages of self-purification For the design of the purification steps in the prototype, the main chemical and microbiological contaminants were identified in most surface water and groundwater in the Southeast region provided by CONAGUA that can be commonly found in rivers, streams, springs and aquifers of Latin America (WHO, 2006). Based on the results it was possible to determine and select the stages with their respective filters and more convenient chemicals for the purification system and achieve a suitable drinking water that met the guidelines by the 041 and 127 Mexican Official Standards for purified water and 201 for drinking water. Table 1. Main physicochemical and microbiological contaminants present in natural waters in the southeast region of Mexico. Contaminant Type Microbiological

Concentration range

Total Coliforms

2000-180,000 NMP/ 100 ml

Faecal Coliforms

1500-50,000 NMP/ 100 ml

Physicochemical

Concentration range

Total alkalinity

200-600 ppm

Total Hardness

150-700 ppm

Total Dissolved Solids

200 -900 ppm

Sulfates

10-50 ppm

Chlorides

10-30 ppm

Heavy metals

Concentration range

Mercury

10.0-30.0 ppm

Lead

10.0-50.0 ppm

Arsenic

10.0-30.0 ppm

Chrome

10-30 ppm

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Copper

15-30 ppm

Manganese

50-900 ppm

Iron

100-1300 ppm

Surface waters may contain a variety of organic and inorganic materials, the particle size of these materials and their nature determine the types of treatments used in the purification processes. The first stage selected to start the process comprises in a clarification, consisting mainly of two sub-stages, coagulation and flocculation, in order to remove very small and suspended insoluble particles called colloids, which are the main cause of turbidity as well as color of raw water. Coagulation aims to destabilize suspended particles, i.e. facilitate agglomeration. Flocculation seeks to help the slow mixing of the contact between the destabilized particles. These particles coalesce to form a flock, which can be readily removed by decantation and filtration procedures (Cardenas et al, 2000). Disinfection was subsequently selected for clarification, with the aim of destroying bacteria, viruses and pathogenic microorganisms, which usually contain surface water at very high concentrations stage. It also allow oxidation of some metals such as iron, manganese and hydrogen sulfide that are the cause of odors and flavors (Ramirez et al, 2001). The third stage comprises the selected process is an adsorptive purification at this stage were used to remove soluble solids from the water. These solids are polymeric materials and activated carbon with the capacity to adsorb, retain particles, organic matter and sediment, remove chlorine, taste and odor, bacteria, heavy metals such as iron and manganese (Toledo et al, 1997). The fourth step consists of an ion exchange. This operation includes the exchange between the inorganic contaminant ions in the water and harmless ions of a solid loaded on its surface (Godos et al, 2004); solid natural resins were used composed of synthetic and aluminum silicates. The fifth step consists of a reverse osmosis operation. We selected it with the aim to control and eliminate inorganic chemicals (excess of salts, metals, and minerals), most microorganisms and chemicals such as nitrates, sulfates, pesticides and herbicides. To complete the purification process it was decided to choose a radiation of ultraviolet light on water stream in order to destroy microorganisms, viruses and bacteria, mainly fecal coliforms as Escherichia coli (Diaz et al, 2008). The water flow at the end of the purification process is stored in a tank with a capacity of about 12 liters under pressure conditions. The following diagram shows the stages and processes implemented and selected for the autonomous H2O purifier.

1. Clarification Coagulation - Flocculation

6. Ultraviolet Light UV

2. Disinfection / Chlorination

5. Reverse Osmosis

3. Adsorption

4. Ionic Exchange

7. Storage

Figure 1. Outline the steps and processes that constitute the purification system in the prototype.

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Dimensioning of the autonomous photovoltaic system The purpose of the photovoltaic dimensioning was to calculate system’s elements (mainly number of panels, batteries, inverters and controllers) needed to reliably supply to the purification system with the electrical power needed to operate and achieve a completely autonomous system. The calculations for sizing photovoltaic were performed by following these steps: 1. Orientation and tilt of solar panels. As the orientation of a solar panel is defined by the azimuth angle α or angle normal to the surface coincides with the Ecuador of the observer, α = 0 (south in the northern hemisphere and north in the southern hemisphere) and it is the orientation that is more uses the sun’s radiation throughout the year. Mexico is in the northern hemisphere so the panels are oriented to the south. The inclination of the panel was in relation to the latitude of the place where they were installed. The location was Tuxtla Gutierrez and is located in the following coordinates: 16 ° 38 ‘and 16 ° 51’ north latitude (INEGI, 2012), therefore, 16 ° inclination was considered adequate. 2. Calculation of energy demand. The daily energy demand required by the purifier was determined according to the power and energy consumption in alternating and direct power of the equipment that work with electricity in the current system, assessing the various losses in the world. The energy consumed by computers in AC were determined using the following formula: EAC = ∑ P(AC)i • tdi

Equation 1

Table 2. Equipment working with electricity in the purification system. Equipment

P(AC)i (Watts)

tdi (hours)

AC COnsumption per day (Wh/day)

1 UV lamp

32.8

10

P(AC)1 • td1

1 1/10 hp pump

14

10

P(AC)2 • td2

Total PAC system power:

P(AC)1 + P(AC)2

AC Energy consumption (EAC):

P(AC)1 • td1+P(AC)2 • td2

EAC is the theoretical power consumption without considering the yields of existing stages. To calculate the total actual consumption, efficiencies of inverter and battery with the following formula were evaluated: (E ) ET = AC ηInv

Equation 2

Inverter efficiency was 95%. The required total energy (ET) was the daily consumption between the germicidal lamp and pump.

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For the selection of the solar time peak, average HSP was considered for Tuxtla Gutierrez with approximately 4.7 kWh / m2 (Almanza, 1997), although for calculation it was take a value of 5 kWh / m2 . 3. Selecting range runtime. Range runtime chosen was N = 7 days, during which the solar panels will not capture solar energy due to unfavorable conditions and all consumption will be supplied from energy reserved in the batteries. 4. Number of panels. Once the energy demand and the HSP were certain, we continued to calculate the number of panels to be installed from the following formula: ET NPT = PGPP HPS

Equation 3

The peak power of the panel (PP ) was 90 Wp and overall loss factor (PG ) was 75%. The operation gave us the number of panels the purifier needed with a capacity of 90 Wp. 5. Determining the battery capacity. The battery capacity can be given in Wh or Ah, the formula used to determine it were: E•N Cn(Wh) = T Pd

Equation 4

C (Wh) Cn(Ah) = n Vbat

Equation 5

The maximum depth of battery discharge (Pd ) was 1.2 with a nominal battery voltage (Vbat ) 6 V. 6. Determining the ability of the controller. The purpose of calculating the capacity of the controller was to obtain maximum current flowing in the installation. For this, it was calculated the current produced in the panels, the current consuming loads and based on the maximum of these streams was endured by the controller. The current intensity produced by the panels was determined with the following formula: IG = IR • NR

Equation 6

The intensity consuming loads were determined considering all the powers of the equipment with the formula shown below: P P IC = DC + AC Vbat 220

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Equation 7

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Of these two currents (IG , IC ), the highest of the two was that supported by the regulator which was used for the selection: IR = max (IG , IC)

Equation 8

7. Determining the ability of the controller. For the capacity of the inverter, the selection was made according to the power demanded by the AC load. An investor was chosen whose nominal power was slightly higher than the maximum demanded by the load. This expression allowed us to know the power of the controller (Abella, 2005): PINV ≈ PAC • ηF

Equation 9

Design in Solid Works The design of the prototype was made in Solid Works software. It had to do with the drafting of the pieces of the storage unit where the photovoltaic and purification system was installed. The device was crafted on the basis of panel dimensions that supports its surface. These measures are 90 cm long and 70 cm wide. A 110 cm height was taken to make the device. Figure 2 shows the main components of the prototype are observed. It consists of a photovoltaic panel (1) which is responsible for converting solar energy into electricity, a box for batteries to prolong their useful life (2) these are responsible for store energy and give backup time for a week, support for filters and photovoltaic equipment (3) key for the supply of purified water (4) a moveable cart that supports the device, making the prototype portable, accessible and practical (5).

Figure 2. Internal view of the purifier where major components can be observed.

Figure 3. Front view of the autonomous H2O purifier in Solid Works.

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CONSTRUCTION OF THE PROTOTYPE. It was carried out the construction of the storage unit based on the design that was made in the Solid Works program. The most suitable materials for manufacturing furniture construction were: Stainless steel: for its high level of resistance to both corrosion and other atmospheric agents and wear. Its major disadvantage is the high construction cost. Bonding sheet: bonding has to do with the treatment of the galvanized sheet, giving similar to the properties of stainless steel, and thanks to a protective layer on its surface corrosion. The building cost is lower than that of stainless steel but higher than that of normal galvanized sheet. Carbon steel: the chemical composition of this material is a mixture of iron and carbon steel iron when exposed to air it oxidizes and becomes less resistant. Its major advantage is the low building cost. Considering the characteristics and properties of these materials, we chose the bonding sheet 24 caliber as the most suitable for the construction material, due to its low cost and resistance to corrosion and wear, suitable for use in drastic environments prototype for storms, rain sun and wind. The storage unit underwent a sanding and glazed operation with anti-corrosive paint. Once it was built, we continued to integrate the purification system and photovoltaic equipment. EVALUATION OF THE PROTOTYPE. For the evaluation of the prototype, purification tests were performed with raw water from different sources and removal of heavy metals. The steps in the evaluation of the prototype were: Natural water sampling technique The objective was to obtain a representative sample of water in drums of 60 and 50 liter and 2 liter sterilized jars. The surface water sample was collected from the Santo Domingo River located in the municipality of Chiapa de Corzo, Chiapas. The sample was obtained as far as possible from the riverbank, trying not to remove the ground and avoiding stagnant zones. Before storing it in cans and bottles, they were washed with the same formula in three consecutive times and completely immersed in the opposite direction to the flow of the river. The bottles were kept in cold water to later do a physicochemical and microbiological analysis. The groundwater sample was collected from a spring located in Ocozocoautla de Espinosa, Chiapas. The sample was purchased directly from the site of the outbreak of water. We wash the drums, jars with this water for three consecutive times before storing. The bottles were kept in cold water to later do a physicochemical and microbiological analysis. The used sample technique was simple and we followed the procedure referred to NOM-014-SSA1-1993. Experimental techniques. Pre-treatment of natural waters The water conditioning before entering the self-purifying device resulted in a clarification treatment to coagulate, flocculate and settle those suspended solids so they can be removed, and disinfection treatment was carried out to eliminate the amount of pathogenic microorganisms present in the water. The clarification treatment was performed by Jar Test technique and disinfectant procedure was made determining the optimal disinfection demand. The tests were carried out only with the river water due to the high concentration of suspended solids. The surface water sample was placed in the tank, resulting

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in a volume of 110 Lt in total and treatment of clarification, and disinfection was carried out before being filtered by the prototype. Jar testing is a technique used to determine the optimal conditions for purification treatments and water purification. It attempts to simulate the processes of coagulation, flocculation and sedimentation in the laboratory for removal of colloids and organic matter suspended in the water (Cardenas, 2000). To this end, a solution of 10% v / v aluminum sulfate coagulant was prepared and stored in a 100 ml volumetric flask. Six beakers were filled with the desired sample volume. In this case were 500 ml of water surface. We proceeded to measure the initial parameters as turbidity and pH. Different doses of the prepared coagulant solution were applied and placed in racks stirring at a speed of 100 rpm for one minute to each sample. The rapid mixing step helps disperse the coagulant through each container. The stirring speed was decreased to 25 rpm and allowed time of 15 min for flock formation. This slower mixing speed helps cause the formation of flocks by improving particle collisions that result in larger flocks. Finally, they stirring grills were turned off, and flocks were settle for 15 to 20 min. The parameter measured was the pH with indicators strips. • Optimum dose of coagulant. From the highest rate of formation of flock recorded in the sample, it was determined the optimal dose of the coagulant added. The following rule of three was calculated for the amount of coagulant required to treat a volume of 110 Lt River water. x ml of coagulant 500 ml of water

=

x ml of necessary coagulant 110,000 ml of water

Equation 10

For the amount of sodium hypochlorite to be used to treat the total volume of natural waters in the supply tank it was determined by the ratio of the volume of water treated and the dose of chlorine added to the sample in the laboratory that met with the right concentration of residual chlorine equal to 1.5 ppm according to the NOM-127-SSA1, ensuring complete disinfection and water purification. To this end, five beakers were filled with the desired sample volume; in this case 500 ml of plain water. We proceeded to measure the initial parameters such temperature, pH and chlorine. There were applied different doses of sodium hypochlorite of 0.01 to 0.1 ml to each sample. The samples were left in contact with the disinfectant for a time of 15 to 30 minutes. After that time, the concentration of free residual chlorine was measured by a digital Checker. Finally, the initial parameters were measured such as temperature and pH. • Optimal doses of disinfectant From the sample registering the suitable concentration of free residual chlorine, optimal disinfectant dose to be added was determined. With the following rule of three, it was calculated the amount of sodium hypochlorite required to treat a volume of 110 Lt natural water: x ml of disinfectant = 500 ml of natural water

x ml of necessary disinfectant 110,000 ml of natural water

Equation 11

Characterization techniques of natural and purified water The purpose of making the characterization of the waters was to determine the quality of the parameters and quantify their contaminants, through laboratory analysis level, allowing accurate results and compared with the maximum permissible levels present in the Mexican Official quality Standards of drinking and

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purified water. The water can be classified if whether or not it is fit for human consumption. Physicochemical and microbiological analyses were made to the raw water samples before treatment and filter through the purification system to compare the results of chemical and microbiological analysis of samples of purified water leaving the system, ensuring that operations are removing contaminants and eliminating their entry altogether. The main parameters that were measured in the chemical and microbiological analysis were as follows: Table 3. Classification of certain parameters of water quality. Measured parameters Microbiological

Physical (organoleptic)

Chemicals

Total Coliforms

Smell and taste

Free chlorine

Fecal Coliforms

Color

Chlorides

pH

Total alkalinity

Turbidity

Total Hardness

Total Dissolved Solids

Sulfates Heavy metals

Techniques for measuring total coliforms in crude and purified water were: Most Probable Number (MPN) and respectively colony forming units (CFU). The first is based on the ability of this microbial group to ferment lactose to acid and gas production when incubated at 35 ° C ± 1 ° C for 48 h, using a culture medium containing bile salts (NOM-112-SSA1 -1994). The second is based on filtration of a direct sample or an aliquot of the sample through a cellulose membrane, which retains the organisms, placing the membrane on either a selective culture medium of Lactose agar or a saturated absorbent pad with a Lactose liquid means (NMX-AA-102-SCFI-2006). The method for determining odor was to dilute a sample of natural water with odor-free water until a dilution having a minimum perceptible odor (NMX-AA-083-1982). The principle of the method used to determine color based on the measurement of true color and / or apparent in a raw water sample by visual comparison with a standard platinum-cobalt scale (platinumcobalt the unit is produced to dissolve a mg platinum / L as chloroplatinate ion). This method relies on the visual appreciation of color of the sample by the analyst compared with a standardized scale (NMX-AA045-SCFI-2001). There were used different methods of determining pH, ranging from the simple use of indicator paper to sophisticated methods using a pH meter. For the colorimetric method, FERMONT strip indicators were used to develop a range of colors for different pH (NMX-AA-008-SCFI-2000). The method for determining turbidity used is based on comparing the intensity of light scattered by the sample under defined conditions, the intensity of light scattered by a reference suspension under the same conditions, the higher the light scattering the greater the turbidity. The apparatus used in this determination consisted of a H1 93703 C HANNA nephelometer with light source to illuminate the sample and one or more photoelectric detectors with an external reading device to indicate the intensity of light scattered at 90 ° to the direction of incident light beam (NMX-AA-038-SCFI-2001). The principle of the method used to determine solids and total dissolved salts is based on the quantitative measurement of dissolved salts and solids and

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the amount of organic matter contained by evaporation and calcination of the filtered sample or, where appropriate, to Specific temperatures, wherein residues are heavy and provide a basis for calculating the content of these (NMX-AA-034-SCFI-2001). The DPD method was employed to determine the free residual chlorine which reacts instantly with a solid reagent weakly acidic DPD (NN, diethyl p-phenylenediamine), producing a complex pink by oxidation of chlorine, the intensity of this is proportional to the amount of free chlorine present in the sample (NMX-AA-108-SCFI-2001). The method for determining acidity and alkalinity in water is based on measuring the alkalinity in the water by means of an evaluation sample using as titrant alkali of known concentration (NMX-AA-036-SCFI-2001). The analysis method employed to determine hardness is based on complex formation by the disodium salt of ethylenediaminetetraacetic acid with calcium and magnesium ions. It consists of an assessment using a visual indicator Eriochrome T, which is red in the presence of calcium and magnesium and turns blue when they are complex or absent (NMX-AA-072-SCFI-2001). The determination of chloride consisted of a silver nitrate titration using as potassium chromate indicator. Silver reacted to chloride to form a chloride precipitate of white silver color (NMX-AA-073-SCFI-2001). To determine the sulfate ion, it was precipitated and weighed as barium sulfate after removing the silica and insoluble matter (NMX-AA-074-1981). Analytical techniques. Adsorption and ion exchange of heavy metals in resin It was studied the Ion exchange of Hg (II), Cd (II), Pb (II), Cu (II) and Zn (II) in aqueous solution on the mineral resin unmodified and modified by cation exchange with solutions of NaCl was studied, order to increase their capacity to trade, since the exchangeable ions are displaced by Na ions, which are more accessible to the exchange of the metal cations. To do this, several samples of mineral resin were washed using triple distilled water, separated by settling, dried for 24 hours in an H-33 Riossa oven at 110 ° C and kept in dry containers. For the experiments with unmodified mineral resin, the experimental data exchange isotherm were obtained on a batch exchanger consisted of a 500 ml Erlenmeyer flask. There were added 500 ml of a solution containing an initial concentration of the ion metal of 10 to 100 mg / L. In a mesh fabric made of nylon there were added 15 to 25 g of zeolite shaped filter, then placed into the solution of the exchanger. The solution was mixed using a magnetic stir bar and Teflon coating powered by a stir plate. The zeolite and the solution were left in contact until they reached equilibrium. It was found that over a period of 24 hours is sufficient for equilibrium. The concentrations of metal ions in aqueous solution were determined by optical emission spectroscopy technique. The principle on which this technique is based is as follows: the sample is sucked and atomized through a plasma, using a monochromator, a light beam through the plasma is directed and a detector the amount of light absorbed is measured. The absorption depends on the free unexcited atoms. As the wavelength of the light beam is a unique characteristic of each metal to be determined, light energy adsorbed by the analyte is a measure of the metal concentration in the sample. The concentration of any of these ions in a sample was determined by measuring absorbance on a spectrophotometer Optical Emission by Inductively Coupled Plasma (ICP-OES) Thermo Scientific iCAP 6000 SERIES, calculating its concentration by a calibration curve. The procedure to establish the calibration curve was to prepare standard solutions ten metals at concentrations of 10 to 100 mg / L and measure its absorbance. The modification of the mineral resin was performed by a cation exchange process, four 250 ml Erlenmeyer flasks 110 g of natural mineral resin were added to 200 ml of intercambiante NaCl solution 116 g / L. The resin solution and mineral were placed in a heating plate and heated to a temperature of 50 ° C for 12 h then allowed to cool within 12 h; the intercambiante solution was separated from the resin by decantation. Then, 200 ml of a new intercambiante solution were added and heated again at 50 ° C. This procedure was repeated for five days. At the end of this period, the resin was separated from the solution, washed repeatedly with triple distilled water until the rinse solution was no longer cloudy and dried in an oven at 110 ° C for 24 h. The modified resin was stored in a dry, sealed container. The experimental isotherm data were obtained following the same procedure of unmodified resin; to the same conditions.

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DEVELOPMENT. PHOTOVOLTAIC AUTONOMOUS SYSTEM. Given the methodology described in the previous section, the results of self-sizing, right photovoltaic system that will enable the prototype work without mains are exposed. Table 4 shows the power and energy consumption of the system, the panel peak power and capabilities of the battery, inverter and controller.

Table 4. Results of the autonomous photovoltaic dimensioning. 46.8 W

Total PAC system power : Theoretical energy consumption EAC :

468 Wh/dia

Actual power required by the purifier ET :

493 Wh/dĂ­a

Number of panels:

1

Number of batteries:

2

Number of controllers:

1

Number of inverters:

1

Peak power panel:

Battery capacity: Controller Capacity

Inverter capacity:

90 W/m2

420 Ah, 6 V 10 A

100 W

To date, the selected panel sheds power of 450-500 Wh/day, which completely satisfies the energy demand required by the prototype for 10 hours of operation. The rate of permeate from the membrane is 14 L/h, thus the work period of the prototype a total flow of 140 L/day is obtained, satisfying 70 people per day, in the case a person consumes approximately 2 L of water per day. If required for use for personal hygiene, it will satisfy a family of 4 to 5 people, if we considers that a person bathes with approximately 20 L of water per day, and the remaining water is for domestic use and consumption. The batteries were connected in series to ensure that the equipment works for a week (60% discharging to prolong its life); if the panel did not capture solar energy and there are, unfavorable conditions during the day and night, beneficiaries can obtain supplies of clean water for multiple uses. RESULTS OF PROTOTYPE CONSTRUCTION Alluding to sketch done in Solid Works software, we can see that certain construction materials and dimensions selected according to the conditions of use of the prototype are mentioned in the methodology, in Figure 4 we can see the schematic representation of the steps that were taken for the construction of the prototype:

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1. Design, construction and assembly of parts according to design.

2. Sanding and polish the furniture with anti-corrosive paint.

3. Integration of the photovoltaic panel on the prototype.

4. Purification system integration and photovoltaic equipment

Figure 4. Schematic representation of the construction of the prototype stages. In Figure 5, we can see the finished H2O autonomous purifier, made of 24 gauge-galvanized steel with treatment bonderized to withstand unfavorable conditions and the weight of the integrated equipment. Overall dimensions are: 110 cm x 90 cm x 70 cm (height x length x width) and relatively low of about 70 kg, with the aim that the prototype is portable and can be easily moved from place to place , to this, it has a wheeled-based platform with to support the device as a whole.

Figure 5. a) Front view of the working prototype, b) side view of the prototype. RESULTS OF CLARIFICATION AND DECONTAMINATION OF NATURAL WATERS According to the methodology referred to in the pretreatment section of natural waters, the results of the procedure performed in the testing of jars, for clarification and technical demand for chlorine for water disinfection are presented. Table 5 and 6 shows the milliliters of aggregate alumina sulfate and sodium hypochlorite to each of the beakers.

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Table 5. Coagulant dosage to 10% v / v were added to each sample. Samples

Sample A

Sample B

Sample C

Sample D

Sample E

Sample F

Coagulant dosage

1 ml

2 ml

3 ml

4 ml

5 ml

6 ml

Table 6. Dose of sodium hypochlorite (NaOCl) that were added to each sample. Samples

Sample A

Sample B

Sample C

Sample D

Sample E

Doses of disinfectant

0.025 ml

0.05 ml

0.07 ml

0.09 ml

0.1 ml

In Figure 1, we can see the recorded data of speeds flock formation in samples, according to the dose of the coagulant added and the time to sediment the suspended solids. It can be estimated that the last point on the graph corresponds to the highest dose of coagulant added and the higher rate of formation. The optimal dose of coagulant was used to calculate the amount of necessary coagulant was 6 ml, due to the higher rate of formation of flock-recorded sample F. Based on Equation 10, with 1320 ml of diluted coagulant, the total volume of the raw water was completely tested. Figure 2 shows the data recorded on the amount of excess disinfectant added in the samples and the formation of free residual chlorine. By adding chlorine to water, it reacted with the substances it generally contains; less chlorine is shown to act as a disinfectant. We continued by adding excess chlorine, reaching a time when the excess free residual chlorine appeared as chlorine, which really acts as a disinfectant. This fact is known as the critical point (break point) is represented in the graph as a function of the minimum chlorine path. The optimal dose of disinfectant that was used to calculate the necessary amount of disinfectant was 0.09 ml, and at that point it formed free chlorine residual of 1.5 ppm. From Equation 11, with 20 ml of sodium hypochlorite both types of natural waters were treated. 2.25

4.0

2.00

3.5 1.75

3.0

Free residual chlorine (ppm)

P a r t i c l e s e d i m e n t a t i o n v e l o c i t y (m L/m in)

4.5

2.5 2.0 1.5 1.0 0.5 0.0

B reak P oint

1.25 1.00 0.75 0.50

-0.5

0.25

-1.0 1

2

3

4

5

6

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.10

0.11

Added disinfectant (ml/lt)

Coagulant concentration (mL)

Figure 6. Evolution of aluminum sulphate clarification of surface water.

128

1.50

Figure 7. Evolution of free residual in the chlorine in natural water chlorination.

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RESULTS OF THE PHYSICOCHEMICAL AND MICROBIOLOGICAL ANALYSIS According to the techniques used for the characterization of the waters described in the methodology section, there were presented the quantitative results of the physical, chemical and microbiological parameters determined in each type of natural water, in the entry and exit of the prototype. Express the results in initial concentration (C0 ) And final concentration (CF ) The first part of the results of natural waters before receiving the treatment of purification in the prototype and the second as the results of water already treated by the prototype at the end of the purification process. Table 7 shows the results of quantifying the microorganisms in the crude and purified natural water. The group of bacteria identified were total coliforms, Gram-negative bacilli made up of four genera, including the most predominant Escherichia coli. Table 7. Test Certificate of the microbiological analyzes of natural and purified water. Surface water (river)

Groundwater (spring)

Parameters

Units

References

Maximum permissible limits

C0

Cf

C0

Cf

Total Coliforms

54,000

0

79

0

NMP/100ml

NOM-112SSA1-1994

Not detectable

Fecal Coliforms

35,000

0

Not detectable

0

NMP/100ml

CCAYAC-M004/8

Not detectable

Aerobic mesophilic

2

0

2

0

CFU / ml

NOM-092SSA1-1994

Not detectable

Table 8 shows the results of the quantification of the physical and chemical parameters in the crude and purified natural waters. Table 8. Test Certificate of the physical and chemical analyzes of natural and purified water. Surface water (river)

Groundwater (spring)

Parameters

Maximum permissible limits

Units

References

Nice

--

NMXAA-083-1982

Nice

Unpleasant

Nice

--

NMXAA-083-1982

Nice

0.00

1.00

0.00

Units

NMX-AA-045SCFI

15 Units

52.00

0.00

0.00

0.00

UTN

NMX-AA-038SCFI

5 UTN

8.20

7.60

7.50

7.20

Units

NMX-AA-008SCFI

6.5-8.5 U

C0

Cf

C0

Cf

Smell

Unpleasant

Nice

Unpleasant

Flavor

Unpleasant

Nice

Real Color

58.00

Turbidity

pH

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Total alkalinity Free residual chlorine

186.80

7.60

401.70

18.87

ppm

NMX-AA-036SCFI NMX-AA-108SCFI

300 ppm

0.00

0.00

0.00

0.00

ppm

Total Hardness

237.50

1.25

385.87

2.45

ppm

NMX-AA-072SCFI

200 ppm

Calcium hardness

140.00

1.00

242.55

0.00

ppm

NMX-AA-072SCFI

200 ppm

Magnesium hardness

97.50

0.25

143.32

2.45

ppm

NMX-AA-072SCFI

200 ppm

Total Dissolved Solids

512.00

30.00

663.00

32.00

ppm

Chlorides

48.80

14.80

15.54

7.17

ppm

NMX-AA-073SCFI

250 ppm

Sulfates

50.70

1.00

14.54

1.70

ppm

NMXAA-074-1981

250 ppm

Baking soda (NaHCO3)

0.00

5.33

13.30

13.79

ppm

NOM-041SSA1-1993

--

--

NMX-AA-034SCFI

0.1 ppm

500 ppm

Calcium bicarbonate Ca (HCO3)2

226.80

1.62

392.93

0.00

ppm

NOM-041SSA1-1993

Magnesium bicarbonate Mg (HCO3)2

68.48

0.37

209.68

3.58

ppm

NOM-041SSA1-1993

--

Sodium chloride NaCl

80.52

24.42

25.64

11.83

ppm

NOM-041SSA1-1993

--

Sodium sulfate Na2 SW4

75.04

1.48

2.52

0.00

ppm

NOM-041SSA1-1993

--

Magnesium sulphate MgSO4

60.99

0.00

0.00

0.00

ppm

NOM-041SSA1-1993

--

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The removal rates of up to 100% of total and fecal coliforms can be attributed to adequate pre-treatment of clarification and disinfection of natural waters before entering raw purification filters. In addition, the germicidal lamp that completely destroyed still present microorganisms. Complete removal of the organoleptic characteristics such as smell, taste and color is due to the effective performance of activated carbon filters. The total elimination of turbidity is caused by the proper functioning of the sediment filters and partly due to mineral resin. The significant decrease in alkalinity and total hardness is attributed to the excellent arrangement and sequence in the semipermeable membrane and the softener respectively. The removal and significant reduction of total dissolved solids, chlorides, sulfates and inorganic salts is due to the high performance membrane that has semi permeability to separate water flow. The results of the purified samples indicate that the equipment is capable of removing and completely remove high concentrations of organic and inorganic pollutants, obtaining water suitable for human consumption complying with the maximum permissible limits of the Mexican Official Standards. REMOVAL OF METAL IONS BY ION EXCHANGE AND ADSORPTION WITH MINERAL RESIN Considering the analytical (ICP-OES) technique described and used in the methodology section of adsorption and ion exchange of heavy metals on mineral resin, final concentrations of metal ions in aqueous solution are presented. The removal rate of an ion in aqueous solution depended on the quantity exchanged in the resin; it is also a function of the mineral exchange capacity. On this basis, it was considered that under the same experimental conditions, the sample that has a higher capacity for a given ion exchange is one that exhibits the highest percentage of removal for that ion. The balance between the concentration of an ion in solution and the mass of the ion exchanged at T = 25 ° C without pH adjustment are shown in Figures 3 and 4. Experimental data were interpreted using the model of Langmuir isotherm that is mathematically represented as follows: N= 1.0

0.9

N (mg adsorbed ion/gr adsorber)

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1

Equation 12 Zn (II) Hg (II) Cr (II) Cd (II) Pb (II)

1.0

Zn (II) Hg (II) Cr (II) Cd (II) Pb (II)

0.9

N (mg adsorbed ion/gr adsorber)

Nmax + KCf 1 + KCf

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

0.0 0

10

20

30

40

50

60

70

0

10

Final concentration (mg/L)

Figure 8. Isotherm exchange of Pb (II), Cd (II), Cr (II), Hg (II) and Zn (II) on the unmodified zeolite.

20

30

40

50

60

70

Final concentration (mg/L)

Figure 9. Isotherm exchange of Pb (II), Cd (II), Cr (II), Hg (II) and Zn (II) on the modified zeolite.

The results of ion exchange on mineral revealed that metal ions Hg (II) and Zn (II) exchanged very slightly, obtaining mass adsorbed per gram of resin low compared to metal ions Pb (II), Cd (II) and Cr (II) exchanged on the resin in larger amount than the other ions. The exchange capacity of the modified resin is greater than that of the unmodified and is dependent ion is used to modify and duration of the modification process.

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Furthermore, the ability of the modified resin increases when there is greater amount of exchangeable ions that moved during modification. The removal percentage of Pb (II) varied from 28.83 to 72.45%, Cd (II) of 15.41 to 34.31%, of Cr (II) 10.84 to 25.35%, Hg (II) 3.77 to 23.56% and Zn (II) 3.82 to 10.58%. Thus, the descending order of the selectivity of the resin for metal ions is: Pb (II) > Cd (II) > Cr (II) > Hg (II) > Zn (II). To obtain percentages of high and favorable removal of heavy metals on mineral resin, one can say that the prototype is competent and is designed to treat polluting effluents of industrial processes that usually are discarded in natural waters, making it an issue of global relevance because of its high toxicity to humans and the environment.

CONCLUSIONS An autonomous water purification prototype for rural and natural disaster was designed, built and tested. . The prototype is designed for the operation of a purification system of up to 10 hours a day, meeting the water use requirements of an entire family for domestic use, personal hygiene and consumption. The capacity and settlement of batteries give us a prolonged autonomy (7 days), to ensure weekly day and night operation in the case the panel did not capture solar energy due to unfavorable weather conditions, to implement it in regions where climate variability is extreme and unstable. The prototype’s dimensions and weight make it convenient and accessible to be moved to different places, in different territories. The prototype is designed to remove various heavy metal contaminants, total coliforms, salts of sulfates, chlorides and carbonates, thereby overcoming the conventional equipment. The evaluation with raw natural waters reveal that the prototype is suitable and appropriate to treat water from different sources, with high concentrations of various types of contaminants commonly found in them. Resulting in a clean, healthy and safe water for human consumption, with good taste, smell and color. Reduced gastrointestinal diseases and reduced mortality rates in children due to the consumption of this contaminated resource. Ensuring the health of society in rural areas that lack of quality of life and drinking purified water and electricity. Evaluating the mineral resin, a material that integrates some of the filters in the purification process, reveals the environmental impact of prototype removing heavy metals in natural waters. Verify that the range of removal and disposal of contaminants is very wide and extensive to be implemented in regions with industrial and agricultural activities without any limitation or condition. An innovative new model of autonomous water purifier for rural and natural disaster was successfully developed. The device generates a water flow of 140 L / day, enough for multiple uses in a family. It contributes to improving the quality of life in marginalized rural areas and in regions where natural disasters happen very often. It has an economic, easy to carry and highly durable design. It will be easy to purchase. The lifetime of the filter is prolonged and their maintenance is very simple.

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REFERENCES Abella M.A., (2005), Sistemas fotovoltaicos, Editorial Era solar, 2a edición, pg. 75-130. Almanza S.E., Cajigal R.J., Barrientos A., (1997), Reportes de insolación de México. Southwest Technology Development Institute, NMSU. Cadotte J.E., (2001), A new thin-film composite seawater reverse osmosis membrane. Desalination 32:25– 31. Cárdenas Y., (2000), Treatment of coagulation and flocculation, SEDAPAL, Lima Peru, April Comisión Nacional del Agua (CONAGUA), (2010), Estadísticas del agua en México, Ed. 2010, México D.F. Díaz D.F., (2008), Desinfección del agua con luz ultravioleta, Anexo 5°, Sección 1, Chile Frundt G., (1989), El Polipropileno. En Iniciación a la química de plásticos, Ed. Hanser, España, pág. 62-70. Godos J.M., (2004), Estudio de procesos de adsorción/desorción de iones en resinas encapsuladas, Tesis. Instituto Nacional de Estadística y Geografía (INEGI), (2012), Perspectiva estadística Chiapas, Ed. diciembre 2012, México D.F. Leyva R., (2000), Remoción de metales pesados en solución acuosa por medio de clinoptilolitas naturales, Revista Internacional de Contaminación y Ambiente, San Luis Potosí, Mayo. Organización Mundial de la Salud (OMS)/UNICEF (2006), Meeting the MDG drinking water and sanitation target: the urban and rural challenge of the decade. Ginebra, Suiza. Ramírez Q.F., (2001), Tratamiento de desinfección del agua potable, Ed. Canal Educa, Junta de Castilla y León. Toledo I.B., (1997), El carbón activado como adsorbente de compuestos húmicos y microorganismos en disolución acuosa, IV Reunión del Grupo Español del Carbón, pág.25.

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ENERGY EFFICIENCY


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BRAZIL DECISION-MAKING SUPPORT METHOD TO IMPROVE ENERGY EFFICIENCY IN RESIDENTIAL BUILDINGS ARTHUR SANTOS SILVA LAIANE SUSAN SILVA ALMEIDA Advisor: Professor Enedir Ghisi, PhD

ABSTRACT Building simulation analyses usually involve different criteria such as thermal comfort, energy consumption, thermal load, and internal temperatures, which makes it difficult to assess the building performance in a rational way. However, there is a lack of specific methods of multi-criteria evaluation in this area. Thus, this paper aims to develop a method for decision-making support to improve energy efficiency in houses. The simulation experiment included analysis of a house in four different climates of Brazil. The performance criteria were the degree-hours of adaptive discomfort and energy consumption for air-conditioning (for heating and cooling) in different preference scenarios of decision-making. The performance of eight constructive systems were evaluated considering uncertainties related to the operation of the house (occupancy routines, openings operation, use of equipment and lighting) whose data were collected through field survey. The results showed that some systems had statistically equivalent performance considering the range of uncertainty, which would not be noticeable with a deterministic analysis. The constructive system of double brickwork masonry performed better for most climates; for Belen, however, steel framing proved the most suitable. Nonetheless, different preference scenarios would indicate different ideal systems for this climate. This work developed a method of decision-making, with advanced least-applied statistical techniques in the building simulation area. This enables the possibility for future analysis of overall performance alternatives, other design variables and other performance criteria involved. KEYWORDS: multi-criteria decision-making; uncertainty analysis; building simulation.

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INTRODUCTION Energy efficiency for buildings is much studied in the fields of engineering and architecture around the world, seeking strategies to give users adequate thermal comfort with low energy consumption. In countless published papers, we found different analyses of parameters that can harm or help energy efficiency, depending solely on how they are applied in buildings. These include solar absorptance of walls and roofs, the thermal properties of the construction systems, building operation by occupants, and others. Computer simulation of buildings is a widely used by scientists to validate building interaction with all the above parameters by combining different air conditioning, passive and natural ventilation systems. In addition to validating performance, simulation is widely applied in the field of environmental regulations and certification (IWARO; MWASHA, 2010) to propose project solutions, provide feedback (HEO et al, 2012) and develop methods for validation, optimization and multi-criteria analyses (FESANGHARY et al., 2012). Simulation is an important tool for these analyzes because it admits various performance criteria with the most advanced algorithms in the scientific community for transfer of available heat and mass. However, some studies point to the problem of using computer simulations without due consideration for confidence intervals when presenting findings (MACDONALD; STRACHAN, 2001). Burhenne et al. (2013) suggest that explicit consideration of uncertainties is much more an exception than a rule in computer simulation studies for buildings, and specific deterministic analyses are limited in contrast to probabilistic studies. Uncertainty analyses are not widely used because they demand a large number of computer simulations and make it necessary to vary the parameters to be analyzed in small values to generate confidence intervals in performance outcomes. Confidence interval analyses enable decision-making and more precisely improve building performance. Computer simulation, being a numerical experiment, is susceptible to the uncertainties inherent in the methods and computational algorithms themselves, mainly in relation to the variables input into the model. As noted by Hoes et al. (2009), user behavior is one of the largest sources of uncertainty and can cause differing results in analyses to improve the energy efficiency of buildings. Examples of user behavior variables are room occupancy routines, venting times and equipment use generate heat loads and consume energy. These directly affect the internal temperature of rooms, changes in surface heat, air changes, air conditioning operation, and consequently energy consumption. Shi (2011) notes that improving building performance is a problem that encompasses various criteria. Energy efficiency studies should consider thermal comfort and energy consumption simultaneously, making it more difficult and non-trivial to determine alternative performance. Performance criteria are usually contrasting, i.e., when one alternative is found to improve a performance criterion, another criteria worsens (MAGNIER; HAGHIGHAT, 2010). Few studies in the international literature include this issue, such as Hopfe et al. (2013) who considered various criteria such as initial cost, resulting internal temperatures, the degree of cold or heat discomfort, individual control, energy consumption, project flexibility, and others. Diakaki et al. (2013) also reviewed different criteria, such as energy consumption and CO2 costs and emissions, to improve building performance strategies within the parameters involved. We see a need to consider uncertainties in computer simulations of buildings, as the significance of user behavior is a reality of multi-criteria energy efficiency validation. A combination of these tools and techniques is needed to select proper performance alternatives that might lead to enhancing the energy efficiency of buildings.

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OBJECTIVE This study aims to develop a decision-making support method that uses computer simulations, uncertainty analyses and the multi-criteria reality of performance validation, and combines them to improve the energy efficiency of residential buildings.

METHOD The work comprises a computer simulation experiment and three additional tests to improve the energy efficiency of buildings. Figure 1 shows the process followed. For this the purpose of this study, ‘energy efficiency validation’ means validating the thermal and energy performance of a building simultaneously. The computer simulation experiment was configured by inputting data from the model (a social interest building) and simulation parameters, defining validation criteria for thermal and energy performance, and characterizing the climates reviewed. Operating uncertainty ranges were defined and reported in the analysis. The first stage is an initial sensitivity analysis that includes the building construction variables, used to obtain significant data regarding their impact on the performance criteria. With this information, the dynamic simulation was configured in the software program for each construction system chosen, with proper propagation of operational uncertainties. Comparative performance among construction systems was verified, given the uncertainties. It concludes with multi-criteria decision-making that aims to select the most efficient systems based on the decision makers’ preferential scenarios. R language scripts were used to process the data and calculate the performance criteria generated by the computer simulations.

Dynamic computer simulation experiment

Defining the simulation model (social interest housing) Evaluation criteria (thermal and energy performance) Weather data (four different climates)

Analyses of the studies

Defining the operational uncertainties (occupancy, venting, electric / electronic equipment use)

Initial sensitivity analysis with ‘basic effects’ (the variables involved)

Performance evaluation for construction systems with operational uncertainties (eight systems)

Multi-criteria decision-making under different preference scenarios considering uncertainties

Figure 1 - Summary of the method by processes performed in this study RESEARCH BACKGROUND We consider this an ‘exploratory’ study because it seeks to generate knowledge on the energy efficiency of various construction systems in different climates with uncertainties, a subject that is rarely covered in the literature. This article is of a ‘practical’ nature, in keeping with the computer experiment conducted. The experiment uses both primary and secondary data sources. The primary source used was the building operational data, derived from field research and audits. The secondary source refers to all other configurations of the computational experiment, such as the thermal properties of materials, weather data, and the algorithms and coefficients used in the simulation (obtained from the literature and normative databases).

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The exploratory analysis approach is ‘quantitative,’ as it uses computational experiments and statistical data analysis. The research outcome is classified as ‘applied’ because it seeks solutions to a specific problem: the need to improve energy efficiency in buildings. The technical procedures of this study included ‘experimental research’ using a dynamic computer simulation, ‘data collection’ of building operational data, and ‘bibliographic research’ for the other configurations of the simulation. The intervention instruments used were a ‘sensitivity analysis,’ an ‘uncertainty analysis’ and ‘multi-criteria decision-making,’ all well-known statistical techniques in the literature. BASIC BUILDING MODEL AND DEFINING CLIMATIC CONDITIONS Figure 2 shows the basic model of social interest housing, which include three bedrooms and a living room/ kitchen, with which the analyses in this paper were validated.

Bathroom Living room / kitchen

Bedroom 3

Bedroom 2

Bedroom 1

Figure 2 - Basic building model used for the simulations. We knew that the weather conditions of each locality were decisive in selecting the most suitable construction system for buildings, primarily sunlight, dry bulb temperatures, relative humidity, and the existence of dry and humid seasons and different temperature ranges. To verify the behavior of different construction systems in the different climates, a decision-making process was conducted in four towns of Brazil using ‘TRY’ (Test Reference Year by Goulart et al. (1998)) or ‘TMY’ (Typical Metrological Year) as follows: Belen (TRY), Campo Grande (TMY), Curitiba (TRY), Florianopolis (TRY). Figure 3 shows the weather data for the four towns mentioned above. Belen has an equatorial, hot and humid climate, with slight temperature variations, and poorly defined dry and cold seasons. Campo Grande has a tropical climate with a dry season, a broader range of temperatures, a hot and humid summer, and a mild, less humid winter. Curitiba is the coldest capital of Brazil, with a temperate climate and a broad range of temperatures, due to cold fronts from the South. Florianopolis has its own coastal climate, with well-defined summer and winter seasons, high relative humidity throughout the year, and no dry season.

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Mean overall sunlight [W/m2] Mean dry bulb temp. [ºC] Mean relative humidity [%]

Month

Temperature [ºC] / Relative humidity [%]

Solar irradiation [W/m2]

d) Florianópolis-SC

Temperature [ºC] / Relative humidity [%]

Month

Month

Solar radiation [W/m2]

Solar radiation [W/m2]

c) Curitiba-PR

Temperature [ºC] / Relative humidity [%]

Month

b) Campo Grande-MS

Temperature [ºC] / Relative humidity [%]

Solar radiation [W/m2]

a) Belén-PA

Mean direct sunlight [W/m2] Mean dew-point temp. [ºC]

Figure 3 - Climatic data for the four towns surveyed

CONFIGURATION OF SIMULATIONS AND PERFORMANCE CRITERIA The dynamic computer simulations were conducted using EnergyPlus v.8.3 (DOE, 2015), a thermal and energy analysis program for buildings under the transitory regime, which can integrate different heat transfer systems and mechanisms. This program is widely used by the scientific community for construction simulations (Crawley et al., 2001). The building was simulated for the four climates described with TRY (Test Reference Year) file time data. The TMY (Typical Meteorological Year) files obtained from the Solar and Wind Energy Resource Assessment (SWERA) project (DOE, 2015) were used for Campo Grande. In order to take into account different performance criteria, two scenarios were adopted for analysis: (a) natural ventilation and (b) hybrid ventilation. For the natural ventilation scenario, the building was configured with the AirFlowNetwork program developed by EnergyPlus for the Walton (1989) studies. Ventilation

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was deemed available throughout the simulation period and controlled for occupancy routines, internal temperatures, and a set point operating value. Equation 1 shows the criteria for operating the vents to allow natural ventilation for the rooms. Há ventilação natural se

Tamb > Text Tamb > Tset point

Equation 1

Rotina de operação ≠ 0

Where: Tamb

is the room air temperature [°C]

Text

is the outside air temperature [°C]

Tset point is the set point temperature [°C]

For this scenario, the simulation program calculates the hourly operating temperatures for each room of the building (living room/kitchen and bedroom). The adaptive thermal comfort criterion of Standard 55 (ASHRAE, 2013) were chosen to determine discomfort indicators over long periods. The indicators were the degree-hours of discomfort due to heat or cold (cooling or heating degree-hours, respectively). These indicators were calculated based on variable baseline temperatures for each month of the year, according to the adaptive criterion of Standard 55 for each climate. Figure 4 shows the adaptive limit of Standard 55 (ASHRAE, 2013) for the different outside temperatures prevailing each month, and the monthly upper and lower temperature limits for each town. For the hybrid scenario, the building was modeled with artificial air conditioning in the bedrooms at night. The Package Terminal Heat Pump system was adopted, which works by heating with a heat pump and cooling with a cooling coil. For the remaining period, natural ventilation was adopted using the AirflowNetwork with the same settings as for the natural ventilation scenario. The system was configured using the coefficient of performance (COP) for the 2.75 W/W heating system and the 3.0 W/W cooling system. The airflow per person was 0.00944 m³/s. The system’s cooling capacity was sized using EnergyPlus with a factor of 1.2 based on typical summer and winter days. In the hybrid scenario, the criteria were energy consumption for bedroom heating and cooling throughout the year. For the two scenarios, four different criteria were analyzed jointly, as shown in Table 1. The criteria were calculated for each room of the building (living room/kitchen and bedrooms), and the equivalent of the entire area was calculated using the weighted average per useful area of each room for each performance criterion.

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Temperatura operativa [ºC]

Temperatura limite inferior Temperatura limite superior b.1) Legenda para os limites da Standard 55

Temperatura de bulbo seco horária Temperatura de bulbo seco média diária Temperatura limite inferior Temperatura limite superior Temperatura média mensal prevalecente [ºC]

b.2) Legenda para os limites mensais

Temperatura [ºC]

Temperatura [ºC]

a) Limites da Standard 55

Horas do ano

Horas do ano

d) Limites para Campo Grande-MS

Temperatura [ºC]

Temperatura [ºC]

c) Limites para Belém-PA

Horas do ano

e) Limites para Curitiba-PR

Horas do ano

f) Limites para Florianópolis-SC

Figure 4 - Lower and upper temperature limits for adaptive thermal comfort according to ASHRAE Standard 55 (2013), for each climate.

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Table 1 - Criteria for heat and energy performance used in the study. Type

Performance criteria

Description

Heat

Degree-hours of heating Degree-hours of cooling

Accrued annual thermal discomfort, as per ASHRAE 55

Energy

Power consumption with nighttime heating Power consumption with nighttime cooling

Sum of annual energy consumption

BUILDING OPERATION DATA SURVEY Data were obtained through field research by applying questionnaires among social interest housing residents in Florianopolis. They answered on their perceptions of use and occupancy routines. The occupancy routine questionnaire collected information on the number of inhabitants for a sample of 51 buildings, plus how each room is used during the week and on weekends. The door and window operating routine questionnaire contained a list of all room openings and their use in summer and winter for natural ventilation. The sample size was 17 buildings in summer and 34 in winter (SILVA; GHISI, 2014). Equipment and lighting use routines were also determined using an energy audit, with 53 buildings monitored for during two weeks each. The study by Silva et al. (2014) shows details of the procedure for data collection and processing. Due to space considerations, graphs and data on the routines are not detailed in this paper. Table 2 shows the variables and descriptions surveyed, the confidence level used, and the variation levels. The values​​ used for each are shown under Item 3.6, where an operational uncertainty analysis is described. Table 2 - Operational variables surveyed as primary research data. Variable

Description

Room occupancy routines

The hourly occupancy of each room in the building on weekdays and weekends. There are three routine levels (lower, middle and upper) with 80% confidence for each room depending on the number of hours of occupancy.

Door operating routines

The time that inner doors are open, allowing for natural ventilation of rooms. There are three routine levels (lower, middle and upper) with 80% confidence for each room in summer or winter, depending on the number of hours they are open.

Window operating routines

The time that outer windows are open to allow for natural ventilation of rooms. There are three routine levels (lower, middle and upper) with 80% confidence for each room in summer or winter, depending on the number of hours they are open.

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Equipment use routine

The time that equipment is used in each room of the building throughout the day, relative to the average installed power in each room. Thus, this is a deterministic routine.

Lighting use routine

The time that lighting is used in each room of the building throughout the day, relative to the average installed power in each room. There are three routine levels (lower, middle and upper) with 80% confidence for each room, depending on the number of hours in operation.

Average equipment power

The calculated mean power for equipment in each room of the building. There are three routine levels (lower, middle and upper) with 90% confidence for each room in terms of installed capacity.

Average lighting power

The calculated mean power for lighting in each room of the building. There are three routine levels (lower, middle and upper) with 90% confidence for each room in terms of installed capacity.

INITIAL SENSITIVITY ANALYSIS A sensitivity analysis determines the variables that affect performance criteria. The Morris method (1991) was chosen as a preliminary form of analysis (CAMPOLONGO; SALTELLI, 1997) to determine which variables to include when validating the construction systems. The Morris method determines the qualitative impact of variables involved through ‘basic effects.’ It was used successfully by authors such as McLeod et al. (2013), who validated the performance of zero carbon emissions buildings, and Heo et al. (2012), who developed an energy consumption calibration method with almost steady-state simulation and actual data. However, no Brazilian studies in the field of ​​dynamic simulation of buildings have applied this method. Table 3 shows the independent variables used in the sensitivity analysis. Table 3 - Independent variables of the initial sensitivity analysis using the Morris method with variance levels. Variable

Code

Unit

Level 1

Level 2

Level 3

Level 4

Outer wall - heat transmittance

Uparext

W/m²K

0.75

1.75

2.75

3.75

Outer wall - heat capacity

Ctparext

kJ/kg K

20

120

220

320

Outer wall - solar absorptance

αpar

-

0.20

0.40

0.60

0.80

Inner wall - heat transmittance

Uparint

W/m²K

0.75

1.75

2.75

3.75

Inner wall - heat capacity

Ctparint

kJ/kg K

20

120

220

320

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Variable

Code

Unit

Level 1

Level 2

Level 3

Level 4

Roof - heat transmittance

Ucob

W/m²K

0.80

1.90

3.00

4.10

Roof - heat capacity

Ctcob

kJ/kg K

20

95

170

245

Roof - solar absorptance

αcob

-

0.20

0.40

0.60

0.80

Roof - outer surface emissivity

εcob

-

0.05

0.35

0.65

0.95

Floor - heat transmittance

Upis

W/m²K

0.80

1.90

3.00

4.10

Floor - heat capacity

Ctpis

kJ/kg K

160

270

380

490

Windows - fraction of area relative to floor

Aab

-

0.08

0.10

0.12

0.14

Windows - fraction of ventilation area

Fvent

-

0.20

0.40

0.60

0.80

Windows - air infiltration rate

TInfJ

kg/s.m.

1.00E05

6.67E03

1.33E02

2.00E02

Windows - shadow size

SombH

m

0.00

0.20

0.40

0.60

Windows - visible transmission of Venetian blinds

Venez

-

0.25

0.50

0.75

1.00*

Windows - solar factor of glass

FSvid

-

0.36

0.53

0.70

0.87

Doors - air infiltration rate

TInfP

kg/s.m.

1.00E05

6.67E03

1.33E02

2.00E02

Building - solar orientation

Azimuth

Degrees

0

90

180

270

* Absence of Venetian blinds Efforts were made to select variables that characterize the building’s heat and energy performance, such as heat transmittance and capacity of construction components, solar absorptance and emissivity of surfaces, ​​opening sizes, ventilation fraction of windows, air infiltration rate of openings, solar factor of glass and, finally, solar orientation of buildings. All these are ‘project variables’ and should be taken into account in building projects that include energy efficiency. For this study, we adopted four levels of variation in the project variables. The input variables for heat transmittance and capacity were configured using simplified components, keeping constant such properties as specific heat and thermal conductivity of layers, and altering the thickness and specific gravity of one of the equivalent materials. The Morris method requires that independent variables be kept continuous and that levels be equidistant from each other for the same variable (e.g., the thermal transmittance of walls varies by 1.00 W/m²K at all levels).

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The sampling was done using triage methods. Each Xi parameter, whose i is between 1 and k, varies by p levels selected in the sample space. The sampling region is called Ω, which is a k-dimensional array of p levels. For an X value, or its ‘basic effect,’ the ith value is defined in Equation 2. di (X) =

y (X1 , ... , Xi-1 , Xi + ∆ + Xi+1 , ... , Xk ) - y(X) ∆

Equation 2

Where: 1 p-1

, ... , 1 - 1 p-1

Is a value between

p

Is the number of levels

X

Is any value selected in Ω, such that the transformed point (X + ei ∆) remains Ω

ei

Is the vector of zeros, but with a unit (1) in its ith component

di

Are the basic effects.

A finite distribution of basic effects is obtained by means of a random sampling of X in Ω, denoted by Fi. The number of elements of each Fi is denoted by pk-1[p - ∆ (p-1)]. However, the method suggests sampling r basic elements for each Fi to obtain an efficient experiment. Then, the minimum computational cost would be r(k + 1). 200 simulations were calculated for each scenario (natural and hybrid ventilation). For each input variable, two sensitivity measures were calculated: the mean (µ) obtained from Equation 3 that analyzes the overall impact on the output variable, and the standard deviation (σ) obtained from Equation 4 that estimates the set of interaction effects and their non-linearity. A sensitivity analysis was applied to each of the four performance criteria in Table 1 and for each climate analyzed. r

µ=∑

di r

Equation 3

i=1

r

σ=

∑ (d r- µ) i

2

Equation 4

i=1

Where: µ

Is the mean of the elementary effects, which determines whether the variable is significant.

σ

Is the standard deviation between the elementary effects, which measures the sum of all interactions of xi with other factors and their nonlinear effects.

r

Is the number of basic effects studied for each variable.

di

Are the basic effects.

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VALIDATING THE ENERGY EFFICIENCY OF BUILDING SYSTEMS For this analysis, eight construction systems were selected with common features found in the construction market, as shown in Table 4. The characterization of each system refers to the type of wall, roof and floor, plus the air infiltration rate of frames. Note that other materials configurations could have been used while maintaining the same characteristics of the system under analysis. System 1 was modeled with a window infiltration rate of 0.02 kg/s.m., systems 5, 7 and 8 were modeled with 0.01 kg/s.m., systems 2 and 4 with 0.005 kg/s.m., and system 3 with 0.0001 kg/s.m. For validation purposes, solar absorptance was set at 0.4 for the walls and 0.5 for the roof, to enable comparison only among construction systems. Table 4 - Overview of the eight construction systems used for this study. Construction system

ID

Walls

Roof

Floor

CIP concrete

01 – CIP concrete

Cast-in-place concrete with plaster finish

Ceramic tiles, air chamber and concrete slab

Concrete, ceramic

Concrete block masonry

02 - Concrete masonry

Masonry of concrete blocks with mortar

Fiber-cement tiles, air chamber, concrete slab, and plaster finish

Concrete, ceramic

Autoclave aerated concrete block masonry

03 - Aerated concrete masonry

Autoclave aerated concrete block masonry

Ceramic tiles, air chamber, concrete slab, and plaster finish

Light steel framing

04 - Steel framing

Cement boards, flex foam, drywall

Fiber-cement tile, foam flex, air chamber, and cement plate with plaster finish

Concrete, ceramic

Wood frame

05 - Wood frame

OSB, foam flex, plasterboard

Ceramic tile, air chamber, OSB, plasterboard

Concrete, OSB, wood

6-hole brick masonry

06 - Perforated ceramic masonry

6-hole ceramic brick masonry with mortar

Ceramic tile, air chamber and plasterboard lining

Concrete, ceramic

Solid ceramic block masonry

07 - Solid ceramic masonry

Solid ceramic block masonry

Ceramic tile, air chamber and planking

Concrete, ceramic

Double solid ceramic block masonry

08 - Double solid ceramic masonry

Solid ceramic block masonry in two layers with flex foam

Ceramic tile, foam flex, OSB

Concrete, ceramic

Concrete, ceramic

Note: OSB means Oriented Strand Board

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Table 5 shows the properties calculated for walls, while Tables 6 and 7 show roof and floor properties. Table 5 - Thermal properties of wall components for each construction system Properties

System 1

System 2

System 3

System 4

System 5

System 6

System 7

System 8

U [W/m²K]

3,35

3,12

1,29

1,49

0,91

2,45

3,69

1,25

RT [m²K/W]

0,299

0,320

0,774

0,670

1,097

0,408

0,271

0,802

Ct [kJ/m²K]

290

116

72

15

26

88

155

327

α [adim-]

0,4

0,4

0,4

0,4

0,4

0,4

0,4

0,4

FS [%]

5,4

5,0

2,1

2,4

1,5

3,9

5,9

2,0

θ [hours]

3,7

2,6

3,9

1,3

2,2

2,4

2,5

8,0

Table 6 - Thermal properties of the roof components for each construction system Properties

System 1

System 2

System 3

System 4

System 5

System 6

System 7

System 8

U [W/m²K]

2.04

2.05

2.03

0.75

1.27

2.17

2.07

1.00

RT [m²K/W]

0.491

0.487

0.494

1.325

0.786

0.460

0.483

1.001

Ct [kJ/m²K]

215

206

215

31

42

27

36

41

α [adim-]

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

FS [%]

4.1

4.1

4.1

1.5

2.5

4.4

4.1

2.0

θ [hours]

4.9

4.8

4.9

2.7

2.4

0.9

1.4

2.7

Table 7 - Thermal properties of floor components for each construction system Properties

System 1

System 2

System 3

System 4

System 5

System 6

System 7

System 8

U [W/m²K]

5.08

5.08

5.08

4.55

2.01

5.08

5.12

5.12

RT [m²K/W]

0.197

0.197

0.197

0.220

0.496

0.197

0.195

0.195

Ct [kJ/m²K]

205

205

205

301

328

205

209

209

θ [hours]

2.1

2.1

2.1

3.0

5.2

2.1

2.1

2.1

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The properties of thermal transmittance (U), heat resistance (RT), heat capacity (Ct), solar absorptance (α), solar factor of opaque components (FS), and thermal lag (θ) were calculated according to the recommendations of Brazilian NBR Standard 15220-2 (ABNT, 2005). For this validation, we propagated operational uncertainties as seen in Table 8 using the Monte Carlo method. The Latin Hypercube was chosen as the random sampling technique. It is used for models that require intensive computational work, due to its efficient stratification, which makes it possible to extract large amounts of uncertainty and sensitivity data using small samples (HELTON et al., 2006). This method represents an evolution of common stratified sampling, because it divides the probability density function of input parameters into strata of equal probability of occurrence. In the same simulation, the value of each parameter is taken from different strata (MACDONALD, 2009). Stratum means a subdivision of the probability density function into intervals. The same number of points is removed from each of these intervals (SALTELLI et al., 2008). The Latin Hypercube method was successfully applied by several authors (e.g., BREESCH;. JANSSENS, 2005, 2010; HOPFE; HENSEN 2011 MARA; TARANTOLA, 2008; MECHRI; CAPOZZOLI; CONRAD, 2010). A sample was taken of 250 simulations for the variables in Table 8 and replicated in the eight construction systems. Note that this number was simulated for the two scenarios (natural and hybrid ventilation), which made it possible to calculate the four performance criteria properly for the four climates. There was total of 16,000 dynamic simulations. Data processing for this analysis involved developing box charts for each climate, divided by performance criteria and by construction systems. Uncertainties were calculated using confidence intervals with the Student’s t distribution with 95% confidence according to Equation 5. Relative deviation was calculated with 95% confidence as per Equation 6. The relative deviation is the uncertainty indicator for this study, as it gives an amplitude percentage relative to the mean disturbance caused by the operational variable figures. Table 8 - Independent variables of the operational uncertainty analysis experiment. Operational variables

Unit

Functions and levels

Bedroom occupancy routine

hours/year

D{(2681; 3229; 4009) (0.2;0.6;0.2)}

Living room/kitchen occupancy routine

hours/year

D{(889; 1785; 2996) - (0.2;0.6;0.2)}

Power installed with equipment in bedrooms

W/m²

D{(10.21; 18.28; 26.36) (0.2;0.6;0.2)}

Installed power with equipment in living room/ kitchen

W/m²

D{(12.51; 19.31; 26.10) (0.2;0.6;0.2)}

-

N(0.5;0.1)

W/m²

D{(3.36; 3.82; 4.29) - (0.2;0.6;0.2)}

hours/day

D{(0.58; 1.17; 1.67) - (0.2;0.6;0.2)}

Radiant fraction of equipment Installed power with lighting in bedrooms Lighting use routine in bedrooms

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Installed power with lighting in living room/ kitchen

W/m²

D{(1.63; 2.01; 2.40) - (0.2;0.6;0.2)}

Lighting use routine in living room/kitchen

hours/day

D{(1.50; 2.00; 3.21) - (0.2;0.6;0.2)}

-

T(0.74; 0.85; 0.95)

Window operating routine in bedrooms

hours/year

D{(2954; 3683; 4206) (0.2;0.6;0.2)}

Window operating routine in living room/ kitchen

hours/year

D{(1627; 2239; 2739) (0.2;0.6;0.2)}

Window operating routine in bedrooms

hours/year

D{(3799; 5099; 5978) (0.2;0.6;0.2)}

Routine operation of doors in the living room/ kitchen

hours/year

D{(1630; 2434; 3384) (0.2;0.6;0.2)}

Set point temperatures for window operation in summer

°C

D{(20; 22) - (0.5;0.5)}

Set point temperature for window operation in winter

°C

D{(24; 26) - (0.5;0.5)}

Radiant fraction of lights

Legend: N means normal probability distribution; N(mean, standard deviation). T means triangular distribution; T (lower level, mode, upper level). D means discrete distribution; D{(level 1; ... 2; ... 3) (occurrence probability of level 1; ... level 2, … level 3)}. X - SXtα (n-1) < µ < X - SXtα (n-1)

Equation 5

SXtα (n-1) X

Equation 6

drα = Where:

X Is the mean operational uncertainty sampling for each climate, criterion and system [in °Ch or kWh/year] S

Is the sample standard deviation of operational uncertainty for each climate, criterion and system [in °Ch or kWh/year]

tα (n-1)

Is the number of deviations from Student’s t distribution for (n – 1) degrees of freedom (equal to 249) and significance α equal to 0.025 at each end of the distribution

µ

Is the mean population estimated by confidence interval

drα

Is the relative deviation with 95% confidence for α equal to 0.025.

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MULTI-CRITERIA DECISION MAKING For the multi-criteria decision-making, we used two related methods: the AHP (Analytical Hierarchical Process (SAATY, 1991)) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution (HWANG; Yoon, 1981)). We used the AHP method to build decision-making scenarios by generating a ‘preference vector’ for performance criteria from a verbal scale shown in Table 9. Seven different decision-making scenarios were defined, which represent different decision makers (fictitious for this study) who might have different preferences for the four criteria involved in the performance validation. Table 10 shows the settings and description for each. The criteria are analyzed broadly with a correlation matrix (i,j). If the criterion on the left is more significant than the one on the right, the scale value of significance is greater than one; if it is less significant, it is less than one. Table 9 - Verbal significance scale to attribute significance to performance criteria using the AHP method (adapted from Saaty (1991)) Numerical values

Verbal scale

1

The two criteria are equally significant

3

The criterion on the left is slightly more significant than the upper one.

5

The criterion on the left is moderately more significant than the upper one.

7

The criterion on the left is much more significant than the upper one.

9

The criterion on the left is extremely more significant than or absolutely preferable to the upper one. Table 10 - Preferential decision-making scenarios

Scenarios

152

Description

1

The degree-hours of discomfort are equally significant, but moderately more so than energy consumption

2

Energy consumption levels are equally significant, but moderately more so than degreehours of discomfort

3

Heating is moderately more significant than cooling, and the degree-hours of discomfort are as significant as power consumption

4

Cooling is moderately more significant than heating, and the degree-hours of discomfort are as significant as power consumption

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5

Each criterion is slightly more significant than the other, in this order: cooling degreehours, consumption for cooling, heating degree-hours, consumption for heating.

6

Each criterion is slightly more significant than the other, in this order: consumption for heating, heating degree-hours, consumption for cooling, cooling degree-hours

7

All criteria are equally significant

The matrix is calculated with ai,j values for each criterion as per Equation 7, w preferences are analyzed for the scale in Table 9, the standardized value xi,j is obtained in each j column according to Equation 8, and the weight vector is calculated on each i line with Equation 9, in which l is the number of criteria, and finally we obtain a consistency vector for each decision scenario in Table 10. ai, j =

se i = j ai, j= 1 se i ≠ j e ai, j= w ai, j= 1/w ai, j

xi, j =

∑ki=1 ai, j

vi = ∑lj =1 xi, j /l

Equation 7 Equation 8 Equation 9

The TOPSIS method was used to determine the best alternative to meets the performance criteria with weight vectors defined using the AHP method, which gave the performance criteria values for each of the alternatives, where applicable, for each construction system. The standardized matrix was calculated using Equation 10 where yi,j was the outcome of each construction system i, for each performance criterion j. Another weighted matrix was calculated by the weight vector of each criterion using Equation 11. The ‘ideal solution’ (Sj+) was determined, i.e., a performance alternative having the lowest (pj+) values on each of the criterion obtained on the systems with Equation 12, and also the ‘non-ideal solution’ (Sj-) with the highest (pj-) values from Equation 13. Finally, the Ci+ vector was calculated with Equation 14, which shows which of the i systems is the best performance alternative. This analysis is done separately for each climate. ri, j = yi, j /∑ni =1 y2i, j

Equation 10

pi, j = ri, j x vj

Equation 11

l

Si+=

∑ (p - p )

_

∑ (p - p )

i,j

Equation 12

+ 2

i

j=1

l

Si =

i,j

i

_ 2

Equation 13

j=1

_

_

Ci+ = Si / (Si+ + Si )

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Equation 14

153


Using these two methods makes it possible to find a ranking of the best construction systems for each climate under each preference scenario chosen. For the decision-making process, we selected the upper limit with 95% confidence for each performance criterion and for each construction system, according to the confidence interval generated by the operational uncertainties. This upper limit value represents the worst-case performance.

FINDINGS The findings are divided into initial sensitivity analysis, performance validation of construction systems, and multi-criteria decision-making. Simulation scenarios (natural and hybrid ventilation) are analyzed together, using the four different performance criteria. INITIAL SENSITIVITY ANALYSIS A sensitivity analysis was performed to identify the variables affecting the results of heating/cooling degreehours and consumption for heating and cooling. Figure 5 shows the analysis findings for the four towns, and the mean value of each variable (horizontal axis) shows its significance compared to the performance criterion. The standard deviation value (vertical axis) shows the non-linearity of the variable in the interval under consideration.

Eixo vertical: desvio padrão σ

Variable

GHA [ºCh]

GHR [ºCh]

CA [kWh/year]

CR [kWh/year]

Eixo horizontal: média µ

Figure 5 - Sensitivity analysis findings using the Morris method for all buildings and each dependent variable for the four climates in terms of mean and standard deviation.

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For example, for heating degree-hours in Curitiba, the solar absorptance of the roof has the most impact, followed by the solar absorptance of the walls (the mean values ​​on the horizontal axis are the largest). Table 11 shows the six most influential variables in each case. As shown, there are no degree-hours of discomfort or energy consumption for heating in the town of Belen, as expected given its characteristics. The mean value shows that the different variables influence each climate, primarily each performance criterion. Belen and Campo Grande have warmer climates, so we obtained practically the same variables affecting cooling degree-hours and energy consumption for cooling, considering that solar absorptance is always significant. However, in cold climates such as Curitiba and Florianopolis, solar absorptance is important in terms of heating degree-hours and the thermal transmittance of the outer walls. As for energy for heating, the variables are different for each climate. This verification justifies the need for multi-criteria analysis and to determine overall building performance, considering that the different variables are significant for each performance criterion. Table 11 - Ranking of the most influential variables using the Morris method. Ranking

Belen-PA

Campo Grande-MS

GHA

GHR

CA

CR

GHA

GHR

CA

CR

1o

-

αcob

-

αcob

Uparext

αcob

Ctparext

αcob

2o

-

αpar

-

Ctparext

Ctparext

αpar

Ucob

αpar

3o

-

Ucob

-

αpar

Ucob

Ucob

Uparext

Ctparext

4o

-

Uparext

-

Ucob

αcob

Uparext

TinfP

Ucob

5o

-

Fvent

-

Ctcob

αpar

Ctparext

αcob

Ctcob

6o

-

Ctparext

-

Uparext

TinfP

Fvent

αpar

Uparext

Ranking

Curitiba-PR

Florianopolis-SC

GHA

GHR

CA

CR

GHA

GHR

CA

CR

1o

αcob

Ucob

Ucob

Uparext

Ucob

Ucob

Ucob

αcob

2o

αpar

Ctparext

Uparext

Ucob

αcob

αcob

Uparext

αpar

3o

Uparext

αpar

TinfP

αcob

Uparext

αpar

Ctparext

Ctparext

4o

Ucob

Uparext

αcob

Ctparext

αpar

Ctparext

TinfP

Ucob

5o

TinfP

εcob

Ctparext

αpar

Ctparext

Uparext

αcob

Uparext

6o

Ctparext

αcob

αpar

Fvent

TinfP

Fvent

αpar

Ctcob

This shows that there is no pattern to follow, and that each climate has its own peculiarities. Therefore, finding a satisfactory performance alternative is not a trivial matter. For example, lowering the solar absorptance of roofs is an alternative for Florianopolis, because it helps reduce cooling degree-hours, but it worsens the cold discomfort conditions (in heating degree-hours).

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We want to emphasize that the standard deviation shown in Figure 5 indicates the nonlinear behavior of each independent variable. We verified that most of the variables behave non-linearly, primarily in the case of cooling degree-hours in Curitiba for the outer wall thermal capacity variable. VALIDATING CONSTRUCTION SYSTEM PERFORMANCE The performance of the eight construction systems we analyzed was also validated for the climates of Belen, Campo Grande, Curitiba, and Florianopolis. The propagation of operational uncertainties in the probabilistic analysis made it possible to compare systems very precisely, and then to check whether the uncertainties in one construction system were higher than in others, meaning that users had a greater influence on a room of that construction system. Table 12 shows the mean relative deviation values ​​with a 95% confidence level, i.e., the percentage that shows the variance of operational uncertainties for each construction system. Comparing the values ​​by size, we see that some climates have insignificant criteria, as obviously there is no concern with discomfort due to cold in Belen. In Curitiba, cooling degree-hours were small and not significant. The same was true of energy consumption for heating in Campo Grande. These observations may lead decision-makers to a given preference scenario and thus avoid having to consider a criterion that is not a concern in a given climate. The highest figures for cooling degree-hours and power consumption were obtained in Belen, while the highest figures for heating degree-hours and consumption for heating were obtained in Curitiba. Table 12 also shows that System 3 (aerated concrete masonry) and System 8 (solid double ceramic masonry) were the ones with the largest operational uncertainties among the performance criteria. Although uncertainties were somewhat similar from one system and climate to another, having a relative deviation of 2% to 29%, after excluding certain ‘misleading’ values ​​(such as low means and high standard deviations, which pose no problems due to the low impact of the mean). Table 12 - Mean and relative deviations with a 95% confidence level for the performance criteria of each construction system and town evaluated (mean (% relative deviation)). Variável (Sistema)

Belém

Campo Grande

Curitiba

Florianópolis

GHA (1)

0 (0%)

675 (9%)

12302 (7%)

3283 (10%)

GHA (2)

0 (0%)

559 (11%)

11254 (8%)

3017 (12%)

GHA (3)

0 (0%)

209 (25%)

7370 (14%)

1735 (20%)

GHA (4)

0 (0%)

489 (19%)

9127 (14%)

1981 (20%)

GHA (5)

0 (0%)

989 (15%)

11375 (12%)

3114 (17%)

GHA (6)

0 (0%)

1199 (10%)

13973 (7%)

4342 (11%)

GHA (7)

0 (0%)

1027 (8%)

13691 (6%)

4026 (9%)

GHA (8)

0 (0%)

62 (55%)

6053 (19%)

941 (29%)

GHR (1)

3502 (15%)

2402 (11%)

12 (47%)

438 (13%)

GHR (2)

4725 (15%)

3131 (11%)

50 (35%)

571 (14%)

GHR (3)

7305 (17%)

4335 (15%)

122 (40%)

815 (20%)

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GHR (4)

6785 (10%)

4998 (8%)

295 (19%)

895 (14%)

GHR (5)

9578 (10%)

7158 (8%)

794 (17%)

1551 (14%)

GHR (6)

8495 (8%)

6375 (6%)

533 (14%)

1336 (12%)

GHR (7)

5729 (9%)

4175 (7%)

162 (17%)

771 (10%)

GHR (8)

3091 (27%)

1580 (21%)

<0,6 (9%)

224 (26%)

CA (1)

0 (0%)

104 (26%)

3062 (10%)

999 (12%)

CA (2)

0 (0%)

69 (19%)

2563 (13%)

834 (13%)

CA (3)

0 (0%)

14 (34%)

1530 (13%)

422 (19%)

CA (4)

0 (0%)

55 (27%)

1742 (13%)

537 (18%)

CA (5)

0 (0%)

105 (29%)

1868 (11%)

651 (18%)

CA (6)

0 (0%)

178 (17%)

2735 (10%)

1089 (13%)

CA (7)

0 (0%)

164 (18%)

3145 (12%)

1162 (12%)

CA (8)

0 (0%)

8 (71%)

1734 (2%)

393 (21%)

CR (1)

2746 (10%)

1142 (8%)

299 (2%)

391 (8%)

CR (2)

2466 (10%)

1090 (10%)

302 (6%)

370 (9%)

CR (3)

2467 (11%)

1172 (11%)

300 (7%)

388 (11%)

CR (4)

886 (17%)

355 (15%)

313 (9%)

167 (7%)

CR (5)

576 (20%)

226 (17%)

276 (8%)

141 (10%)

CR (6)

781 (15%)

323 (12%)

256 (5%)

157 (9%)

CR (7)

1618 (10%)

659 (9%)

282 (7%)

254 (9%)

CR (8)

2602 (11%)

1140 (11%)

333 (<0,6%)

328 (9%)

Note: The criteria are heating degree-hours (HDH) and cooling degree-hours (CDH) in [°Ch], and energy consumption for heating (CH) and for cooling (CC) in [kWh/year].

This shows that operational uncertainties are significant and interfere with the performance outcomes of all of the construction systems, so it is imperative to consider them in the computer simulation analysis. Figure 6 shows the performance criteria findings in graphic form for easy viewing. The filled-in boxes represent the intervals between the first and third quartile (containing 50% of the sample), and the lines show the total sample size, while asterisks represent ‘misleading’ values (outside ​​ of the distribution).

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GHA [ºCh]

GHR [ºCh]

CA [kWh/year]

CR [kWh/year]

Horizontal axis: Construction system

Figure 6 - Performance criteria findings by town: heating degree-hours (HDH), cooling degree-hours (CDH) in [°Ch], and energy consumption for heating (CH) and for cooling (CC) in [kWh/year]. Note: The vertical axes are in different units and scales for easy viewing.

For heating degree-hours, construction system 8 (solid double ceramic masonry) was found to be the best option, followed by system 3 (aerated concrete masonry). System 8 is also the best choice for cooling degree-hours, followed by System 1 (cast-in-place concrete). Obviously, the high thermal capacity of the walls in construction system 8 showed a large advantage in that ‘naturally ventilated’ scenario. In terms of energy consumption, the best systems were 8 (double ceramic masonry) and 3 (aerated concrete masonry). For consumption with cooling, system 5 (wood frame) was the best in all climates except Curitiba, where system 6 (perforated ceramic masonry) was better. Because of the operational uncertainties, there may not be a significant difference in the performance of some of the construction systems for the same criterion. In the case of energy consumption for cooling, systems 1, 2 and 3 have similar means and overlapping probability distributions, which shows that they are statistically equivalent (in all climates). The same is true of systems 2 and 5 for heating degree-hours in the climates of Curitiba and Florianopolis, which perform equally in terms of uncertainty, and systems 5 and 7 in terms of consumption for cooling in Curitiba.

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The study showed that it is hard to improve the energy efficiency of buildings without including several criteria simultaneously. It is not a trivial matter to find the best construction system that meets all the performance criteria, since each system is best for particular conditions (e.g., good only with heating, natural ventilation, air conditioning, etc.). These findings lead to making the decisions shown in Item 4.3. MULTI-CRITERIA DECISION MAKING Table 13 shows the outcomes of the analysis with the AHP (Analytical Hierarchy Process) to generate the preference vector of the performance criteria under each preference scenario. The sum of the weightings for each vector is always 1. Scenario 5, for example, shows that decision makers have a strong preference for cooling degree-hours, i.e., the findings for cooling degree-hours have greater weight than do others. Table 13 – Weight vector Cj+ for each performance criterion under each preference scenario Preference Scenario

Performance Criterion HDH - Heating degreehours

CDH - Cooling degreehours

CH - Consumption for heating

CC - Consumption for cooling

Scenario 1

0,417

0,417

0,083

0,083

Scenario 2

0,083

0,083

0,417

0,417

Scenario 3

0,417

0,083

0,417

0,083

Scenario 4

0,083

0,417

0,083

0,417

Scenario 5

0,167

0,518

0,127

0,187

Scenario 6

0,187

0,127

0,518

0,167

Scenario 7

0,250

0,250

0,250

0,250

Table 14 shows the upper limit value at 95% confidence for each criterion and climate, obtained using an operational uncertainty analysis. These are the figures that were actually used in the decision-making process. Table 15 shows the final outcome of the TOPSIS analysis containing the preference ranking of each construction system in descending order (1 being the best system and 8 the worst). The analysis of Item 4.2 shows that some scenarios make no sense in certain climates such as Belen, where there is no heating, and Curitiba where heat discomfort is low compared to cold discomfort.

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Table 14 - Decision-making criteria, considering the upper limit with 95% confidence for each criterion Town →

Belen

Campo Grande

Curitiba

Florianopolis

HDH

CDH

HDH

CDH

HDH

CDH

HDH

CDH

01 - CIP concrete

0

4043

739

2657

13119

18

3597

495

02 - Concrete masonry

0

5425

620

3484

12156

67

3367

652

03 - Aerated concrete masonry

0

8563

262

4991

8404

171

2088

975

04 - Steel framing

0

7481

581

5391

10405

353

2380

1020

05 - Wood frame

0

10511

1139

7713

12720

926

3631

1771

06 - Perforated ceramic masonry

0

9174

1317

6779

14970

608

4801

1497

07 - Solid ceramic masonry

0

6244

1111

4458

14542

190

4398

850

08 - Double Solid Ceramic Masonry

0

3926

97

1908

7179

0

1214

282

System ↓

CA

CR

CA

CR

CA

CR

CA

CR

01 - CIP concrete

0

3016

131

1235

3344

304

1122

423

02 - Concrete masonry

0

2717

82

1193

2817

308

943

405

03 - Aerated concrete masonry

0

2740

19

1306

1722

318

502

432

04 - Steel framing

0

1034

70

407

1962

334

636

178

05 - Wood frame

0

694

135

264

2113

302

771

154

06 - Perforated ceramic masonry

0

895

209

363

3032

277

1234

172

07 - Solid ceramic masonry

0

1787

193

719

3454

296

1306

275

08 - Double Solid Ceramic Masonry

0

2875

13

1270

1939

357

475

359

System ↓

Note: The criteria are heating degree-hours (HDH) and cooling degree-hours (CDH) in [°Ch], and energy consumption for heating (CH) and for cooling (CC), in [kWh/year].

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Table 15 - Findings from ranking the best construction systems for each climate, considering the different multi-criteria decision-making scenarios System

Scenario [Belém-PA]

Scenario [Curitiba-PR]

1

2

3

4

5

6

7

1

2

3

4

5

6

7

01 - Concreto In Loco

2

7

6

6

2

6

6

4

6

6

1

2

6

4

02 - Alv Concreto

3

5

7

7

3

7

7

3

5

4

2

3

4

3

6

8

8

8

7

8

8

2

1

2

4

4

1

2

5

3

1

1

5

1

1

6

3

3

6

6

3

5

8

1

4

4

8

3

4

8

4

5

8

8

5

8

7

2

3

3

6

2

3

7

8

8

7

7

8

7

4

4

2

2

4

4

2

5

7

7

5

5

7

6

1

6

5

5

1

5

5

1

2

1

3

1

2

1

03 - Alv Concreto Celular 04 - Steel Framing 05 - Wood Frame 06 - Alv Ceram Furada 07 - Alv Ceram Maciço 08 - Alv Ceram Maciço Dupla System

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03 - Alv Concreto Celular 04 - Steel Framing 05 - Wood Frame 06 - Alv Ceram Furada 07 - Alv Ceram Maciço 08 - Alv Ceram Maciço Dupla

Note: The ranking is in descending order, i.e., 1 is the best system and 8 is the worst system. When each scenario of Table 15 is interpreted as a different decision maker, the best system is identified as being the one resulting in the greatest number of ‘1’ values ​in the ranking. For example, for the climates of Curitiba, Campo Grande and Florianopolis, the best system would be 8 (solid double ceramic masonry)

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because it was first place in the ranking under most scenarios. This system has a high thermal capacity and a low thermal transmittance, making it suitable for these climates. For Belen, the best system would be 4 (steel framing) based on its low thermal capacity and low thermal transmittance. System 4 was the first in the ranking for scenarios 3, 4, 6 and 7 in Belen. These findings can be an aid to choosing a construction system for a building in any of those towns, and were analyzed according to designer or owner preferences, which can be identified with any of the supported scenarios. These scenarios are useful for different decision-maker choices, depending on the air conditioning systems used in the rooms. For example, if a study were done in Belen for a building with no air conditioning, the best system would be 8, as the findings for scenarios 1 and 5 show. In the case of Belen, being a warmer climate, scenario 4 would be used because cooling is more important than heating, so system 4 (steel framing) would be best. Interestingly, this system did not stand out in any of the individual criteria (cooling degree-hours or cooling consumption) but had a reasonable energy consumption performance (3rd best system) and a medium heat discomfort performance. It became the system of choice for applying the method because of a trade-off among the criteria, which was not the case with the other systems. For example, system 5 (wood frame) would be the best for cooling consumption, and the worst in terms of cooling degree-hours (see Figure 6). In Campo Grande, scenario 4 or 5 would be chosen by a decision maker, so system 4 (steel framing) or system 8 (double ceramic masonry) would be best. However, if there were no type of artificial air conditioning for that climate, the best system would be 8, and if decision-makers did not prioritize cooling degree-hours during the day (because of the lack of building occupancy during that period), system 5 (wood frame) would be the best (see Figure 6). In Curitiba, scenario 3 or 6 would be chosen if heating were deemed the most important, and the best systems would be 3 (aerated concrete masonry) or 8 (double ceramic masonry). Thermal inertia and thermal transmittance are decisive factors in this climate. In Florianopolis, system 8 (double ceramic masonry) was the best under practically all scenarios except for scenario 2 for which system 4 (steel framing) is better because energy consumption has more weight than the other criteria.

LIMITATIONS OF THE STUDY We should emphasize that this study applies to social interest housing, with all necessary scenario settings, such as natural and hybrid ventilation, and all choices inherent in the method. Operational uncertainties (occupancy, operation of openings, use of equipment, and lighting) were the primary data surveyed in Florianopolis. Despite this fact, uncertainties that might not match the realities of that specific town were analyzed for other locations. In any case, being an exploratory study, its aim was met successfully. Importantly, the method made it possible to find the best construction system among the ‘predefined’ alternatives. We did not find an ‘optimized system’ for local climatic conditions, but only a ‘best choice.’ It assumes that although the system chosen for each climate still has considerable energy consumption for artificial air conditioning, and also some associated heat discomfort, an optimized system would seek to minimize discomfort and energy consumption to the extent possible. However, that is beyond the scope of this work.

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Item 4.1 showed that solar absorptance is a highly influential variable for most of the performance criteria, but it was a fixed value in our performance validation of the construction systems. If absorptance were considered, system performance could be improved.

CONCLUSIONS This study developed and applied a decision-making support method for public interest buildings, seeking to improve energy efficiency. The computer simulation considered the criteria of thermal comfort and energy consumption for air conditioning, plus advanced statistical tools such as sensitivity analyses using the Morris method, uncertainty analyses using the Monte Carlo method, and multi-criteria decision-making using the AHP and TOPSIS methods. Validation was done for four different climates in Brazil, considering the operational uncertainties related to occupancy routines, operation of openings, and use of electronic equipment, obtained from a field survey. An initial sensitivity analysis showed that the most influential variables were different for each performance criterion and each climate, which were often contrasting. In other words, the same variable was significant in terms of cold and heat discomfort, but was inversely proportional. The influence of the thermal transmittance and capacity of the building components was noted, which justified our validation of construction system performance. Performance validation in the presence of uncertainty showed that each system performed well with certain criteria and poorly with others. Operational uncertainties were slightly higher in systems 3 (aerated concrete masonry) and 8 (solid double ceramic masonry), but without any pattern due to weather, with a relative standard deviation ranging from 2% to 29%, and a confidence interval of 95%. Finally, the decision-making process indicated the best-performing construction systems considering all criteria simultaneously. System 8 (solid double ceramic masonry) had the best performance for all climates, while system 5 (steel framing) was the most suited for Belen. However, different systems could be recommended for each climate, considering only one of the proposed preference scenarios, given the wide divergence of results. We infer that although an effort is made to find a suitable alternative for each climate, the end result depends on the decision-makers’ preference scenarios. Therefore, this method is important in terms of enabling studies of specific cases in which there is a group of decision makers with well-defined preferences. This study helped to develop a robust, rational method to support multi-criteria decision-making with statistical techniques that are recognized in the literature, to find the best construction system in terms of climate and preference scenarios. For professionals, the findings indicate interesting alternatives to be considered in the project for the four climates analyzed. For researchers, the method can be reproduced with other decision-maker preference scenarios and with different climates and criteria in search of an ideal alternative. We want to stress that the method is applicable not only to construction systems, but also to any ‘performance alternative’ that can be configured in a computer simulation. Future research will be conducted with the inclusion of other variables such as the solar absorptance of surfaces and different types of residential buildings.

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HOPFE, C. J.; HENSEN, J. L. M. Uncertainty analysis in building performance simulation for design support. Energy and Buildings, v. 43, n. 10, p. 2798–2805, 2011. HWANG, C. L.; YOON, K. P. Multiple attributes decision making methods and applications. Berlin, 1981. IWARO, J.; MWASHA, A. A review of building energy regulation and policy for energy conservation in developing countries. Energy Policy, v. 38, n. 12, p. 7744–7755, 2010. MACDONALD, I. A. Comparison of sampling techniques on the performance of Monte Carlo based sensitivity analysis. Building Simulation. Anais... p.992–999, 2009. Glasgow, Scotland. MACDONALD, I. A.; STRACHAN, P. Practical application of uncertainty analysis. Energy and Buildings, v. 33, n. 3, p. 219–227, 2001. MAGNIER, L.; HAGHIGHAT, F. Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network. Building and Environment, v. 45, n. 3, p. 739–746, 2010. MARA, T. A.; TARANTOLA, S. Application of global sensitivity analysis of model output to building thermal simulations. Building Simulation, v. 1, n. 4, p. 290–302, 2008. MCLEOD, R. S.; HOPFE, C. J.; KWAN, A. An investigation into future performance and overheating risks in Passivhaus dwellings. Building and Environment, v. 70, p. 189–209, 2013. MECHRI, H. E.; CAPOZZOLI, A.; CORRADO, V. Use of the ANOVA approach for sensitive building energy design. Applied Energy, v. 87, n. 10, p. 3073–3083, 2010. MORRIS, M. D. Factorial Sampling Plans for Preliminary Computational Experiments. Technometrics, v. 33, n. 2, p. 161–174, 1991. SAATY, T. L. Some mathematical concepts of the Analytic Hierarchy Process. Behaviormetrika, v.18, n.29, p. 1–9, 1991. SALTELLI, A.; RATTO, M.; ANDRES, T.; CAMPOLONGO, F.; CARIBONI, J.; GATELLI, D.; SAISANA, M.; TARANTOLA, S. Global Sensitivity Analysis: The Primer. John Wiley and Sons, Ltd, 2008. SHI, X. Design optimization of insulation usage and space conditioning load using energy simulation and genetic algorithm. Energy, v. 36, n. 3, p. 1659–1667, 2011. SILVA, A. S.; GHISI, E. Uncertainty analysis of user behaviour and physical parameters in residential building performance simulation. Energy and Buildings, v. 76, p. 381–391, 2014. SILVA, A. S.; LUIZ, F.; MANSUR, A. C.; VIEIRA, A.S.; SCHAEFER, A.; GHISI, E. Knowing electricity end-uses to successfully promote energy efficiency in buildings : a case study in low-income houses in Southern Brazil. International Journal of Sustainable Energy Planning and Management, v. 2, n. 2012, p. 7–18, 2014. WALTON, G. N. AIRNET – A Computer Program for Building Airflow Network Modeling. NISTIR 89-4072, National Institute of Standards and Technology, Gaithersburg, Maryland, 1989

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BRAZIL USER IMPACT ON NATURAL AND ARTIFICIAL LIGHTING SYSTEMS: A CASE STUDY OF CLASSROOMS AT THE UFMG SCHOOL OF ARCHITECTURE CAMILA CAMPOS GONร ALVES Advisor: Roberta Gonรงalves de Souza Vieira, PhD

ABSTRACT Brazil has seen an increase in instruments designed for assessing and improving building performance and certification. However, there is evidence that the potential energy savings of certified buildings does not guarantee its effective energy economy, because of the way buildings are used. This study aims to assess user behaviors towards lighting and the control of solar radiation management systems, and how buildings and their systems influence user behavior, using rooms at the UFMG Architecture School as a case study. It began with an initial assessment of the rooms to check the lighting and solar radiation conditions. User behavior was observed in the rooms under study, to assess the barriers and potential of each room and to propose lighting changes, replacement of curtains and installation of signs with information on the systems. It was found that teachers make most changes in classrooms. In general, existing systems do not meet user expectations, and occupants often have difficulties using the systems available to them. Despite some benefits, most system changes did not produce the expected results. Users tend to act on systems only when uncomfortable, so the most efficient projects are those that least require users to act on them. Thus, the main contribution of this work was to identify that building performance is a consequence of how they are designed and, consequently, understood by their users. KEYWORDS: User behavior, Energy Efficiency, Building performance

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INTRODUCTION By definition, energy efficiency is the ratio between the energy used by an activity and that used to implement it. Promoting energy efficiency includes optimizing energy resource transformation, transportation and use, from primary sources to consumption. Its basic principles include ensuring user comfort, safety and productivity; contributing to enhancing the quality of energy services; and mitigating environmental impacts (MMA, 2014). Laws enacted after the ‘70s pointed to the importance of energy efficiency in the world, in Brazil and, more specifically, in buildings. The emergence of building certification schemes and the efforts now made in this regard are signs of visible progress in the search for more efficient buildings. User behavior is a decisive variable in the energy efficiency equation, but is rarely studied, especially among Brazilian theorists. In particular, little is known about how an impact cycle works, how users influence energy efficiency and comfort in buildings, and how buildings influence user behaviors. Accordingly, this study aims to analyze the influence of user behavior in terms of their use of natural and artificial lighting systems. The use of natural lighting is influenced by how artificial lighting circuits are divided, how windows are used for daylight and ventilation, and how solar protection systems are used. This study will use observation techniques to assess the status of internal spaces and user behaviors, and will propose enhancements to the systems studied in order to change user behaviors.

OBJECTIVES • To assess how lighting systems are used, how installed solar control devices are used and how users relate to them, and to identify the difficulties and potentials of those installed systems • By observing user behavior, to propose changes to the environments with the aim to encourage more ‘efficient’ use of the systems under study. • To assess the efficacy of the proposed measures and the relationship between user behaviors and the systems available to them

LITERATURE REVIEW AND THEORETICAL GROUNDING Crisp (1978) observed the use of an office lighting system in the UK, in which all switches were located on the same panel. He noted that more lamps were turned on when their switches were placed next to and above the others. Which lamps were lit bore no relationship to the availability of daylight and the occupancy of rooms. Crisp concluded that switch use was directly related to their position. Similarly, Lindelof and Morel (2006) classified users in active and passive according to their willingness to use the controls available to them. They described the findings of a study in Switzerland, in which they observed that after users entered an office, lighting systems would only be acted upon again in cases of extreme discomfort. Furthermore, employees rarely used the available regulators, but only triggered the on/off control. The authors believed that this was because switches were positioned close to the front door, with no switches at employee workstations. Reinhart and Voss (2003) in Germany conducted a study in a commercial office building where one or two persons worked. They concluded that groups of individuals followed a very similar pattern of behavior, while

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isolated individuals followed more diversified patterns. The authors observed that all building occupants received daylight, which confirmed the impact of the building’s architectural design and lighting systems on the differentiation of user behaviors. It is precisely due to the importance of architectural design that building certification programs have gained significance in the domestic and international arena, particularly the LEED (Leadership in Energy and Environmental Design) system and the PBE EDIFICA (Brazilian Building Certification Program) launched in 2007 (the 2009 edition included office buildings). With regard to lighting systems, the LEED Reference Guide for Green Building Design and Construction (U.S. GREEN BUILDING COUNCIL, 2009) envisions independent controls to afford room occupants the autonomy to adjust them for greater comfort, productivity and well-being. In classrooms, lighting controls should be easily accessible to teachers and placed so as not to attract student attention, although the use of desktop fixtures related to the overall lighting of environments is suggested. Rooms with audiovisual activities should be easily adjustable to allow for low light levels and to achieve optimal contrast for projection screens, and when daylight is used together with artificial lighting, windows should allow adequate lighting without interfering with the projection. In these cases, building designers should consider passive project strategies such as suitable orientation to the sun and using devices for solar radiation protection and to daylighting control. Furthermore, the PROCEL-EDIFICA Program developed the RTQ-C technical quality requirements for energy efficiency levels in commercial, service and public buildings. They are intended to create the conditions for certifying the energy efficiency category of buildings. To grant this certification, buildings are inspected and classified from ‘A’ for the most efficient buildings to ‘E’ for the least efficient, based on the Roofing, Lighting Systems and Air Conditioning Systems criteria (BRAZIL, 2010). Lighting systems (the aim of this study) are assessed by comparing installed power to the power limits set in the RTQ-C and inspecting compliance with the requirements of circuit division,1 daylight contribution,2 and automatic system shutoff.3 Buildings must meet all three requirements to be classified as ‘A,’ the first two for class ‘B’ and the first one for class ‘C’ (BRAZIL, 2010). As for recommendations to address this issue, ISO 50.001 (2011), based on the continual improvement model (Plan-Do-Check-Act), states that since facilities, equipment and systems have a significant impact on energy consumption, users are not solely responsible for unnecessary energy costs. Norman (2010) reinforces this idea with a harsh criticism of the ‘blame and test’ philosophy, saying that poor projects are designed for people as we wish they were, not as they really are, which is the true cause of project inefficiency. Although the users’ role in architecture has evolved from being incidental to playing an effective part, in practice users are still mostly treated as accessories or ‘model users’ in architectural design (LINO, VILLELA, FIGUEIREDO, 2009). 1 Each room enclosed by walls up to the ceiling should have at least one manual control device for independent operation of internal ambient lighting. Each manual control should be easily accessible and located where the entire lighting system being controlled can be seen. If it is not possible to see the entire lighted area, users should be informed what areas are covered by manual controls with room diagrams (BRAZIL, 2010, p. 38). 2 Spaces having openings towards the outside [...] and more than one row of lamps parallel to openings should have manual or automatic controls installed for independent operation of the row of lamps closest to the opening, in order to promote the use of daylight (BRAZIL, 2010, p. 38). 3 Using an automatic system that turns lamps off at a preset time, a presence sensor, or an alarm system that indicates when the area is unoccupied (PROCEL/ELETROBRAS, 2009). An ‘automatic light shutoff system’ is required only for areas larger than 250m². Eco_Lógicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.

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Hertzberger (2010) distinguishes between users’ and ‘inhabitants,’ which is essential to understanding behavioral differences among the occupants of different spaces. According to this author, architectural projects can create conditions for a greater sense of responsibility and, consequently, more involvement in arranging the area so that users of those spaces become their inhabitants. In a study conducted for residential buildings, Gyberg and Palm (2009) concluded that changing people’s behavior requires ensuring that choices start with the individual, and that project alternatives do not negatively affect their lifestyle. Therefore, it is essential that the concept for an architectural project include proposals that will guarantee comfort and efficiency. Despite the ongoing studies, we found that there is still much to be done in the search for ways to identify and encourage more efficient user behaviors, primarily by creating more intuitive systems so that they will be utilized. After analyzing how users interact with curtains, windows and lamps in offices of Austria, Mahdavi and Proglhof (2009) remarked on how difficult it is to estimate behavior variables based on a single individual in a building, and that it is necessary observe trends in specific groups. They further concluded that findings in one building can hardly be used on another, and that the use and context of each building and its users’ cultural patterns should be taken into account, among others.

METHODOLOGY CASE STUDY: UFMG SCHOOL OF ARCHITECTURE AND DESIGN This study assessed three classrooms and three rooms of the Environmental Comfort and Energy Efficiency Lab (Labcon) at the UFMG School of Architecture (Figure 1), all naturally conditioned. These rooms had openings toward the North and South (room 315), toward the East (room 318) and toward the West (room 320A and labs). As solar radiation control systems, room 315 had translucent Venetian blinds on all northern windows, rooms 318 and 320A had blackout curtains, and room 320A also had white paint on the windowpanes. All three labs had Venetian blinds with horizontal white metal slats. All of the rooms studied had independent artificial lighting controls located inside the room next to the main entrance, in keeping with the RTQ-C ‘Circuit Division’ requirement. Figure 1 – Classrooms 315, 318 and 320A and labs 01, 02 and 03, respectively

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Assessing the natural and artificial light available in the rooms studied As an initial assessment of the rooms studied, their natural light was inspected using the Daysim computer program and consulted the Solar Chart of Belo Horizonte. We checked room brightness using on-site measurements with digital luxmeters (Model MLM-1010 – Minipa) to ascertain whether the luminance levels supplied by existing fixtures complied with the recommendations of the standard NBR 5413/1992, and the possible need to adapt the system to comply with ISO 89951/2013.4 This inspection was conducted following standard NBR 5382/1985 (measuring inside lighting). The areas for the case under study were evaluated for compliance with the RTQ-C requirements of control and lighting (circuit division and daylight contribution) and the density of installed capacity. In this way, we validated the lighting system classification of the rooms according to the Brazilian Certification Program (PBE EDIFICA). We checked the rooms that met the natural light contribution requirement to see whether the lamps could be turned off near the doors. In rooms where no daylight contribution was possible, this was allowed and we checked to see whether their use had changed after the adjustment. We also reviewed room diagrams to see what areas were effectively covered by the placement of the lighting system’s manual control. On-site observation To collect observational data, the rooms were observed for 162 days in the morning and evening periods. This observation was divided into four phases: 1) without changes; 2) after modifying the lighting system; 3) after modifying the solar radiation control system; and 4) after putting up the signs. For data collection, observation sheets were filled out, covering the six key aspects mentioned below: • General Characteristics: schedule of room use; radiation of direct solar radiation; and type of classes (practical or theoretical) • Audiovisual activities: Use of projection equipment in classrooms • Occupancy: In the room diagram, marking the tables occupied during the observation • Lighting Systems: Identifying when users turn lamps on or off, their reason for doing so, the person responsible for the change, and any difficulties in using the system • Solar Radiation Control Systems: Identifying how they work, the ratio of natural versus artificial lighting systems, and any difficulties using the system • Using Windows: Observing possible relationships between opening windows and using solar radiation control systems

4

The school was designed prior to this standard, so was not expected to meet its requirements.

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Proposed changes to the rooms studied The observations made during the initial assessment identified the challenges and potential of existing systems in the rooms studied at the UFMG School of Architecture and Design, in order to begin the second phase of this project. Specific changes were made to the proposed systems in order to determine how they changed user behavior. These changes were made ​​gradually in three stages: 1) changing the lighting system as proposed; 2) changing the solar radiation control system; and 3) putting up informational signs. These changes aimed to create a methodology for continual monitoring and quality improvement of existing systems in school environments through the techniques of ISO 50.001/2011 (Plan-Do-Check-Act). With each change, a new assessment was performed to test their efficacy and identify the need for further adjustments. Although we are aware that the changes made during this study were limited, the aim was to guide future work to introduce ‘continual improvement’ recommendations in the context of school projects. The following changes were proposed: Lighting System: 1) Changing the circuit division and the position of lamps that did not foster combined use of natural and artificial lighting, as per the guidelines of the RTQ-C (2010) 2) Installing three-way switches5 near these lamps to make their use more intuitive, following recommendations on controller positioning by Crisp (1978) and the LEED Reference Guide for Green Building Design and Construction (U.S. GREEN Building Council, 2009). This activity was carried out in rooms 318 and 320A. 3) Changing the switch arrangement, which was contrary to the location of the lamps, to make the system more intuitive. Solar radiation control system: 1) Replacing blackout curtains with Venetian blinds having gray metal horizontal slats on each window. The purpose for this change was to increase the availability of daylight and offer different use alternatives by adjusting the position of the slats (Figure 2). Figure 2: Room 318 before and after changing lamp locations and replacing blackout curtains with Venetian blinds

5 Three-way switches make it possible to turn on the same light or set of lamps from different points. In the case study, three-way switches were installed next to the doors and at another point near the lamps they operated. 172

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Putting up informational signs: 1) At this stage, we tried to correct the existing unreadable systems by putting up informational signs and testing the efficacy of the graphic materials proposed in the RTQ-C (2010) for smaller environments (see Figure 3). Figure 3 – Example of an informational sign

WHAT LIGHTS WHAT? DO YOUR PART TO HELP!

THESE LIGHTS CAN STAY OFF DURING THE DAY!

FINDINGS AND ANALYSIS INITIAL ASSESSMENT During the initial assessment of the rooms under study, we observed on the solar chart that the time of most direct solar radiation in room 315 would be the winter solstice, when sunlight would penetrate the room most deeply. However, the Venetian blinds obstructed much of this light. On the Southern side, however, the time with most solar radiation would be the summer solstice, when there would be direct solar radiation during the midday hours. In the early morning and late afternoon, direct solar radiation was obstructed by the neighboring buildings to the east and by the School of Architecture itself to the west. During critical periods, solar radiation could be easily regulated with the Venetian blinds installed. Since December and March are months of school holidays, solar radiation on the south side did not hamper

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classroom activities at those times. With regard to the artificial lighting system, the lamps in that room were divided into five circuits to take daylighting and InFocus use into account. From the light autonomy data simulated with the Daysim software, we found that the lamps by the windows could be left off all morning and part of the afternoon. For room 318, we found that, since not all panels coincided, the existing curtains blocked 40% of the window area when fully open, and solar radiation fell directly on the tables near the windows in the late morning. With regard to the artificial lighting system, this room had six lamps divided into two circuits. However, this division did not allow for combined use of natural and artificial lighting, or for turning off the lamps located close to walls used for projections. Using the Daysim simulation with the windows partially covered by the curtains, we found that if the circuit were divided to turn off the lamps near the windows separately from the others, the room would have 100% autonomous lighting near the windows in the mornings6 and 80% in the afternoons. In room 320A, the curtains obstructed 50% of the window area when fully open, and the white paint on the panes further blocked the daylight. Solar radiation fell directly on the tables in the afternoons. This room’s artificial lighting system was made ​​up of two lamps on the same circuit, which did not allow the combined use of natural and artificial lighting. Using the Daysim program, we found that with the curtains and artificial lighting system installed in the room, it was not possible to save energy through combined use of the natural and artificial lighting systems in the mornings or afternoons, because the light autonomy was 0% in the back of the room during both periods. However, if the windows had no obstructions (paint or curtains) and if the lamps were divided into two circuits, the light near the window could be kept off for 80% of the morning, because the room had no direct solar radiation during this period. Therefore, having a suitable system to control direct solar radiation, it could remain fully open during the morning period. In the afternoon, although daylight autonomy was even higher, due to the incidence of direct solar radiation, the solar radiation control system would have to remain closed, which reduced the potential use of natural lighting together with artificial lighting. In the labs, the installed Venetian blinds allowed different levels of solar radiation control, depending on the position of the flaps, without complete loss of available natural light. Our study of this side of the building, using the Belo Horizonte solar chart combined with an analysis of nearby obstructions, found that this room had direct solar radiation in the afternoon––from 12:00 to 16:00––during all months. Solar radiation was blocked by a nearby wall in the late afternoon, so combining natural and artificial lighting systems showed great potential in the mornings and less in the afternoons, when the Venetian blinds tended to remain totally or partially closed to regulate the incidence of direct solar radiation. With regard to the existing artificial lighting system, all lab rooms had lamps on more than one circuit, placed parallel to the window, meaning there was great potential to use combined natural and artificial lighting systems. Using the NBR 5282/1985, we found that all classrooms under study met the minimum luminance values​​ recommended in NBR 5413/1992, while none of the Comfort Lab rooms met these recommendations. None of the rooms studied met the mean values ​​of the current ISO 8995/2013 standard. Assessed individually, these rooms obtained good rankings in the PBE EDIFICA program (Table 1) in terms of low IPD (Installed Power Density) and compliance with some or all of the requirements for lighting systems. 6 Part of the light autonomy potential is expected to be lost in this period due to the need to keep the curtains closed some of the time to control direct solar radiation.

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However, they cannot be deemed as efficient because they do not meet the brightness levels required by the applicable regulations. Table 1 – Review of lighting system classifications in the rooms studied, according to the PBE Edifica program Place

Corresponding

Total

activity

area

REFERENCES PBE EDIFICA

INSTALLED

DPI (W/m2)

DPIL A

DPIL B

DPIL C

DPIL D

POWER

PREREQUISITES EqNumDPI

Circuit

Contribution of

division

natural light

EqNumDPI

PBE Edifica Classification

315

salas de aula

154,9

10,2

12,24

14,28

16,32

1562

10,09

5

Si

Si

5

A

318

salas de aula

89,65

10,2

12,24

14,28

16,32

455

5,08

5

Si

No

3

C

320A

salas de aula

34,48

10,2

12,24

14,28

16,32

260

7,54

5

Si

No

3

C

escritorios

26,13

11,9

14,28

16,66

19,04

130

4,98

5

Si

Si

5

A

escritorios

26,13

11,9

14,28

16,66

19,04

195

7,46

5

Si

Si

5

A

escritorios

40,64

11,9

14,28

16,66

19,04

260

6,40

5

Si

Si

5

A

LABCON SALA 01 LABCON SALA 02 LABCON SALA 03

ANALYSIS OF AND COMMENTS ON THE FINDINGS The findings from the four phases of this study in both the classrooms and Comfort Lab rooms reinforced the conclusions of Lindelof and Morel (2006) regarding the use of lighting systems. Users tend to operate such systems when entering and leaving rooms, and in the mean time they only change settings when uncomfortable because of a lack of lighting or need for other audiovisual activities such as using an InFocus projector. We found that the situation of windows and lighting systems when users enter a room would influence their subsequent use. If windows are open or closed, users tended to keep them that way. Likewise, if certain lamps were already lit in an environment in use, then unless the lack of light bothered users, they would tend to keep the lamps as they were. This behavior was observed in both classrooms and lab rooms. What changed from one type of room to another were the people who regulated the systems and how often they did so. In the classrooms, it was primarily the teachers who regulated to existing systems, mainly at the beginning and end of each class. The LABCON rooms showed more diversified patterns of behavior, as there was no predefined person to regulate the lighting systems and windows. In addition to changes when the first occupant entered and last one left, there were also more frequent changes upon arrival of new users. This confirmed the findings of the Reinhart and Voss (2003) study that the behavior of groups of individuals are more standardized, while isolated individuals follow more diversified patterns of behavior. In the lab rooms, users regulated the systems more and even adapted them, such as placing a table lamp on one of the worktables. This confirmed the characteristic behavior of ‘users’ in the classrooms and ‘inhabitants’ in the Comfort Lab rooms, as per the Hertzberger (2010) study. The fact that users of Labcon rooms are always the same and memorize the control settings for their lighting system also reinforced this feature of ‘inhabitants’ and significantly reduced the number of cases observed of users having difficulty understanding how the existing systems in the rooms worked.

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No significant adjustments were made in the classrooms during our observations, although the fact that the windowpanes of room 320A were painted white prior to our observation could be interpreted as an adaptation due to a project failure that did not provide adequate protection for solar radiation control. Although we did not see this adaptation being made, we believe that it was requested by a teacher or permanent school member and not by the students, which would characterize the behavior of that person as an ‘inhabitant’ of the school. In room 315, we found behaviors similar to those of lab users, as some of the students who used the room frequently memorized the operation of the lighting system controls, although most classroom users needed to try the switches in order to understand which lamps they turned on. Once again confirming the findings of Reinhart and Voss (2006) regarding the relationships seen in operating lighting systems, during all four phases we observed differences between classrooms (large groups of users) and Comfort Lab rooms (small groups of users). In the classrooms, use of lighting systems was associated primarily with the type of class given. Practical classes and projects required turning more lamps on, while lectures using an InFocus required turning off the lamps to see the projection better. In lab rooms, use of lighting systems was more related to occupancy. In classrooms, we observed a tendency to turn on fewer lamps during classes that used an InFocus projector before the change to the lighting system. However, this relationship became even more significant after the lighting system was modified to make it possible to turn off the lamp near the InFocus screen separately from the others. The fact that a room had different options for using the lighting system, with lamps on different circuits, brought positive results by allowing users to adjust the systems however they deemed most appropriate. This confirmed the conclusions of Gyberg and Palm (2009) that individuals should be able to choose. By enabling lighting systems to operate under at least two modes as per the recommendations of the LEED Reference Guide––general lighting and lighting for projector use––users could choose how to regulate them to serve their needs better. These findings were significant because it improved the quality of images projected on the walls7 by making it possible to turn off some of the lamps separately from the others, and enabled users to take notes during classes because the classrooms were not completely dark. The use of three-way switches in rooms 318 and 320A, located at the doors and near the projection areas and windows, did not have the desired effect. We expected that positioning switches near the lamps they controlled would increase their use in combination with natural lighting and projections, but this did not happen. We now believe that the switches at the front of the room were not used for two reasons. Although having the new switches closer to the teacher (who made most of the changes) would seem to be a benefit, they failed to use them out of habit, as they were not used to having such facilities in the classrooms and apparently had not noticed these new switches. In addition, the three-way switches sometimes resulted in lamps being left on or turned off inappropriately. During the 3rd phase of the observations, in addition to changing the solar radiation control system in room 318, the switch for the lamps near the window was placed next to the door and away from the others. This change was meant to encourage using them less in relation to the other switches, but sometimes users have left these lamps on when leaving the rooms. Nevertheless, this is a good way to develop new prototypes to be tested in future studies, as it ensures changes in user behavior. Regarding the use of combined artificial and natural lighting systems, we found that adapting the systems caused changes in user behavior, which reinforced the finding of Norman (2010) that users are blamed for the inefficient use of systems proposed by designers, although such misuse is caused by project failures. 7

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The changes made to ​​ the solar radiation control system are not considered here but will be mentioned later.

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This finding reinforced the validity of the continual improvement model (Plan-Do-Check-Act) implemented by ISO 50.001/2011, because as soon as modifications were made there were changes in the associated behaviors, which showed the need for new tests to correct failures or improve proposed systems. During the first phase, the systems in rooms 318 and 320A had circuits that did not allow for daylight contribution, contrary to the two RTQ-C requirements for lighting systems, and the curtains, although fully open, blocked part of the solar radiation. We did not find relationships between daylight and artificial lighting use in these rooms although room 315 had separate lighting circuits and Venetian blinds that let daylight in. This was seen in 75% of the theoretical classrooms observed. During the second phase, after altering the lighting systems in rooms 318 and 320A, we saw a behavior change in lighting system use in room 318, where daylight was used in the afternoon observations when there was no direct solar radiation, which allowed the use of natural light and no need to use the curtains. During these observations, the fact that daylighting was not used along with the lamps in room 320A seems to be a direct consequence of its white-painted panes and blackout curtains, which blocked part of the natural light and made it insufficient to keep the lamps turned off, as observed in the daylight autonomy data simulated with the Daysim program. During the 3rd phase of the observations, the window paint was removed and the blackout curtains were replaced with Venetian blinds in room 320A, which increased the availability of natural light in rooms 318 and 320A. We found that the combined use of natural and artificial lighting was significant in both rooms and that the lamps near the windows were kept turned off during 50%8 of the classes observed in room 318 and 67% of the classes in room 320A. Finally, after the signs were put up, we found that users initially kept their old habits with regard to turning on the lamps (ignoring the signs), and had difficulties understanding which switches were responsible for which lamps. However, after a while the users read the signs and changed their behavior: they tried the lighting system with the signs and we observed an awareness of how to use the lamps with daylighting on 87% of days in room 318 and on 67% of the days in room 320 A. In room 315, although users also tested the system after reading the signs, their behavior did not change but rather the ratio of natural versus artificial lighting remained the same during some of the lectures. The signs helped to standardize the lighting system controls in school. With regard to maintenance, during the phase of difficulties to understand what switches were responsible for what lamps, we believe that the way we graphically represented ‘What lights that?’ (see Figure 3) was not clearly understood by users, who felt that the time required to read the sign was greater than the time required to test the system, so users preferred the latter option. In the lab rooms, although the only change was putting up the signs, changes were also seen in user behaviors regarding the use of artificial lighting systems, solar radiation control systems and the combination of these systems with daylight. During the first phase, we observed little daylighting, with no relationship between the use of natural and artificial lighting in any of the rooms observed, although we did see daylighting being used in rooms 01 and 02 by operating the Venetian blinds,9 where were open for 50% of the observed days in room 01 and 35% in Room 02 to achieve greater incidence of daylight in both rooms. 8 In addition to altering the solar radiation control system, this phase included changes in the position of switches responsible for lamps close to the windows, which were installed separately from the others as mentioned above. 9

Blinds positioned outside the movable glass sales, as will be explained later.

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After posting the signs in these rooms, we observed the combined use of natural and artificial lighting, as the lamps near the windows were turned off on 34% of observed days. Furthermore, the number of times the Venetian blinds were fully or partially open also grew to 60% of the observed days in room 01, 50% in room 02 and 55.5% in room 03. Despite these behavioral changes in using the artificial lighting system, we also found that the lamps near the windows were lit before the time forecast in the simulations with the Daysim software, indicating that natural light availability may be lower than was simulated. This may be due to two factors: 1) the high density in the lab surroundings, with tall buildings that shade the rooms; and 2) the fact that the available daylight in the rooms was insufficient in relation to user expectations. This is contrary to the applicable regulations, which require higher luminance levels than the daylight autonomy values that were ​​simulated with the Daysim software for this study (as recommended by the old standard). Another factor that should be mentioned for the lab rooms is that although different people adjusted the systems, user requests for the room were respected, so it follows that the behavioral changes associated with these signs may also be related to this ‘position’ regarding system use in these rooms. We found that teachers who used the rooms during our observations encouraged their students to look at the signs. As for window use, we observed that they were fully or partially open on most observed days over the four phases in both the classrooms and the lab rooms. This confirmed the findings of Steemers, Yun and Tuohy (2009) that people interact with windows mostly upon entering a room. Similar to the use of switches, there was more interaction with the windows when occupants entered and left the rooms. We found that the type of room and the students’ position in the room influenced whether they operated the windows more or less often. Therefore, although teachers were usually the ones regulating the system, students tended to adjust them more during practical classes and when sitting near the windows, compared to theoretical classes and when sitting farther from the windows. In the lab rooms, those usually adjusting the windows were the first to arrive. In the classrooms, we found that although there was a desire to keep the ventilation pointing to all sides, in most classes, the doors were left open, and several times the teachers indicated that the reason for this was to keep the room better ventilated. Cross ventilation was not possible in the Labcon rooms due to their location on the first floor with windows facing a wall and doors facing the corridor. As for the use of the solar radiation control system, we found that in all environments it was more related to window use than to daylight availability, and the tendency was to prefer opening the blinds or curtains located in the upper part. Those making changes to the solar radiation control systems in the classrooms and Labcon rooms, were mostly those sitting close to them, which reinforces the conclusions of Crisp (1978) that users tend to act on systems according to the position of the controls. The changes made to the solar radiation control systems also reinforced the conclusion of Lindelof and Morel (2006) that in addition to changes made upon arrival and departure, users also change systems when they feel uncomfortable. Discomfort in this case was caused by the impact of solar radiation on users, the reflection on computer screens and the excess of natural lighting. In all the settings we analyzed, most changes to the solar radiation control system were made when there was an incidence of direct solar radiation in the rooms. Therefore, we found that favorable orientations relative to the incidence of direct solar radiation or protection from it significantly decreased the number of actions taken on the solar radiation control system. After installing Venetian blinds in the classrooms, we noted that they were a more complex system than the blackout curtains, which was verified by increased difficulties in using the system after installing them. The number of changes in the classroom systems also grew, primarily during class when an InFocus was used, due to the increased availability of natural light, which made ​​it hard to display projections on the wall, so Venetian blinds offered a more interesting alternative than blackout curtains.

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With regard to the best use of natural light, we found that since the School was designed at a time when InFocus projectors were not used in classrooms, some rooms had little wall space for projections (e.g. Room 320 A). The use of natural light was hampered once the blinds were installed, as they made it hard to read projected text, and it was continually necessary to change the blind settings. Despite the significant increase in the number of cases observed in which users had difficulties using the blinds, these problems declined after the signs were put up. This can be seen as an outcome of users adjusting to the system, the availability of information on system operation, or that users discovered the best settings for the blinds. After putting the signs up in the classrooms, the number of adjustments to the flap position of this solar radiation control system grew significantly, which meant an attempt to increase the availability of natural lighting in the room without reducing ventilation or solar radiation control. In the Comfort Lab, also with the aim of increasing available daylight, the number of adjustments to the blinds grew. Although these actions were usually taken by those sitting near the blinds, their behavior differed from that in the classrooms, which reinforced the conclusion of Reinhart and Voss (2003) that it is hard to create behavioral parameters for isolated individuals.

CONCLUSIONS The aim of this study was to analyze user behaviors regarding the use of natural and artificial lighting systems, solar radiation control systems and windows, by observing these behaviors, proposing improvements and testing the impact of those systems for use in other school buildings to enhance their energy efficiency. The general conclusions of this study include the following: • The recommendations for lighting systems by certification schemes - such as daylight contribution, divided circuits and having different ways to adjust the artificial lighting system in classrooms - contributed to improving the use of the overall lighting system and confirmed that not only did users influence the efficiency of the proposed systems, but also those systems influenced user behaviors. Due to this study’s methodology of implementing changes and re-evaluating the use of spaces by users based on ISO 50.001/2011 ‘Management Systems,’ we found the ‘continual improvement’ model to be an interesting way to enhance the quality of existing systems and to understand the relationships between system use and user behavior for application to other buildings. It is important to work with a continual improvement cycle, because user needs and preferences change and what was good yesterday is no longer good today. Understanding users is an on-going process that improves systems based on past experience. • The first user to enter an environment tends to be primarily responsible for adjusting the existing systems there. It is advisable to have information available on the installed system and offer simple, efficient user solutions to encourage behaviors, and it important for users to find suitable lighting, ventilation and solar radiation control conditions upon entering an environment. • We also found indications that when users are informed about building operations, they tend to act as expected. Although we cannot say that the graphics used in the signs were clear to all users, the informational signs brought benefits in terms of increased use of combined natural and artificial lighting, which was clear from the drop in the use of lamps next to windows during the final observation phase. The signs were not noticed when users first entered a room, but when leaving, users tended to read the signs and often made​​ meaningful changes such as turning off the lamps near windows. Since the same people tend to use the same room throughout the semester, one can see that they understood the contents of the signs.

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• We noted that one cause of doubts regarding the use of available systems was their lack of standardization. It is advisable to position these controls logically, as this will help users understand them. Although the new controls placed at the front of the classrooms (following the conclusions of the Crisp study and the LEED recommendations) did not have the desired outcome, we found that by altering the switch positions in room 318 we could influence different behaviors regarding which lamps were lit. • The use of solar radiation control systems that allow sunlight to enter the environment has proved an interesting alternative to encourage its use with artificial lighting, as observed after replacing the blackout curtains with Venetian blinds. • For the case under study, we found that the use of solar radiation control systems had more to do with using the windows than with increasing the sunlight in the room, so the blinds covering the movable windowpanes were used more than those covering the immovable ones. Therefore, one alternative that could be tested in other environments where solar radiation control is required would be to have one Venetian blind covering both windowpanes (movable and immovable), thereby encouraging users to open the control system on both sides of the window simultaneously. • Placing desk lamps at the worktables of each of the students and teachers in the Comfort Lab would also be a good alternative to ensure the individual comfort of users. Since some prefer more light than others, desk lamps are a good way to serve all users and achieve the needed brightness in the room, without having to modify the existing lighting system. Although the school would incur expenditures for this type of solution, we understand that by improving the quality of environments and meeting current standards, these changes can have several benefits for users and their productivity, which would produce future economies. • If users tend to act on existing systems upon entering and exiting rooms or when they are uncomfortable, it follows that the best projects are those that require fewer user adaptations. Therefore, when planning schools or other buildings, designers should be aware of the various factors that can cause users discomfort when they start to use the environments. They should select appropriate guidelines for each side of the building according to the activities to be carried out, and work with external elements such as Venetian blinds, lighted racks, or other types of protections on the facades to ensure the internal comfort of rooms without requiring internal protection devices that require user adjustments. Room 315 is a good example of this, with its openings towards the northern and southern sides of the building. The southern side is a suitable orientation for classrooms, as it offers ample sunlight with no direct solar radiation. Although the northern side receives daylight at certain times of the year, it was protected by Venetian blinds to control solar radiation. Therefore, even when a room has curtains, they are seldom used. Users can utilize the system but need not do so because they are not hampered by excessive solar radiation. Furthermore, having openings on two opposing sides ensures cross ventilation without having to keep doors open. Rooms such as 320A with unprotected openings on the western side receive intense solar radiation and increase the probability that users will adjust the system more due to discomfort caused by excessive solar radiation and a subsequent rise in temperatures. Solutions such as Venetian blinds would be quite appropriate for this room. The discomfort caused by the westerly orientation of rooms with no protection was also observed in the three Comfort Lab rooms, where the Venetian blinds were adjusted several times per day. Users raise the blinds to ensure ventilation and lower them to protect against direct solar radiation in the afternoons.

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Although installing Venetian blinds on the sides with direct solar radiation initially adds to construction costs, it is advisable in view of the benefits to the users of those rooms. Building efficiency depends not only on using efficient equipment, but also on the architectural design of the building. When projects are conceived holistically, they take better advantage of available daylight without using curtains or blinds, ensure the thermal quality of rooms without needing equipment for mechanical ventilation and without interrupting activities due to outside noise or other sources of interference. • We found that using InFocus projectors in classrooms can influence the use of lighting systems, and that sometimes excessive natural light interferes with projections. Using newer models of projection equipment that make text readable regardless of the amount of solar radiation in the room is a good alternative to the older equipment and does not require changing the architecture in order to use them. We recognize that projects are not static. Needs change, as do the users of each of the spaces. Therefore, for each modification to a completed project, proposals should be made to assess what other project features will be impacted by the change. Decision-making should consider compliance with existing legislation at the time of the change, the quality of modified environments and, especially, the well-being of their users, who should not simply be forced to adapt to the architecture as is. Changes made ​​to old and new projects should follow updated concepts regarding the role of users in architecture as described by Lino, Villela and Figueiredo (2009), for whom users are an essential part of all architecture. The process of understanding users is difficult, mainly because, as Mahdavi and Proglhof (2009) say, there is no typical human behavior and behavioral trends change constantly, which hinders standardization of any kind. However, this does not mean that we should ignore users, but rather that the task of understanding them must be implemented with each new project. Finally, we conclude that it is only by observing users that we can understand them, and only it is by understanding them that we can design good projects. The way projects are designed has an enormous impact on the way users act in a given building. We should bear in mind the basic assumptions of energy efficiency: users need good projects that provide conditions of comfort, safety and productivity, with solutions that encourage and enable both the rational use of energy resources and the use of natural lighting and ventilation.

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REFERENCES ASSOCIAÇÃO BRASILEIRA DE NORMAS TÉCNICAS: NBR ISO 50.001: Sistemas de Gestão de Energia: Requisitos com orientação para uso. Rio de Janeiro, 2011. ASSOCIAÇÃO BRASILEIRA DE NORMAS TÉCNICAS: NBR ISO/CIE 8995-1: Iluminação de ambientes de trabalho. Rio de Janeiro, 2013. ASSOCIAÇÃO BRASILEIRA DE NORMAS TÉCNICAS: NBR 5382: Verificação de Iluminância de Interiores. Associação Brasileira de Normas Técnicas. Rio de Janeiro, 1985. ASSOCIAÇÃO BRASILEIRA DE NORMAS TÉCNICAS: NBR 5413 – Iluminância de interiores. ABNT, Rio de Janeiro, 1992. BRASIL. Ministério do Desenvolvimento, Indústria e Comércio Exterior. Instituto Nacional de Metrologia, Normalização e Qualidade Industrial (INMETRO). Portaria Nº 372, September 17, 2010. Requisitos Técnicos da Qualidade para o Nível de Eficiência Energética de Edifícios Comerciais, de Serviços e Públicos (RTQ-C). Brasília, 2010. CRISP, V.H.C. The light switch in buildings. Lighting Research and Technology. 1978. Vol. 10, No. 2, pp. 6982. GROOT, E.; SPIEKMAN, M.; OPSTELTEN, I. Dutch Research into User Behaviour in Relation to Energy Use of Residences. PLEA. 2008 Oct. 1-5. Available at <http://architecture.ucd.ie/Paul/PLEA2008/content/ papers/oral/PLEA_FinalPaper_ref_361.pdf>. Accessed on Oct. 18, 2011. GYBERG, P.; PALM, J. Influencing households’ energy behaviour – how is this done and on what premises? 2009. Energy Policy, Vol. 37, No. 7, pp. 2807-2813. HERTZBERGER, H. Lições de Arquitetura: Martins Fontes. São Paulo, 2010. LINDELOF, D.; MOREL, N. A field investigation of the intermediate light switching by users. Energy and Buildings. 2006 Jul: 1-29. LINO, Sulamita Fonseca, VILLELA, Clarisse Martins, FIGUEIREDO, Cesar Augusto. Arquitetura sem “modo de usar”, 2009. Available at <http://cumincades.scix.net/data/works/art/sigradi2009_955.content.pdf>. Accessed on Nov. 02, 2012. MAHDAVI A, PROGLHOF, C. User Behavior and energy performance in buildings. IEWT. 2009: 1-13. Available at <http://ee.g.tuwien.ac.at/ee.g.tuwien.ac.at_pages/events/iewt/iewt2009/papers/4E_1_MAHDAVI_A_P. pdf>. Accessed on Oct. 09, 2011 MINISTÉRIO DO MEIO AMBIENTE – MMS. Eficiência Energética e Conservação de Energia, 2014. Available at <http://www.mma.gov.br/clima/energia/eficienciaenergetica>. Accessed on Feb. 20, 2014. NORMAN, D. A. O design do futuro. Rio de Janeiro: Rocco, 2010.

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PROCEL/ELETROBRÁS. Manual para aplicação dos Regulamentos RTQ_C e RAC-C, V. 4, 2009. 159p. Available at <http://www.eletrobras.com>. Accessed on Jul. 01, 2013. REINHART, C. F.; VOSS, K. Monitoring manual control of electric lighting and blinds. International Journal Lighting Research & Technology, 2003; 35 (3): 243-260. STEEMERS K. Sustainable Design and Well-Being. SHB2009. 2009 Feb: 173-179. Available at <http:// www.sustainablehealthybuildings.org/PDF/9.%20Koen%20Steemers.pdf>. Accessed on Mar. 27, 2012. U.S. GREEN BUILDING COUNCIL. LEED Reference Guide for Green Building Design and Construction: For the Design, Construction and Major Renovations or Commercial and Institutional Buildings Including Core & Shell and K-12 School Projects. Washington, 2009. 645p.

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BRAZIL ENERGY EFFICIENCY IN THE ELECTRICITY INDUSTRY PLANNING FOCUSED ON CO2 EMISSIONS LUIZ FILIPE ALVES CORDEIRO Orientation: Professor Ronaldo R. B Aquinas

ABSTRACT The energy issue is presently in focus worldwide. This work describes the application of modeling, control, and artificial intelligence to improve energy efficiency in pump systems. This Artificial Intelligence approach can be applied to industrial systems in order to reduce the energy consumption. Among the contributions of this work is investigate the problem of emissions of greenhouse gasses (particularly carbon dioxide) derived from a thermoelectric generation in Brazil. For this, was projected the simplex optimization model as a way to determine the carbon dioxide (CO2) emissions that are found from the optimization of the order of plants. For this, we used data from the decennial program of power sector expansion and compared the scenarios under study regarding the optimization of emissions and total cost of power generation.

KEYWORDS: Energy Efficiency, CO2 Emissions, Power Generation Optimization, Artificial Neural Network.

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INTRODUCTION Climate change is a natural phenomenon and has periods of intense change during Earth history. However, the rate of climate change in recent decades is considered by scientists as an unusual phenomenon. During the Treaty of Copenhagen (2009), a critical statement was made: “Climate change is one of the greatest challenges of our time [1]. Different studies confirm that the elevation of the average temperature of the Earth and rising sea levels by melting frozen areas are evidence of intensified greenhouse gasses. According to the Intergovernmental Panel on Climate Change (IPCC) [2], emissions of greenhouse gasses from the activities of the company doubled in the past four decades. Of all of these emissions, almost 80% is carbon dioxide, which at this time raised from 21 to 38 gigatons (Gt). Increased emissions of equivalent carbon dioxide was higher in the last two decades than in the first two. It is worth mentioning that the sectors that contributed most to the increase in emissions were energy, transport, and industry. Currently, studies suggest that the planet is close to 50 Gt CO2 and can reach 61 Gt in 2020 and 70 Gt in 2030. And so, in March 2009, at the UN Conference on Climate Change in Copenhagen [1], governments collectively decided that the world needs to limit the increase in global average temperature in no more than 2 degrees Celsius and international negotiations are committed to this end [3]. As energy and industry are the sectors contributing most to the increase in emissions, the main drivers of energy policy are made in the coming decades [4]. In this regard, we take into account that globally, the EU is leading in taking measures to mitigate climate change [5] so it was established the so-called 20-20-20 agreed reduction targets: (1) Reducing CO2 emissions by at least 20%; (2) increase the share of renewable energy in 20%; (3) Reduce its energy consumption by 20% through 2020. At the national level it is perceived that Brazil, at the time the Kyoto Protocol was taking place, was not required to adopt targets for reducing emissions of greenhouse gasses, however, this does not exempt it from participating in the global mitigation effort. If so, it is found that some specific studies of great importance for this sector in Brazil were those conducted by MCKINSEY [6] AND LA ROVERE [7]. Both indicate potential to reduce emissions in the medium and long term for some subsectors and estimated costs reduction. Although these works are very rich and provide a starting point for the discussion and analysis of mitigation measures in the sector, they could not possibly be deepened in some detail by the variety and complexity of the sectors. Recalling a brief history, we find that in 1979, it was held the First World Climate Conference that pointed out the need for cooperation among nations to develop a global strategy and understanding of the functioning and rational use of the climate system. In 1989, it was created the Intergovernmental Panel on Climate Change (IPCC) to provide governments with a clear scientific view on what is happening to the global climate. In 1992 it was created the United Nations Framework Convention on Climate Change, UNFCCC, which brought together the countries to join efforts to stabilize concentrations of greenhouse gasses (GHGs) in the atmosphere, at a level that does not interfere with the climate system, slowing global warming and its possible impacts.

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In 1997, the creation of the Kyoto Protocol, which brought real commitments. It established that the countries included in Annex I of this Protocol must reduce their GHG emissions by at least 5% below 1990 levels in the period between 2008 and 2012. In 2009 it was instituted in Brazil, the National Climate Change Policy (NCCP), through Act nยบ 12.187 / 2009, which seeks among its objectives, to support the economic and social development with the protection of the climate system and reduction of Emissions of greenhouse gases in relation to different sources, defining voluntary commitment to adopting national mitigation actions with a focus on reducing emissions of greenhouse gases (GHGs) between 36.1% and 38.9% compared to projected emissions by 2020. From the above, it is perceived a significant flaw in the validation of existing mitigation potential in Brazil, more specifically in the electricity sector. Further investigation could range from simple measures of energy conservation and energy efficiency in energy consumption and more complex possibilities allowing the reduction of GHG emissions in the medium and long term. Another important flaw is the analysis of planning the expansion of the Brazilian electrical system, allowing not only the security issues of reliability but also the minimization of CO2 emissions by the use of cleaner energy matrix. Then, to contextualize the Brazilian reality is of utmost importance to analyze the current Brazilian situation regarding emissions of carbon dioxide (CO2) in the generation and consumption of electricity. Starting with the generation, in Table 1.1 is presented the annual average factor of the National Interconnected System (SIN), for a more detailed analysis of the emissions from power generation from fossil fuels. Table 1.1 Annual average factor of SIN Year

2006

2007

2008

2009

2010

2011

2012

2013

2014

Average annual factor (gCO2 /kWh)

32,32

29,18

48,38

24,50

51,28

29,20

65,34

96,03

135,49

20.128

18.669

36.489

16.307

37.497

25.982

53.405

93.104

125.635

Power generation from fossil fuels (GWh / year)

When analyzing Table 1.1 is stated that due to the increasing use of electricity generation from fossil fuels, the average annual CO2 emission factor has increased considerably in recent years. From 2009 until 2014 it has increased by 450%. That is, the generation of the Brazilian electric sector is increasingly emitting carbon dioxide. In relation to consumption, in Figure 1.1 it is also presented the historical series of CO2 emitted by the electric power sector in Brazil.

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Electric Power Consumption 500,000 450,000 400,000 350,000 300,000 250,000 200,000 150,000 100,000 50,000

National

2011

2008

2005

2002

1999

1996

1993

1990

1987

1984

1981

1978

1975

1972

1969

1966

1963

1960

0

Industry

Figure 1. 1 - Electricity consumption in Brazil over the last decades Source: Prepared from EPE/BEN/2014 In Figure 1.1, we can see that the consumption part that is also constant to the emissions growth in the area of electricity ​​ consumption in Brazil. In summary, we have a picture that warrants further study, namely: • Brazil needs to stop the emissions of gasses that cause global warming, concerning those emissions from thermal generation. • On the consumption side, the industrial sector has an important job in the country, and there are indications that there is a high potential for mitigation. • An urgent objective is a need for energy and environmental security. With this, the project proposes the optimization of the Brazilian electrical system in a generation, optimization based on CO2 instead of costs as it is currently done. In addition, in the area of ​​consumption, the use of neural network techniques related to energy efficiency in industry.

METHODOLOGY GENERATION The daily operation of a power system covering the operation of hydropower, thermal and wind power companies. Due to the large size of the electrical systems, the distribution of energy is an extremely complex task and can be efficiently performed, seeking lowest cost and highest security level, with the aid of a Wind - Hydrothermal Optimal Distribution program ( DHO). The National Interconnection System (SIN) consists of

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power generation and transmission systems. The generation system is decentralized due to the large size of the country. As such, large power generating sources are distributed throughout the country. The planning of the operation of an electrical system aims to define a strategy for each generation company that minimizes the expected value of the operating costs in the planning period [8]. Operating costs refer to expenses with fuels at thermoelectric companies; no attention costs to the load and possible nearby systems power purchases (exchange). The close interconnection between systems allows a reduction in operating costs through the exchange of energy and increase of the reliability of supply and through the distribution of reserves [9]. If a system has the highest operation cost than the closest one, the most economic solution would be to transfer the energy from the system with a cheaper operation cost for the system that has a higher operation cost. The energy exchanges between systems and thermal companies result in the global optimization of the operations costs of interconnected systems. This paper presents a DHO computer program, developed by the Search and Development project “Optimization Model for Energy Simulation of Wind Energy and Other Sources in northeastern Brazil” as a joint work between CHESF (Hydroelectric Company of San Francisco) and UFPE (Federal University of Pernambuco). DHO problems are formulated in this project as linear programming problems (LP). Due to the successful history of the interior point, methods (PI) in the solution of problems of large-sized PL [10, 11] DHO problems are solved by algorithms Primal-Dual Simplex and Primal-Dual Predictor Corrector IP. This project contributes to the development of a DHO computer program and optimization function of “CO2” as well as in the formulation of DHO problems and their solution through PI algorithms. CO2 optimization In this project, we have defined a methodology to measure CO2 emissions by fuel type, using the criteria adopted in the report of the IPCC (Intergovernmental Panel on Climate Change), according to Table 2.1. The objective function adopted in this case is to minimize the value of the CO2 from thermal generation and deficit. Carbon dioxide emissions were quantified to estimate emission values ​​for thermoelectric companies. Similarly, the optimization of thermal generation costs and deficit used the dhoVisual. Table 2.1 Emission factor by Fuel Type UNIT

TJ/UNIT

Carbon Emission Factor

Fraction of carbon oxidation

Emissão (tCO2/Un)

tC/KWh

tCO2/KWh

1000m³

35.52

20.2

0.99

2604.54

72.72

65,34

Fuel Oil

106l

40.15

21.1

0.99

3075.21

75.96

266.64

Carbon

1000t

11.93

26.2

0.98

1106.01

94.32

278.52

Natural Gas

106m3

36.84

15.3

0.995

2056.39

55.08

345.84

FUEL Diesel

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Objective Function min EMISSIONS = min ∑Tt=1 λt • [ ∑Jj=1 ETj (GTj,t) + ∑Ss=1 EDs (DEFs,t)] λt =

1 (1+β)t

Where: λt : Coefficient of present value for the period t; GTj,t : Thermal Plant generation i during the period t [MWmes]; ETj : Emissions of thermal Plant j for period t [CO2]; EDs : System deficit emissions s [CO2]. The E functionTj is a function representing Emissions from thermal company, which depends on the type of fuel used by the company and it will be approximated by a quadratic polynomial. The “environmental” value of energy deficits represented by the variable EDs , the cost function of the deficit emission subsystem [CO2] should represent the impact caused by not meeting the demand for energy in different economic activities of the country, this cost is represented by a quadratic polynomial obtained by quadratic approximation of piecewise linear function defined by the NEWAVE [12]. Similar to the optimization of the cost function, restriction of the water balance and care demand is made in the same way. With the above tool, the National Operator System (ONS) will be able to operate the system in order to minimize the CO2 emissions instead of the current operation that allows lower cost and security. It should be noted that simulations with both types of optimizations for comparison would be performed during the results session. Finally, simulations will be made with reduced energy consumption by 5% to check how the system would behave in case of energy efficiency policies implemented in consumption, as proposed in this project. CONSUMPTION Energy efficiency improvement is considered as the fastest and cheapest way to reduce CO2 emissions; it is seen as one of the most promising measures for reducing CO2 global emissions [13]. Moreover, based on successful examples such as the European Union, whose electricity savings potential in the tertiary and industrial sectors are of 8 Tw / year by 2015, with investors applying loads like fans, pumps, compressors and conveyors bands [14 and 15].

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From what is stated above, to promote actions aimed at minimizing emissions of CO2 through the use of energy efficiency, experiments were developed at the Efficiency Laboratory on Drive systems - LAMOTRIZ. The proposal is for a method of energy conservation and energy efficiency in the highest energy consuming sector in the country: the industry. In this sector, the highest energy consumption system is the pump system. Thus, directly contributing to the goals of reducing emissions of CO2. Significantly, relevant results were obtained [16, 17 and 18]. It proposes that the industrial sector in Brazil still has great potential for reducing energy consumption and consequently emissions. In this proposal, it is worth mentioning the results of UFPE LAMOTRIZ in Pumping Systems. Initially, the methodology consists in replacing the flow control of the valve system throttling by using frequency inverters. In the part concerning the results, it is perceived that the simple valve replacement throttling by frequency inverters can bring considerable energy economic increase. Finally, it is worth noting that since our objective is the reduction of GHG, no other details will be discussed such as reduced starting current, minor damage pressure in the pipeline, financial economics, reduced system maintenance, etc. In search of higher profits, at LAMOTRIZ, Artificial Intelligence techniques were applied with the aim of auxiliary flow control frequency inverters. Considering the industrial sector is responsible for almost half of the CO2 emissions in the Brazilian electrical system, it is concluded that investment in this sector, with regards to technology, can get a considerable reduction when used in industrial systems with progress such as the use of frequency inverters. Thus, the proposed mitigation of CO2 involves introducing the measures proposed above within industries.

RESULTS RESULTS IN THE GENERATION Using the Visual DHO, the scenario was chosen with the market beginning in 2013, and simulating five years, with the PDE 2022 operating database, and choosing some hydrology, such as 1949 ~ 1953 in the case of 5 years for the title analysis of energy scenarios, the results obtained are shown in the behavior of Marginal Cost of Operation (CMO), which is the variable operating cost necessary to meet 1 MWh additional demand, using existing resources. Figure 3.1 presents a comparison of Marginal Cost of Operation between the normal market and market preserved by 5%. In this case, the optimization is being performed by cost.

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Marginal Operating Cost (1949-1953)

Figure 3.1 - Marginal Cost of Media concerning the hydrological period 1949-1953

Analyzing Figure 3.1, specifically the blue line, we should note that the hydrology of 1949 ~ 1953, has high CMO, high CMOs are reflections of low storage verified in the SIN, and since May 2016 the CMO, will considerably rise for values up to R $ 2,200.00 (Royals Brazilian currency) / MWh. It is noteworthy that, as reported, these are average values, but in some years the CMO has reached the value of 3,100.00 R $ / MWh, remaining until the end of the study period; this value represents the cost of deficit; this is the hydrothermal system which corresponds to the hydrological condition, unable to meet an existing electricity demand. On the other hand, when analyzing the green line in Figure 3.1, we can see lower values reaching almost five times lower in critical periods. It is relevant to report that no deficits would be obtained in the period if the market was reduced by 5% through ways to conserve energy efficiencies. Following the optimization model of operating costs of thermal companies and deficits discussed above, where the Marginal Cost of Operation (CMO) is verified; It will be adopted for the purposes of this thesis, for the optimization, such as costs, using CO2 emissions of the respective thermal companies, which we have called the Marginal Operation Issue (EMO). It is noteworthy that the EMO will be critical in this project to validate the results of simulations of the various cases studied, always seeking to plan the power system with greater environmental safety, this is, minimizing the emissions of carbon dioxide. From Figure 3.2 we can see the comparison in the case of CO2 emissions, it is emphasized that in this case it was used the optimization of DHO for CO2, according to the methodology as described in the previous chapter.

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Operation’s Marginal Emission (1949-1953)

Figure 3.2 - Marginal Emissions of Media concerning the hydrological period 1949-1953 Through the analysis of Figure 3.2, we note that for this dry hydrology, CO2, will considerably rise from May 2016, reaching high values ​​of the order of 7.4 tCO2 / MWh. Furthermore, when viewed 5% conserved market, it is perceived that these values fall ​​ considerably reaching twice less CO2 emissions. After the analysis through energy conservation, it was carried out the assessment of some simulations in Visual DHO. To enrich our study, a simulation was conducted choosing a ten year time period hydrology. The decade of 1946-1955 has been chosen for this case, according to Table 3.1

Table 3.1 Holding Market Comparison YEAR

CONSERVATION ENERGY

Alternative Cost

Economy (%)

tCO2 Emission

1946 - 1955

NORMAL

1.55x1012

5%

5.95x1011

62%

8.34x1014

10%

3.21x1011

79%

5.27x1014

1.13x1015

From Table 3.1, we conclude that total cost R $ (Real) has been significantly reduced with the market economy reaching levels of 70% reduction. It is also highlighted the significant reduction of CO2 emissions. That is to say, it is extremely important for the mitigation of CO2 in Brazil a higher investment in energy efficiency policies. By analyzing the decade of 1946-1955, we can perceive once again, considerable values ​​of CO2 costs and emissions.

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Table 3.3 Summary of CO2 optimization for the decade 1946-1955 YEAR

ENERGY CONSERVATION

Alternative Cost

Growth R $ compared to cost optimization

tCO2 Emission

CO2 Economy compared to cost optimization

1946 - 1955

NORMAL

1.70 x1012

0.15 x1012

1.08x1015

0.05x1015

5%

7.76x1011

1.81x1011

7.90x1014

0.44x1014

10%

3.43x1011

0.22x1011

4.97x1014

0.30x1014

From Table 3.3, it is perceived that by optimizing CO2, the cost can rise, but there is also a considerable reduction in emissions of CO2. It is noted that considering the normal market, just for the optimization of CO2 , avoided emissions were the order of 0.05x1015. Meanwhile the cost has increased in the order of 0.15x1012.

Operation’s Marginal Cost (1949-1953)

Figure 3.3 - Comparison of Marginal Cost of Operation between markets

To be more specific, in Figure 3.3, we see that the normal system is appreciated in the purple line while the green and blue curve shows the system with the conservation of 5 and 10% respectively. As expected, the value of the operation cost for the conservation scenarios are less than the normal scenario. The Marginal Cost of Operation dropped in the order of R $ 2,200.00 for values below ​​ R $ 100.00. In Figure 3.4 it is presented what we call Marginal Issue Operation (EMO).

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Operation’s Marginal Emission (1949-1953)

Figure 3.4 - Marginal emission operation between Scenarios Another way to visualize the importance of energy conservation is in Figure 3.4. As is visible mitigation of CO2 falls from almost 8 tCO2 / MWh to 1 tCO2 / MWh. This involves minimizing environmental damage. CONTROL RESULTS Initially, the results presented in Table 3.4 have to do with in replacing the flow control of the throttling valve system through the use of frequency inverters. Table 3.4 Economy Rates using frequency inverters instead of Strangled valves Strangled valves

Economy

30%

6%

50%

39%

70%

74%

It is perceived that the simple throttling valve replacement by frequency inverters can bring considerable energy economic increase. It is also important to say that when this change is applied in large industries, it can bring countless economic energy profits and consequently less CO2. As such, it appears that if similar techniques are applied to this, in industries that still use the throttling valve control, emission reductions in the systems of up to 70% could be obtained. Table 3.5 presents the second analysis concerning consumption. In this case, the comparison is made not only by the use of the frequency inverter but also with artificial intelligence tools aimed to energy efficiency.

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Table 3.5 Comparison of energy consumption for 3m reservoir with and without RNA. Volume (L)

Energy Consumed (s/RNA) (Wh)

Energy Consumed (c/RNA) (Wh)

Economics (Wh)

%

50

28

19

9

32,1

100

56

38

18

32,1

150

83

57

26

31,3

200

110

76

34

30,9

300

165

112

53

32,1

400

219

148

71

32,4

500

275

188

87

31,6

The data obtained through actual experiments show a significant reduction in energy consumption. In percentage terms, the RNA control system gains are around 30%. You may also notice that the economy of energy (W.h) had a significant increase in increasing required volume. That is, in large systems (thousands of gallons) energy savings will be significant. Considering that the industrial sector is responsible for almost half of the CO2 emissions in the Brazilian electrical system, it is concluded that investment in this sector, with regards to technology, can get a considerable reduction of up to 30% when used in industrial systems with progress such as the use of frequency inverters. It should also be noted that if compared to the flow control traditionally used in the industry (throttling valve) this energy saving is greater to obtain the same flow, according to Table 2.4 Table 2.4 Comparison of energy consumption for the reservoirs to 3m RNA and traditional control (throttle valve).

196

Volume (L)

Energy Consumed (Throttling valve) (Wh)

Energy Consumed (c/RNA) (Wh)

Economics (Wh)

%

50

40

19

21

52.5

100

85

38

47

55,3

150

121

57

64

52,9

200

170

76

94

55,3

300

252

112

140

55,6

400

335

148

187

55,8

500

419

188

231

55,1

Eco_Lรณgicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.


The data presented in Table 2.4 shows high energy savings when applied to the smart control instead of the traditional control (throttle valve) resulting in a saving of approximately 55%. As it can be seen when a comparison is made with systems without frequency inverters (very common in Brazilian industry), CO2 reduction exceeds 50%. It is important to state that, although the results have been obtained from pumping systems, the idea of ​​efficient control with neural networks can be implemented in any industrial system.

CONCLUSION The initial objectives of this thesis proposal were achieved. Initially, an analysis of the current Brazilian electrical system in its main aspects has been made, verifying both energy generation and environmental safety; and consumption through the analysis of the steady and gradual growth of the load and consequent increase in emissions of greenhouse gasses. Some simulations for the generation and consumption of electricity were implemented, and it is clear that if the current projections go on, the trend is that every year that passes by, there will be a significant increase in the emissions from the generation and consumption. With regard to consumption, it clearly reflects that there is still great potential for savings of electricity through the use of energy efficiency techniques such as simulated proposals in LAMOTRIZ pumping system and it can be used as a prototype evidence of large industrial processes that seek to increase energy efficiency and improve the quality of energy of these systems. It is worth mentioning that all the work of consumption mitigation can be implemented in industrial drive systems in general, such as compressors and extractors. In this case, from the above-discussed results, we find that it is feasible a considerable reduction in CO2 in the electricity sector by adopting efficiency measures applied to industrial plants. That is, the mitigation of CO2 consumption of electricity depends on decision makers, since the potential of this sectors is high in the contribution to the reduction of carbon dioxide. Finally, concerning generation, it became clear that a good energy planning scheduled for CO2 optimization can bring significant gains in the environment. According to the results, it became clear that mitigation can occur due to the expansion of the park of nuclear energy as well as for the optimization of CO2. This will provide subsidies to decision makers to choose the least polluting thermal company.

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BIBLIOGRAPHICAL REFERENCES COPENHAGEN ACCORD - The United Nation Climate Change Conference. In: Copenhagen, 2009. IPCC – INTERGOVERNMENTAL PANEL CLIMATE CHANGE. Cambio climático: informe de síntesis. Genebra, Suíça, 2007. IEA - INTERNATIONAL ENERGY AGENCY. World Energy Outlook 2013. Paris: OECD/IEA, 2013. IEA - INTERNATIONAL ENERGY AGENCY. World Energy Outlook 2009. Paris: OECD/IEA, 2009 IEA - INTERNATIONAL ENERGY AGENCY. World Energy Outlook 2008. Paris: OECD/IEA, 2008. MCKINSEY. “Caminhos Para Uma Economia de Baixa Emissão de Carbono no Brasil”. McKinsey & Company, 47p, 2009. LA ROVERE, E., PEREIRA, A., SIMÕES, A., PEEIRA, A., GARG, A., HALNAES, K., DUBEUX, C., COSTA, R., “Development First: Linking Energy and Emissions Policies with Sustainable Development for Brazil”. UNEP – RISØ Centre, 88p, 2007. L. A. M. Fortunato, T. A. A. Neto, J. C. R Albuquerque, e M. V. F Pereira, “Introdução ao Planejamento da Expansão e Operação de Sistemas de Produção de Energia Elétrica”, Universidade Federal Fluminense, Brasil, 1990 CEPEL, “Modelo Decomp Manual de Referência”, Versão 11.0, Centro de Pesquisa de Energia Elétrica, Brasil, 2003. N. A Karmarkar, “A New Polynomial Time Algorithm for Linear Programming”, Combinatorica (4): 373–395, 1984 M. H Wright, “The interior-point revolution in optimization: History, recent developments, and lasting consequences”, Bulletin of The American Mathematical Society 42(1): 39–56, 2004 LATEC; Relatório técnico 4: Desenvolvimento dos projetos piloto. Otimização do despacho hidrotérmico através de algoritmos híbridos com computação de alto desempenho. Instituto de tecnologia para o desenvolvimento; Disponível em : http://www.dhs.ufpr.br/pesquisas/Projeto%20PHOENIX/ Relat%F3rios%20T%E9cnicos/Rel4_rev2.pdf; Acesso em 31 de março de 2015. SAVOLAINEN, A., “Hacia um futuro major” Revista ABB, p.34-38, 2004. ALMEIDA, A. T., FERREIRA, F. J. T. E. and BOTH, D., “Technical and Economical Considerations in the Application of Variable-Speed Drives With Electric Motor Systems”. IEE Proc.-Electr. Power Appl. Vol 41, N°1 January/February p. 188-199, 2005. DE ALMEIDA, A. T., FERREIRA, F. J. T. E., FONSECA, P., CHERITIEN, B., FALKNER H., REICHERT, J. C. C., WEST, M., NIELSEN, S. B. and BOTH,D., “VSDs for Eletric Motor Systems”.

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AQUINO, R. R. B., LINS, Z. D., ROSAS, P. A. C., CORDEIRO, L. F. A., RIBEIRO, J. R., AMORIM, P. S., TAVARES, I. A., “Eficiência Energética no Controle e Automação de Processos Industriais”. Eletrônica de Potência, v. 14, p. 117-123, 2009. AQUINO, R. R. B., LINS, Z. D., ROSAS, P. A. C., CORDEIRO, L. F. A., RIBEIRO, J. R., TAVARES, I. A., AMORIM, P. S., “Eficiência Energética em Métodos de Controle de Vazão”. Eletricidade Moderna, v. 425, p. 84-93, 2009. AQUINO, R. R. B., LINS, Z. D., ROSAS, P. A. C., CORDEIRO, L. F. A., RIBEIRO, J. R., TAVARES, I. A., AMORIM, P. S., “EFICIENTIZAÇÃO ENERGÉTICA EM MÉTODOS DE CONTROLE DE VAZÃO”. In: VIII Conferência Internacional de Aplicações Industriais, 2008, POÇOS DE CALDAS. INDUSCON, 2008.

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BRAZIL ENERGY RECOVERY FROM URBAN SOLID WASTE: CASE STUDY IN THE MUNICIPALITY OF ITANHAEM, SAO PAULO LUIZ HENRIQUE TARGA GONÇALVES MIRANDA Advisor: Suani Teixeira Coelho, PhD

ABSTRACT

Rapid technological and economic development promoted by current generation’s big wastage, which results in a huge waste generation. The Brazil produces about 209 thousand tons of waste daily, the big issue is that only 190 thousand tons are collected and those collected from only 60 % hold a proper destination. Not least, the capabilities of traditional systems of provision are already reaching its limit, requiring alternative for the final disposal. Why this concern that National Policy on Solid Waste came into force requiring all municipalities to develop a Plan of Integrated Solid Waste Management. The biggest concern is with the municipalities of small and medium where there is no knowledge and no specialized staff on the subject, requiring external consultants. There is still another issue involving the coastal municipalities of the State of Sao Paulo, where they need to travel hundreds of miles to dispose of their waste properly. This occurs with the Country of Itanhaem, considering medium sized, with the need to have their residue 110 km away from the generation, leading great expense to the county. Thinking of a way to solve this issue, this paper aimed and developed technical feasibility studies resulting in gasification technology as it fits more to the case study in question and provide the proper disposal of municipal solid waste and generate energy electricity capable of powering 4,730 homes - equivalent to 22 % of the population of Itanhaem. KEYWORDS: solid waste, energy recovery, gasification

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INTRODUCTION In today’s society, we apply higher standards to the products we consume, which includes a growing use of products with shorter life cycles and disposable packaging, thus generating a huge amount of urban solid waste (USW). The capacity of traditional disposal systems has reached its limit, leading to the need for alternatives for the end disposal of goods after they have been consumed (Gonçalves, Tanaka and Amedomar, 2013). According to data from ABRELPE (2013), daily waste generation in Brazil grew 4.1% between 2012 and 2013 (Figure 1). Similar growth was seen in waste collection, at 4.4% (Figure 2). Even so, around 20,000 tons of waste are generated on a daily basis that are not collected but end up in unknown disposal sites, although wastes dumps are likely one of such places. It is important to point out that the collected USW are not necessarily taken to correct waste disposal points, as seen in Figure 3, which shows that 41.74% of the waste generated in Brazil is not properly disposed of. USW Generation (t/day)

USW Recollection (t/day)

Final destination in 2013 (t/day) APPROPIATE 58,26% 110.232 t/day

INADEQUATE 41,74% 78.987 t/day

Figure 1 - Generation of Urban Solid Waste Source: ABRELPE, 2013.

Figure 2 - Collection of Urban Solid Waste Source: ABRELPE, 2013.

Figure 3 - End Disposal of Urban Solid Waste in Brazil Source: ABRELPE, 2013.

All of the waste tossed into controlled waste dumps and landfills causes major impacts on public health and the environment, releasing noxious gases, damaging the soil, and polluting water bodies due to the lixiviates generated in them; all of this in addition to proliferation of transmitters. As a result of this concern, the new National Solid Waste Policy (PNRS from the Portuguese) (Law No. 12,305, August 2, 2010) establishes the “guidelines related to integrated management and administration of solid waste (including hazardous waste) as well as the responsibilities of waste generators and public authorities regarding applicable economic instruments.” The law establishes direct and indirect liability for solid waste management. Under these terms, when hiring services for collection, storage, transportation, transfer, treatment, and final disposal of solid waste, such persons are not exempt from liability related to preparing a Solid Waste Management Plan in terms of damages that may be caused due to inappropriate waste management. Therefore, the municipality will also be liable for damages to the environment or to third parties due to disposal of solid waste generated in its territory.

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In terms of correct end disposal, municipalities have a few alternatives, some of which combine complementary alternatives and others that are used on their own, such as: • Recycling + complement; Composting + complement; Landfills; Anaerobic digesters; Incineration; Pyrolysis; Gasification; Plasma; Clinker oven processing. Recycling is a reality that has been adopted by the great majority of municipalities, whether using “eco points,” selective collection, or using collectors. The added value obtained from such waste becomes an income source for the municipality or community. Composting, in short, is the decomposition of organic material, which can then be used as fertilizer in postprocessing. A landfill is the quickest and most commonly used method for disposing of solid waste. However, landfills are more and more overloaded, leading to the need for alternative waste destinations so that only the parts of waste that cannot be salvaged are sent to landfills. The other ways to dispose of USW are using internationally prevalent technologies. However, these are new to Brazil, and only have a few plants installed, mainly due to a lack of technical knowledge and the high cost of technology, since the equipment has to be imported. Due to the need to involve numerous points in the analysis to assess the most sustainable alternatives, no USW destination can be considered the best option for all cities and regions. This leads to the need to do a case-by-case analysis (Lanziani, 2013). Based on the above list of waste management alternatives, in the seven last technologies, there is the possibility of recovering the gases generated in the residue decomposition (specifically, landfills, anaerobic digesters, pyrolysis, and gasification) and then burning it or using thermal recovery (incineration, plasma and processing). Due to the heat exchange, in both cases energy is generated as one of the end products of the process. This energy recovery from USW would not only add value to its life cycle, but also contribute to electricity generation in Brazil, where the situation is worrisome. According to Pimentel (2014), some factors that intensify and justify these concerns arise from the fact that our energy grid is mainly supplied by hydroelectric plants. In recent years, the hydrological situation has been unfavorable, as hydroelectric storage levels have been low, added to delays and cancellations of infrastructure projects. Many of the planned hydro electrical plants that are either in progress or have been completed, do not have water reservoirs, and the complementary thermal companies are working around the clock, when they were planned to work only in peak consumption periods. This shows that our energy grid has reached levels of demand in which the generators that were planned to cover this need are unable to do so. It is clear that there is warranted concern related to energy supply, which has negative issues that are harmful to everyone, and therefore it makes sense to decide on generation alternatives that fall under the renewable category, in addition to self-production and distributed generation. It is important to consider whether this will be the new configuration of the national grid, which would lead to more expenses, since studies done by EPE show a growing trend in electrical consumption, as shown in Figure 4.

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TWh

∆% 2013-2050 → 3,2% a.a.

2.000 Autoproducción

1.500

1.624

Consumo Red

1.285 965

1.000

500

0

513

685

463

604

2013

2020

860

2030

1.165

2040

1.495

2050

Figure 4 - Total Electricity Consumption, 2013-2050 (TWh) Source: EPE, 2014. It is important to note that this forecast takes into account improvements in energy efficiency, an increase in solar heating of water, new use of electrical equipment that are able to create a self-supply of electricity (mainly solar), introduction and expansion of a “smart grid” and significant growth in self-production. While the industrial sector is the largest electricity consumer, the residential sector will show significant growth in energy consumption, growing by 6.2% as compared to 0.2% in the industrial sector, and 4.8% in all other sectors put together (public, agriculture, commercial, and transportation) (BEN, 2014). Another sector that may show some surprises and has not yet been seen as a potential consumer will be transportation. This change will be more detectable with electric and hybrid vehicles that become more and more prevalent on the road, as shown in the Figure 5 forecast (on the next page). Based on this opportunity for recovering energy from urban solid waste, with the technologies available in the market, and the National Solid Waste Policy (PNRS), this paper takes part the discussion and provides ideas to be applied mainly in small municipalities, which are most affected by the lack of waste management and administration, and which lack specialized technical staff. With the PNRS and the historical problem of waste management and administration in the coastal area of the State of Sao Paulo (with numerous notifications, fines by the public administration, and prohibition on many controlled waste dumps and landfills in these regions, which are required to haul their waste many kilometers away from their place of origin), the objective of this project is to prepare a study about energy recovery for one of the municipalities located in the coastal area of the State of Sao Paulo, specifically, the Itanhaem Municipality.

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100%

3%

90%

12%

80%

32%

9%

85%

70%

52%

60% 50% 40% 30%

7% 66%

20%

32%

Light-fleet cars*

10% 0%

2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050 Flex Fuel * Diesel light commercial vehicles excluded

Gasoline

Ethanol

Hybrid

Electric

Figure 5 - Profile of Automobiles by Fuel Type Source: EPE, 2014

OBJECTIVE GENERAL OBJECTIVE The objective of this monograph is to analyze the feasibility of building an energy recovery system based on urban solid waste in the Itanhaem-SP Municipality, to discuss the existing difficulties, and make proposals. SPECIFIC OBJECTIVES • Analyze the current scenario of urban waste generation in the State of Sao Paulo and in other municipalities. • Identify the technological alternatives related to treatment and generation of solid waste. • Analyze the maturity of the chosen technologies. • Verify feasibility of the chosen technology within the municipality’s waste generation reality. • Identify potential municipalities to replicate the case study. • Present policy proposals.

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CASE STUDY FOR ENERGY RECOVERY OF USW IN THE ITANHAEM MUNICIPALITY/SP METHODOLOGY This project is based on exploratory research complemented by the case study technique in the ItanhaemSP Municipality. The objective is to tackle questions regarding energy recovery from USW and its correct disposal. As a result, the following methodology has been applied, which was prepared using different stages. 1. Analysis of generation and destination of urban solid waste in the State of Sao Paulo Analysis of waste generation and destination was made possible by exploratory investigation done through government documents, articles, theses, books, specialized sites, and other sources available on the internet. 2. Selection of a municipality for the case study After analyzing the situation in the State of Sao Paulo, the Itanhaem municipality was specifically selected in order to make a case study on energy recovery of USW. 3. Analysis of urban solid waste generation in the chosen municipality Using the municipality chosen for the case study, a diagnosis was done on the management and administration of USW related to its generation, classification, quantification, destination, and end disposal in order to identify any potential energy recovery from such residue and the best technology to apply. 4. Identify the technological alternatives for energy recovery of urban solid waste. Possible existing technologies have been identified for recovering energy from solid waste. 5. Selection of a referential technology for the case study With the compilation of items 3 and 4, a technology was chosen that best fit the situation of the case study municipality. 6. Verification of feasibility of installing the technology in the chosen municipality With the waste generation reality of the chosen municipality and the most appropriate technology, a technical feasibility study was prepared regarding final disposal of USW, focused on achieving energy recovery. The main purpose of this study is to serve as an example to be reproduced in many other municipalities. FINDINGS Results have been achieved using the methodologies proposed in this project. Selection of the Itanhaem -SP Municipality for the case study was due to recurring motives found during the investigation.

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The first motives were the problems in solid waste management and administration, mainly in small and medium-sized municipalities, given that the PNRS established deadlines and requirements for preparing an integral management plan. Such municipalities need to hire outside consultants because they do not have specialized technical staff to do so. While the Itanhaem Municipality, with approximately 87,000 residents (IBGE, 2010) qualifies as a mediumsized municipality, as defined by the Ministry of Social Development and Fight against Hunger (2004) and the Social Assistance Reference Center (CRAS), and as shown in Table 1, below, this study could be replicated in other municipalities or be used as a basis for preparing a management plan. This important because according to the classifications, 5,037 of the 5,561 municipalities in Brazil are considered small (see Table 2). This number represents more than 90% of all municipalities with less than 50,000 residents. More than half of these municipalities are concentrated in the South and Southeast regions (IBGE, 2000), which are considered to be the regions with the most economic growth and purchasing power, providing possible resources for waste management incentives. Table 1 - Classification of the municipality size according to its resident population Small Size I

Municipalities of up to 20,000 residents

Small Size II

Municipalities of 20,001 to 50,000 residents

Medium

Municipalities of 50,001 to 100,000 residents

Large

Municipalities with more than 100,001 residents

Source: Prepared by the author based on the MDS classification (2004) Table 2 - Classification of the size of the municipality based on the resident population - Brazil - 2000 Classification according to the population index

Number of municipalities

Proportion of the Total

Small Size I

up to 20,000

4.074

73,26%

Small Size II

From 20,001 to 50,000

963

17,32%

Medium

From 50,001 to 100,000

299

5,38%

Large

More than 100,001

225

4,05%

5.561

100,00%

Total

Source: Prepared by the author based on IBGE (Brazilian Institute of Geography and Statistics), 2000.

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The second motive was related to the destination of solid waste generated in the Itanhaem Municipality, finding that nearly all of the coastal municipalities of Sao Paulo need to haul waste hundreds of kilometers to a final disposal site, thus generating major expenses for the municipality, and with no energy recovery. All of the waste generated in this municipality is taken to a transfer area, which was built in 2012 by a private company, LARA, located on the Gentil PĂŠrez Highway. From this site, the waste is taken to the LARA landfill, located on Av. Guaraciaba, 430, in the Maua Municipality. This shows that due to a lack of options and no development of alternatives, the waste needs to be transported approximately 110 km to reach an appropriate final disposal point. Generation and Gravimetric Analysis of Waste from the Municipality in the Case Study Data presented by the municipal prefecture of Itanhaem shows that in 2013, the municipality collected and hauled approximately 31 thousand tons of trash for end disposal, as shown in Table 3. It is important to note that the municipality currently collects 97% of all generated waste. Table 3 - Waste generated in 2009, 2010, 2011, 2012, and 2013. Total household wastes collected, transferred and hauled for final disposal Month/Year

2009

2010

2011

2012

2013

Jan.

1,584.86

2,159.63

2,692.01

3,422.64

4,294.02

Feb.

2,696.23

4,313.91

4,009.38

3,647.25

2,691.85

Mar.

1,955.50

2,694.06

3,389.81

3,104.95

2,468.27

Apr.

1,882.44

1,891.64

2,990.66

1,997.09

2,366.13

May

1,667.65

1,861.74

2,121.11

1,909.40

1,949.82

Jun.

1,601.94

1,893.80

2,287.98

1,602.93

2,061.09

Jul.

1,765.26

2,044.63

2,512.52

2,905.98

2,605.08

Aug.

1,602.90

1,766.73

2,422.07

2,232.30

1,943.79

Sep.

1,944.51

1,572.85

2,079.10

2,383.51

1,884.40

Oct.

1,969.43

2,108.06

2,240.38

2,527.15

2,657.53

Nov.

2,087.64

2,223.55

1,958.46

2,629.63

2,661.94

Dec.

2,377.92

1,681.59

2,439.85

3,081.70

3,485.32

Yearly Total

23,136.28

26,212.19

31,143.33

31,444.53

31,069.24

Source: PGIRS, 2014

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The gravitational composition of the generated waste, according the Integrated Solid Waste Management Plan of the Itanhaem Municipality (2005), is made up of approximately 75% organic material, as shown in Table 4. Table 4 - Physical composition of trash from the Itanhaem Municipality Components

%

Organic Material

74,80%

Plastic

9,90%

Paper

5,40%

Cloth

2,60%

Glass

2,30%

Trash

1,90%

Metals

1,80%

Wood

1,10%

Other

0,20% Source: PGIRS, 2005

Selection of the Energy Recovery Alternative Selection of the best alternative for energy recovery for USW generated in the municipality requires understanding the type of waste (gravity balance) and amount generated per day. These two parameters are delimiting and exclusive for the type of technology to be used. These energy recovery technologies to generate direct or indirect energy were mentioned and analyzed previously in order to verify which fits best in the case study municipality’s reality. Starting with the premises such as gravimetric composition of the generated USW, generation, collection, and daily destination of the municipality’s USW, generation forecast for the next 20 years, municipal restrictions, and technical feasibility restrictions, the study analyzed the technologies to see which best applied. The forecast was obtained from a logarithmic trend (R² = 0.90), related to the five years presented in Table 3 and considering 20 years of useful life for the selected plant (Figure 6).

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SW Mass ton (ton)

Yearly total Logarithm (Yearly total)

Year Figure 6 - Projection of the amount of RSU generated, collected and sent for disposal. Source: Prepared by the author based on data from PGIRS, 2014. Figure 6 shows that generation, collection and destination forecasts for 2024 and 2034 will be approximately 38,700 and 41,300 tons of waste, respectively. Table 3, presents the monthly generation for 2012 and 2013 (considered the most representative years for the study), with data obtained from PGIRS (2014), average monthly generation percentages, and monthly averages for those two years (Table 5). This monthly average was used for the other years in the forecast (represented by 2024 and 2034) and for the daily averages (Table 6). Table 7 summarizes the total daily average and the daily gravimetric average correlated to Table 4 (for 2013, 2024 and 2034). The study concludes that daily generation and collection, applying the average for 12 months, would not be representative due to the large variations in January, February and December, in the summer season, which does not coincide with the rest of the year.

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Table 5 - Monthly percentage of generation for 2012 and 2013

Month/Year

Generation, Collection and Destination of USW (ton)

Percentage related to yearly total

Average of the two years

2012

2013

2012

2013

Jan.

3.422,64

4.294,02

10,9%

13,8%

12,4%

Feb.

3.647,25

2.691,85

11,6%

8,7%

10,1%

Mar.

3.104,95

2.468,27

9,9%

7,9%

8,9%

Apr.

1.997,09

2.366,13

6,4%

7,6%

7,0%

May

1.909,40

1.949,82

6,1%

6,3%

6,2%

Jun.

1.602,93

2.061,09

5,1%

6,6%

5,9%

Jul.

2.905,98

2.605,08

9,2%

8,4%

8,8%

Aug.

2.232,30

1.943,79

7,1%

6,3%

6,7%

Sep.

2.383,51

1.884,40

7,6%

6,1%

6,8%

Oct.

2.527,15

2.657,53

8,0%

8,6%

8,3%

Nov.

2.629,63

2.661,94

8,4%

8,6%

8,5%

Dec.

3.081,70

3.485,32

9,8%

11,2%

10,5%

Yearly Total

31.444,53

31.069,24

100,0%

100,0%

100,0%

Source: Prepared by the author based on data from PGIRS, 2014. Table 6 - Generation, collection and destination of average daily USW Perceptual Distribution

Month/Year

2024/2034

Jan.

12,4%

Feb.

Generation, Collection and Destination 2024

Daily Average

2034

2013

2024

2034

4.780,51

5.101,69

143,13

159,35

170,06

10,1%

3.920,90

4.184,32

89,73

130,70

139,48

Mar.

8,9%

3.447,94

3.679,58

82,28

114,93

122,65

Apr.

7,0%

2.702,58

2.884,15

78,87

90,09

96,14

May

6,2%

2.389,34

2.549,86

64,99

79,64

85,00

Jun.

5,9%

2.270,05

2.422,56

68,70

75,67

80,75

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Jul.

8,8%

3.410,70

3.639,84

86,84

113,69

121,33

Aug.

6,7%

2.584,29

2.757,91

64,79

86,14

91,93

Sep.

6,8%

2.640,35

2.817,74

62,81

88,01

93,92

Oct.

8,3%

3.210,25

3.425,92

88,58

107,01

114,20

Nov.

8,5%

3.276,06

3.496,15

88,73

109,20

116,54

Dec.

10,5%

4.067,05

4.340,29

116,18

135,57

144,68

Yearly total

100,0%

38.700,0

41.300,0

Source: Prepared by the author based on data from PGIRS, 2014.

Table 7 - Physical composition of waste generated for 2013, 2024 and 2034. Composition of generated waste (ton) 2013

2024

2034

Daily average generation

76.3*

96*

102.5*

Organic Material

57.1

71.8

76.7

Plastic

7.6

9.5

10.1

Paper

4.1

5.2

5.5

Cloth

2.0

2.5

2.7

Glass

1.8

2.2

2.4

Trash

1.4

1.8

1.9

Metals

1.4

1.7

1.8

Wood

0.8

1.1

1.1

Other

0.2

0.2

0.2

* Daily averages for those years, taking into account January, February and December. Source: Prepared by the author based on data from PGIRS, 2005.

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Based on the analysis of generation, collection and destination of USW in the Itanhaem Municipality, it is possible to refine the type of technology to be selected for the study, analyzing the possibilities of technical feasibility for implementing energy recovery. The initial analysis excludes plasma arc technology, because it is a rather recent technology, not very useful for energy recovery from USW, and there is no talk of using such a system Brazil due to the need for major investments and training for the work force, since the technology requires specialization. Regarding landfills, with the use of biogas for energy recovery, there are unfavorable geological issues in the coastal region (flat region with a phreatic level very close to the surface). Another issue, which also applies to incinerators, is related to the amount of waste generated on a daily basis by the municipality, which is insufficient to achieve minimum economic feasibility, not even upon forecasting waste generation over the next 20 years, as shown in Tables 7 and 8. Table 8- Estimates of the minimum amount of USW and electrical energy generation for the above mentioned technologies. Technology

t USW/day

MWh/t USW

Incineration

500 250 (with auxiliary combustion)

0,4 a 0,6

Bio-gas from the landfill

300

0,1 a 0,2

Source: Tolmasquim (2003) and Oliveira (2009 and 2011) with FEAM (2012)

For landfills, there is a restriction applied through Federal Law No. 12,725/12, which establishes requirements on fauna control in the areas near airfields. While landfill control will be monitored continuously, any failures could cause attraction of bird species, thus compromising the safety of passengers. IV - Airport Security Area (ASA): circular area of one or more municipalities defined in relation to a geometric center of the largest airfield runway or military airstrip, with a 20 km (twenty kilometer) radius, the use and occupation of which is subject to special restrictions based on the natural fauna; By drawing this 20 km radius from the runway, the only possible buildable region for the landfill is located in protected areas: either the Sierra del Mar State Park or indigenous tribe lands (see Figure 7). The technologies that best satisfy the needs of small municipalities are pyrolysis, gasification and anaerobic digesters. Despite the fact that anaerobic digesters are included in this sub-category, they are only applicable for the biodegradable fractions of waste. All other types of waste inserted in the anaerobic digesters may damage and even put an end to the microbial reaction, requiring a rigorous segregation of waste both at the source and using a mechanized selection method. It also implies suitable management of recyclable materials to avoid landfill waste disposal as much as possible, without eliminating the prefecture’s expenses for this issue.

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Figure 7- Photo showing a 20-km circumference Source: PGIRS, 2014. For pyrolysis, in addition to syngas produced through the process, there is production of pyroligenous liquid. While there is possibility of gasification and refining of this pyroligenous liquid for energy, it requires a certain amount of attention, since it is corrosive, noxious and highly pollutant. This shows that the chosen technology that most satisfies the municipality’s need for energy recovery is gasification, which can be considered in numerous small and medium-sized municipalities, albeit following feasibility studies in each case. Table 9 shows a summary of the exclusion criteria for the above-mentioned technologies. Table 9 - Possible Technologies Technology Plasma Arc Landfill

Incineration Anaerobic digesters Pyrolysis

Exclusionary Criteria - Recent Technology - High Investment - Unfavorable geology - Large buildable areas - Insufficient amount of waste generated to make it feasible - Amount of waste generated is insufficient to make it feasible - Only applicable to the biodegradable part of waste - Production of pyroligenous liquid Source: Prepared by the author.

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It is important to point out that other USW treatment programs must be implemented and complemented, mainly due to the summer season (January and December), during which there is an average increase of 50% in waste generation. These alternatives mainly need to cover recycling of dry waste and composting for wet waste, which is what is most generated in the municipality. Compositing leads to the creation of fertilizer, which can be applied to gardens, parks and recreational areas. Selection of the Buildable Area According to guidelines from FEAM (2012) in selecting the site of any project, technical aspects must be observed in relation to infrastructure, USW transportation logistics, and environmental obstacles. With these specifications and for economic reasons, taking into consideration a region where there is already infrastructure, easy access, an industrial area, and a certain distance from urban areas, sites have been chosen in the northern region of the Padre Manoel da Nรณbrega Highway, where there is a lower population density (excluding the Oasis, Umuarama and Jardim Coronel neighborhoods) (Figure 8). It is believed that the best location would be in the interior neighborhoods of Cibratel Chรกcaras, San Francisco Chรกcaras, and Jamaica due to issues that we have mentioned above, which are located 15km (average) from the transfer station.

Number of inhabitants more than 5.000

between 1.000 and 3.000

between 3.000 and 5.000

less than 1.000

Figure 8 - Number of residents per neighborhood Source: Drainage Diagram for Itanhaem, 2010

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Gasification Plant According to information from CarboGas Energía, a company specialized in gasification plants, 3,000 m² are required to install a complete gasification plant. This plant includes three different blocks, which are: • USW receiving unit and CDR production: Unit made up of equipment that acts on the received material, promoting mechanical selection, with or without the help of collection staff. Metallic elements are separated out (ferrous and non-ferrous), as well as inert materials, such as glass, rocks, clay, producing WDF (Waste-Derived Fuel). This occupies the largest area of the project. • Gasification Unit: This unit includes the gasser and various integrated equipment items. • Energy generation unit: This unit includes the motorized generators or boilers with pipes and turbo generators, as well as all associated equipment. The first plant unit (reception, projection and CDR production) is in conformance with CONAMA 316/2002, Art. 24, which establishes the obligatory nature of a separation program for recycling and reuse. Social inclusion is involved in this process, with the participation of cooperatives, which work to separate out recyclable materials with market value, as established in Law 12,305/2010, including the Coopersol Reciclando cooperative, which already is working with the municipality to collect recyclable materials. A study done by CENBIO (2013) has analyzed the maximum amount of waste with market value that the cooperative would be able to remove using the manual system and using mechanized projection. Applying these results to the municipality in this case study, we find the information included in Table 10: Table 10 – Waste with market value that is removed by the cooperatives using the manual and mechanized systems.

Components

%

Daily generation and collection Ton/day

Organic Material

74,80%

57,07

10,0%

51,37

5,71

Plastic

9,90%

7,55

9,1%

6,87

0,69

Paper

5,40%

4,12

14,0%

3,54

0,58

Leather/Cloth/ Rubber

2,60%

1,98

0,0%

1,98

0,00

Glass

2,30%

1,75

30,4%

1,22

0,53

Trash

1,90%

1,45

0,0%

1,45

0,00

Gravimetric Composition

216

Percentage of reduction through recycling and/or reuse %

Post Selection

Recycled Waste

Ton/day

Ton/day

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Metals

1,80%

1,37

89,9%

0,14

1,23

Wood

1,10%

0,84

24,8%

0,63

0,21

Other

0,20%

0,15

0,0%

0,15

0,00

Total

100,0%

76,3

67,4

8,9

Source: Prepared by the author based on data by CENBIO (2013) and PGIRS (2005). Although organic material and wood have no market value, the projection considers removal of between 10 and 15%, respectively, as they can be used for composting to conserve microorganisms and accelerate decomposition of wet waste during summer periods, when there is increased waste generation. This way, the decomposed waste can be reused as fertilizer. The generic component known as plastic can be made up of various sub-products, such as polystyrene, polypropylene, high and low density polyethylene, PVC, as well as others. It can also include ferrous and non-ferrous metals. System Energy Recovery Based on the analysis done on generation, disposal, recycling, and composting that will be implemented along with the system, it is possible to know how much energy recovery will be obtained from the waste during the gasification process. Since it was not possible to obtain representative wetness data for the USW generated in the Itanhaem Municipality, we have chosen to use the value from the study prepared by CENBIO, since it covers USW generated in coastal municipalities in the State of Sao Paulo (Ubatuba, Santos, San Vicente, Playa Grande, Guarujá and Cubatão) in order to obtain representativeness for the project. This being the case, wetness was established at 52% of USW. Still, the requirement for acceptable efficiency in the gassers is a wetness rate of between 10 and 30%. This reduction can be obtained by using presses in the CDR post-projection and production processes, reducing the wetness rate by up to 15%. It is then possible to use a formula to calculate PCI (lower heating value) and taking into account the CDR wetness after the press, with a value of 25%. This value is very often used to reduce wetness in the biomass gasification plants, with the following: PCI = 4500 * (1 - U) – 600 * U Where: PCI = lower heating value (kcal/kg) U = wetness fraction in the residue PCI = 4500 * (1 - 0.25) – 600 * 0.25 PCI = 3,225 kcal/kg

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With a daily generation of 67.4 tons (RS mass after selection, which will be treated), taking into account building the system during the present year, removal of the above mentioned materials, and the plant operating 24 hours per day, the input energy in the system will be: 67,400 kg/day = 2,808.33 kg/hour x 3,225 kcal/kg = 9,056,875 kcal/hour = 9.05 Gcal/hour Practical studies, according to REED and DAS (1988) with RIBIERO, LIMA and VERAS (2006), show that gasification is able to convert 60% to 90% of the biomass energy into syngas energy. Moura (2012) and pilot projects for CarboGas Energía using CDR as fuel, showed conversion of around 70%. Therefore, it is possible to calculate the converted energy from the fuel for the syngas. 9.05 Gcal/hour x 0.70 = 6,335 Gcal/hour of usable energy Since this is raw material from urban solid waste, it is highly heterogeneous in its composition and uses air as its gasifying agent; syngas is considered to have a lower heating value (up to 1,194 kcal/Nm³ = 5MJ/ Nm³). The value adopted for this study will be PCI = 1,160 kcal/Nm³ (CarboGas). Therefore, the volumetric flow of the syngas will be: 6.33 x 106 kcal/h / 1,160 kcal/Nm³ = 5,461.20 Nm³/h The produced syngas will be directed to a motorized generator. The type of motor that is best adapted for using syngas in burning is that of Diesel Dual-Fuel cycle, and it is not necessary to make any adaptation for proper functioning of the motor. This is an advantage in comparison with Otto cycle motors, which require adaptations for the syngas to operate. The dual-fuel type motor is able to operate simultaneously with two types of fuel: diesel and gas. For this study, diesel would be used for the start-up, and gradually substituted by syngas; the opposite would occur when the motor is decelerated to stopping. The substitution cannot be complete, since a small fraction of diesel is needed for operation, though this can be replaced up to 80%. In this substitution of diesel with syngas, there is an increase in thermal efficiency, but the electrical efficiency suffers, going from a possible 34% (total use of diesel) to 28%. It is important to point out that this thermal utility can be re-used in the system to heat up the gasifying agent (air) or to help in reducing the CDR moisture, or it can even be used in the absorption chiller. With this data, the below equation can be used to determine the electrical potency of syngas in the motorized generator. Q x PCIgás x η Pot = 860.000 Where: Pot = available power (MW) Q = gas flow (m³/h) PCIgas = lower heating value of syngas = 1,160 kcal/Nm³

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η = efficiency of diesel-cycle motors = 28% = 0.28 860,000 = kcal/h conversion factor for MW Pot =

5.461,20 x 1.160 x 0,28 860.000

= 2,06 MW

Considering a Capacity factor of 95%: Pot = 2.06 x 0.95 = 1.95 MW To estimate the daily available energy, use the equation below: E = P x FC x Operation Time Where: E = available energy (MWh/day) P = available power (MW) FC = load factor - engines working at full capacity = 87% = 0.87 Engine operation time = h/day E = 1.95 x 0.87 x 24 = 40.71 MWh/day Table 11, below, presents a summary of the premises taken into account and the results obtained. Table 11 – Summary of premises and results Daily generation

kg/day

67.400,0

Entrance flow into the gasser

kg/hour

2.803,3

Natural wetness

%

post-treatment wetness

Converted energy from kcal/hour CDR for Syngas

6,33 x106

PCI of syngas

kcal/Nm³

1.160

52

Volumetric flow of syngas

Nm³/hour

5.461,20

%

25

Motorized generator yield

%

28

PCI of the CDR

kcal/kg

3.225,0

Capacity factor

%

95

Operation of the Gasification Plant

hours/ day

24

Available Electrical Output

MW

1,95

CDR Energy

kcal/hour

9,05 x106

Load Factor

%

87

Gasser Yield

%

70

Available Electricity

MWh/day

40,71

Available Electricity

MWh/ month

1.221,30

Source: Prepared by the author.

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FINAL CONSIDERATIONS The purpose of the project presented herein had the objective of doing a feasibility analysis for installing an energy recovery system for USW in the Itanhaem-SP Municipality, in addition to discussing the existing difficulties and make proposals. After making an analysis of USW generation in the municipality, the best technology to apply includes energy recovery through electrical generation, which is gasification. This is mainly due to the low generation of solid waste in the municipality, which makes certain technologies infeasible, in addition to legislative issues, an lack of skilled labor and available funds in the municipality. With the current energy crisis and the forecasts for an increase in electricity consumption, two issues must necessarily be part of a discussion involving the use of solid waste, specialized waste management and energy recovery by generating electrical and/or thermal energy. This then becomes not only an initiative with legal and environmental issues, but also a future solution for decentralized generation. The Itanhaem Municipality presented a draft Integrated Solid Waste Management Plan in August, 2014, informing the possibility of creating a consortium between coastal municipalities to create a possible thermal treatment plant with incineration technology, which would be located in the CubatĂŁo Municipality. That document expressed a certain rejection of the initiative, mentioning public health and atmospheric pollution issues. It also mentioned that the best solution for the waste would be recycling and composting, in addition to continuing to send a fraction of the residues to the landfill. This would not solve the problem of major expenses related to transportation with no real recovery factor. Technology for recovery of solid waste using gasification would be a good solution for the municipality since it can be implemented in municipalities that generate a small amount of waste, in which emissions are created in lower proportions and are more controlled. This would make it possible to achieve recovery of waste generated in the same municipality, reducing expenses and gas emissions with greenhouse effects by avoiding hauling the waste by truck over long distances until reaching the disposal site. It is also important to remember that this plan involves joint efforts by cooperatives in collecting recyclable materials and implementing a compost pile. It is important to note that implementation of an environmental education is of utmost importance to raise the social and environmental awareness of all residents. Implementation of this gasification technology with energy recovery to convert into electrical energy would be done as presented in the study for the year 2014, 40.70 MWh/day. Since household consumption in the Itanhaem Municipality is 0.258 MWh/month, according to data from the SEADE Foundation (2012), the electricity generated by this system could meet the needs of 4,733 households. If four people per household were taken into account, 18,935 residents of the municipality would be served in the year 2014. This number represents 22% of the municipal population.

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This number can be improved by applying different measures that include installing a more efficient dualfuel engine, introducing waste with higher PCI and even changing the gasification agent to use pure oxygen, etc. These initiatives could improve the system’s efficiency and consequently improve the end amount of electricity generation. Since it is a modular system, there is still the possibility of installing other gasification towers to provide an increase in generation and reaching an agreement to treat waste from nearby municipalities. In a few sections, the plan mentions that the municipality does not have enough money to implement a few of the solutions, including the anaerobic digester initiative mentioned in the Management Plan, much less anything involving technical and skilled staff. To overcome this issue, one option would be for the municipality to make a Public-Private Partnership (PPP). In this partnership, the private party would make the investment in technology, while the municipality would provide the land and guarantee purchase of the electricity generated through the system. Through this partnership, the private company would earn through the price per ton of treated waste and through the generated electricity. Meanwhile, the municipality would earn through a lower price for sending waste to end disposal, as well as for electricity purchased for public consumption. During this project, there was still interest in verifying the economic feasibility for implementing the technology in the Itanhaem-SP Municipality, but due to a lack of data, mainly due to financial issues, it was not possible to complete it. For this reason, the recommendation is to make an economic feasibility study and refine the values mentioned for this project using practical data. By working off of the premise of calculations and new case studies in small and medium municipalities, help can be proposed for planning an integrated solid waste management project.

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REFERENCES ABRELPE. Panorama dos Resíduos Sólidos no Brasil – 2013. Available at http://www.abrelpe.org.br/. Accessed on 08/17/2014. BRAZIL. CONAMA Nº 316, October 29, 2002. Dispõe sobre procedimentos e critérios para o funcionamento de sistemas de tratamento térmico de resíduos. Available at http://www.mma.gov.br/. Accessed on 09/16/2014. BRAZIL. Lei nº 12.305 August 2, 2010. National Solid Waste Policy. Available at http://www.abinee.org.br/ informac/arquivos/lei12305.pdf. Accessed on 08/16/2014. BRAZIL. Ministério do Desenvolvimento Social e Combate à Fome. Política Nacional de Assistência Social. Brasília: MSD, 2004, 46p. BRAZIL, Ministério de Minas e Energia. Balanço Energético Nacional 2014. Brasília, 2014. BRAZIL, Ministério de Minas e Energia - Empresa de Pesquisa Energética – EPE. Demanda de Energia 2050. Rio de Janeiro, 2014. CARVALHAES, Vinícius. Análise do potencial energético de resíduo sólido urbano para converSao em processos termoquímicos de gaseificação. Universidade de Brasília, Master’s thesis in Mechanical Science. Brasília, 2013. CEMPRE – COMPROMISSO EMPRESARIAL PARA A RECICLAGEM. Lixo municipal: manual de gerenciamento integrado. Sao Paulo: CEMPRE, 2010 CETESB - Companhia de Tecnologia e Saneamento Ambiental. Resíduos sólidos domiciliares e de serviços de saúde: tratamento e disposição final – texto básico, Sao Paulo, 1997. Coelho, Suani Teixeira. Resíduos Sólidos Urbanos para Geração de Energia. Congresso L.E.T.S. (Logística e Transporte, Energia, Telecomunicação e Saneamento Básico). Sao Paulo, 2014. FEAM – Fundação Estadual do Meio Ambiente. Estudo do estado da arte e análise de viabilidade técnica, econômica e ambiental da implantação de uma usina de tratamento térmico de resíduos sólidos urbanos com geração de energia elétrica no estado de Minas Gerais: Relatório 1. 2. ed. Belo Horizonte, Minas Gerais, 2010 FEAM (Fundação Estadual do Meio Ambiente). Aproveitamento energético de resíduos sólidos urbanos: guia de orientação para governos municipais de Minas Gerais, Belo Horizonte, 2012. EPE – Empresa de Pesquisa Energética. Nota Técnica DEN 06/08. Avaliação Preliminar do Aproveitamento Energético dos Resíduos Sólidos Urbanos de Campo Grande, MS. Rio de Janeiro, 2008. (available at http:// www.epe.gov.br). Fundação SEADE. Estatísticas – Índice Paulista de Responsabilidade Social e Índice Paulista de Vulnerabilidade Social (Itanhaem), 2012. Available at http://www.novomilenio.inf.br/baixada/itaestat. htm. Accessed on 11/04/2014.

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Goldemberg, José; et al. Avaliação de ciclo de vida (ACV) comparativa entre tecnologia de aproveitamento energético de resíduos sólidos. Projeto CENBIO - P&D EMAE/ANEEL – IEE (Instituto de Energia e Ambiente). Sao Paulo, 2013. Gonçalves, Marilson Alves; Tanaka, Ana Karolina; Amedomar, André de Azevedo. A destinação final dos resíduos sólidos urbanos: alternativas para a cidade de Sao Paulo através de casos de sucesso. Profuturo: Programa de Estudos do Futuro. Sao Paulo, 2013. Itanhaem. Plano de Drenagem de Itanhaem – Relatório R3. Sao Paulo, 2010. Available at http://www. itanhaem.sp.gov.br/plano-municipal-saneamento/R3/ITA_R3_V3A_mar2011-drenagem.pdf. Accessed on 10/22/2014. Lanziani, Rodolfo L. Estudo de caso das alternativas para recuperação energética dos resíduos sólidos urbanos (USW) gerados na região metropolitana de Campinas. Universidade de Sao Paulo, Specialization. Sao Paulo, 2013 MOURA, Johnson P. Estudo de casos das rotas tecnológicas para produção de biogás e da influência da composição química de dejetos de matrizes suínas na qualidade do biogás gerada por biodigestor. Doctoral thesis in Mechanical Engineering, UFPE. Pernambuco, 2012. Muylaert, M.S., et al. Consumo de Energia e Aquecimento do Planeta – Análise do Mecanismo de Desenvolvimento Limpo – MDL – do Protocolo de Quioto – Estudos de Casos. Editora da COPPE. Rio de Janeiro, 2000. PGIRS. Plano de Gestão Integrado de Resíduos Sólidos do Município de Itanhaem/SP. Prefecture of Itanhaem. Sao Paulo, 2005. PGIRS. Plano de Gestão Integrado de Resíduos Sólidos do Município de Itanhaem/SP. Prefecture of Itanhaem. Sao Paulo, 2014. REED, T.B., and DAS, A., 1988, “Handbook of biomass downdraft gasifier engine systems”, Biomass Energy Foundation Press, USA. IN RIBEIRO, Ricardo da S.; LIMA, Rafael D. C.; VERAS, Carlos A. G. Caracterização de emissões em sistema de geração energética por gaseificação de biomassa aplicada a comunidades isoladas. Universidade Federal de Uberlândia. 2006.

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BRASIL DEMAND-SIDE MAGANEMENT AND PHOTOVOLTAIC SYSTEMS CONNECTED TO THE GRID AS ENERGY RESOURCES: A CASE STUDY OF THE ELECTRIC INDUSTRY IN NICARAGUA CARLOS GERMÁN MEZA GONZÁLEZ Guidelines: Professor Doctor Sonia Seger Mercedes

SUMMARY The Ministry of Energy and Mines (MEM) was created in 2007 and for the first time in the history of the electricity sector in Nicaragua, the energy efficiency programs focusing on residential lighting, street lighting and lighting in the public sector are being implemented. The objectives of this work are: (i) characterize the electricity sector in Nicaragua (ii) evaluate two energy efficiency programs (replacement of 37,557 mercury vapor light bulbs by sodium vapor light bulbs in street lighting and replacement of 20,000 T-12 light bulbs by T-8 ​​in public sector buildings) and compare with other technology options (iii) evaluate the financial conditions for the implementation of photovoltaic systems connected to the network for the residential sector (iv) applying the technical basis for the inclusion of energy efficiency programs and resources. The results show that (i) the growth in demand for electricity (2004-2014) is mainly explained by the expansion of access to electricity service (ii) from a social standpoint, CFLs compact fluorescent light bulbs, sodium vapor light bulbs and T-5 light bulbs are options that offer better financial returns (iii) currently, photovoltaic systems connected to the network would offer a financial return for consumers with consumption> 1,000 kWh / month and, by 2030 they will be a more viable financial option than energy offered on the net for most customers >150kWh / month (iv) estimated conservation load factors (CLF) indicate that the CLF and sodium vapor light bulbs reduce demand peaking and lighting programs in public buildings decrease demand off peak hours. This paper presents the potential and justifies the inclusion of demand-side management in an integrated resource (IRP) as opposed to the traditional electric Planning. KEYWORDS: Nicaragua, electricity sector, demand-side management.

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INTRODUCTION Nicaragua is a Central American country of 130.373 kilometers2 with a population of 6.1 million inhabitants [1]. The population density is approximately 47 persons per km2, where 58% corresponds to the urban population [2]. In 2013, GDP per capita (in current dollars) was US $ 1.800, with a Human Development Index of 0.62. The average temperature ranges between 24o C y 26.5o C and the highest rate of rainfall occurs between May and October of each year (from 230 mm to 330 mm) [3]. In 2012, the gross power supply (2947.7 ktoe) was mainly based on firewood (34.1%), oil (25.9%) and oil derivatives (31%) [4]. The electrical grid operated mainly based on oil (approximately 55%), but with the significant addition of renewable energy in the last seven years. The Ministry of Energy and Mines (MEM) was established in 2007 and for the first time in the history of the electricity sector, energy efficiency programs are being implemented. These programs are focused on residential lighting, street lighting and public industry lighting. In addition, high electricity rates in Nicaragua and reduction of prices of photovoltaic systems worldwide [5] justify the analysis of photovoltaic systems connected to the network through a preliminary analysis of grid parity ( Cologne, Germany [6] Brazil [7- 9] Cyprus [10] are examples of this type of analysis). This work aims to study the potential impacts of these energy efficiency programs1 and assess current and future financial conditions of penetration of photovoltaic systems connected to the network in the residential sector. In the first section a characterization of the electricity sector was developed. Two potential impacts efficiency programs for lighting and photovoltaic systems connected to the network in the residential sector were then evaluated. After that, the [22] results of the residential lighting program in Nicaragua previously evaluated, there were included with the analyzed results of 2 efficiency programs and there were included as energy resources. The last section corresponds to the findings, policy implications and recommendations for additional research. OVERVIEW OF THE ELECTRICITY SECTOR From 1991 to 2005, the effective capacity increased by 76.5% (from 356 MW to 628 MW), while the peak demand increased by 78.2% (from 271 MW to 483 MW) (Fig. 1). This period was characterized by a constant pressure of peak demand and blackouts were frequent. In 2006, the crisis became critical and there were daily blackouts of up to 10-12 hours. Between 2007 and 2012, the deficit was solved with the entry into operation of about 200 MW of thermal power plants and renewable sources. In 2013, there were 2226.4 km of power transmission lines (93% 7% public and private) and 84% of all transmission lines are 138 kV (1066 km) and 69 kV (817.8 kilometers). The transmission lines of 230 kV are in the Pacific region and are used to interconnect with Central America (Honduras to the north and Costa Rica to the south) (Table 1).

1 There is a significant number of studies that have applied the concepts of demand management in other countries of Latin America [11-21].

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1100 1000 900 800

MW

700 600 500 400 300 200

19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 20 12 20 13

0

Wind

Geothermal

Biomass (sugarcane bagasse)

Hidroelectric

Thermoelectric (Diesel)

Maximum demand

Thermoelectric (fuel oil) Fig. 1. Development of effective installed capacity by type of generation and peak demand. 1991-2013 period [23]. Table 1. Transmission lines and features. [25] Property

Tension

Distance (km)

Percentage

Public lines

230 kV 138 kV 69 kV

339,6 956,8 767 2063,4

15% 43% 34% 93%

230 kV 138 kV 69 kV

Total private lines

2,2 110 50,8 162,9

0% 5% 2% 7%

Total (public + private)

2226,4

100%

Total public lines Private lines

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In 2012, the estimated number of interruptions per kilometer rate was 4.04%. This index is mainly explained by gaps in lines of 69 kV, which are the oldest installed [24]. In addition, the National Transmission System has 86 electrical substations (including transmission, sub transmission and distribution), of which 65 are public and 21 private, with a total installed capacity of 3063.2 MVA (Table 2). Table 2. Total public and private electrical substations [26]. Electrical Substations 230 kV

Public

Private

Total

Percentage

7

3

10

12%

138 kV

37

8

45

52%

69 kV

21

7

28

33%

24,9 kV

0

3

3

3%

Total

65

21

86

100%

The total electricity delivered to the grid grew by 4% per year during the period between 2004 and 2014, while sales in retail electricity grew approximately 5% per year. This period was characterized by relative economic stability and GDP growth of (≈ 4% per year), interrupted by the global economic crisis of 2008. Technical and non-technical losses were reduced from ≈ 30% in 2004 to ≈ 22% in 2009 2 but since 2010 there were no significant additional reductions. The losses have been oscillating around 20% of the total electricity supplied (Fig. 2). 350000

35%

300000

30%

250000

25%

Electricity injected in the network (MWh)

20%

Sold electricity (MWh)

15%

Technical and nontechnical loss %

200000 150000 100000 50000 Jan-14

Jan-13

Jan-12

Jan-11

Jan-10

Jan-09

Jan-08

Jan-07

Jan-06

Jan-05

Jan-04

10%

Fig. 2. Electricity fed into the grid (MWh), invoiced electricity (MWh) and total losses (2004-October 2014). Source: Own research prepared based on the database of the MEM, 2014 [27]. 2 To reduce technical and non-technical losses, Law No. 661 (Law for distribution and responsible use of public electricity service) was approved in 2008.

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Electricity sales are mainly divided between the commercial residential sector (34%), industry (26%) (23%), representing over 80% of total sales. Sales of energy for pumping water, irrigation and public lighting are responsible for 7%, 5% and 3%, respectively. (Fig. 3). 100%

4%

3%

4%

3%

3%

3%

3%

8%

8%

8%

8%

8%

8%

7%

25%

25%

25%

25%

24%

23%

23%

25%

33%

31%

31%

33%

32%

32%

33%

34%

34%

21%

21%

20%

24%

23%

25%

26%

26%

25%

26%

5%

6%

5%

5%

4%

5%

5%

5%

5%

5%

5%

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

4%

4%

4%

9%

9%

8%

27%

27%

28%

31%

32%

22%

90% 80% 70%

4% 8%

60% 50% 40% 30% 20% 10% 0%

Irrigation Industrial Residential Commercial Water pumping Impoverished communities Public lighting

Fig. 3. Percentage of electricity invoiced by sector from 2004-2014. Source: own calculations based on [27]. Table 3 lists the general increase in the number of customers in all sectors between 2004 and 2013. On the other hand, average energy sales per customer (MWh / client) stagnated or even decreased between 2004 and 2013, except in the industrial sector (6.52 MWh / month in 2004 to 8.83 MWh / month in 2013) (Table 4). This scenario suggests an increase in domestic energy demand primarily driven by a vegetative growth. Table 3. Number of customers by sector, 2004-2013 [28]. Types

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

Residential

455394

534842

555045

587996

623753

647881

733527

775806

803493

835637

Commercial

30105

32706

35957

38614

39023

45503

46736

48495

50087

51434

Industrial

4936

5293

5270

5532

5884

6482

6795

7065

7180

7326

Irrigation

770

821

814

847

901

924

984

1032

1070

1100

Pumping

665

709

730

769

831

893

937

1007

1102

1140

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Table 4. Average sales per customer (MWh / client) by sector, 2004-2013 (using the reference month of December of each year). Types

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

Residential

0,10

0,09

0,09

0,09

0,09

0,10

0,09

0,09

0,10

0,10

Commercial

1,40

1,32

1,31

1,10

1,21

1,10

1,07

1,08

1,13

1,12

Industrial

6,52

6,31

6,15

7,81

7,34

7,55

7,87

8,37

8,69

8,83

Irrigation

9,37

7,44

7,50

6,98

6,54

7,45

6,99

7,26

10,57

7,08

Pumping

19,35

17,59

18,04

18,42

16,76

16,39

17,44

16,39

15,77

14,66

Source: own calculations based on [27-28]. RESIDENTIAL SECTOR The growth in average residential electricity sales was 6% annually from 2004 to 2014 (Fig. 4). About 85% of total customers in Nicaragua is below 151 kWh / month (about 710,000) and they receive subsidies. [29] Cooling (52%) and lighting (22%) are the main activities of end-use energy [30]. The main component to explain this growth is the increase in the number of customers (increased ≈ 6% in the period 2004-2013), which is due to massive national electrification program in progress, with increased coverage of 54% from 2006 to 76.2% in 2013 [31]. Moreover, average customer billed electricity (kWh / client) remained almost constant during this period even it decreased in some years (Fig. 5). 100000 90000

MWh

80000 70000 60000 50000 40000 30000

n/ 14 Ja

Ja n/ 13

/1 2 Ja n

Ja n/ 11

n/ 10 Ja

/0 9 Ja n

/0 8 Ja n

Ja n/ 07

/0 6 Ja n

/0 5 Ja n

Ja n

/0 4

20000

Fig.4. Evolution of monthly billed electricity (MWh) for the residential sector and smoothed series using the moving average of 12 months, from January 2004 to October 2014 [27].

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5%

4%

3% 4% 7%

8%

5%

2%

-1%

2007-2008

7%

4% 5% 7%

7%

6%

0%

3%

4%

6%

0% 2006-2007

-11%

-4%

-2%

4%

7%

12%

15% 2004-2005

2005-2006

2008-2009

2009-2010

2010-2011

2011-2012

2012-2013

Variation of billed electricity on average per user (kWh/user)

Variation of number of clients Variation of billed electricity in the sector (MWh)

Fig. 5. The variation in the number of billed electricity customers on average per user (kWh /client), and total billed electricity (MWh) for the residential sector, from 2004-2013. Source: Prepared based on [27-28]. This situation probably indicates that: (A) the possession of certain appliances such as microwave ovens, air conditioners and washing machines remains almost non-existent for most of the population; (B) replacing old appliances with more modern appliances can result in considerable energy savings; (C) substantial increase in the residential rate due to the restriction on household consumption (Fig. 6). Moreover, from a management perspective by the demand side, it has shown that replacing 2 million incandescent light bulbs with compact fluorescent bulbs have an impact, reducing the tendency of growth of residential energy consumption [22].

cĂŠntimos US$/kWh

25 20 15 10 5

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

0

Fig. 6. Evolution of the average electricity tariff for the residential sector [32].

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BUSINESS SECTOR The commercial sector primarily includes the following economic activities: finance and insurance, wholesale and retail, government, hospitals, hotels and educational institutions. According CH, 2010 [30], the central government (28%), retail and wholesale trade (16.7%) and financial entities (10.3%) represent 55% of total electricity business demand. Lighting (36%) and air conditioning (35%) account for 70% of electricity demand [30]. The average growth in total electricity sales in the commercial sector was ~ 4% in the period 2004-2014 (Figure 7). 70000

MWh

60000 50000 40000 30000

n/ 14 Ja

Ja n/ 13

12 n/ Ja

Ja n/ 11

Ja

n/ 10

09 n/ Ja

08 n/ Ja

Ja n/ 07

06 Ja

n/

05 n/ Ja

Ja

n/

04

20000

Fig. 7. Evolution of monthly billed electricity (MWh) for the residential sector and smoothed series using the moving average of 12 months, from 2004 to October 2014 [27].

-3%

6%

-20%

-10%

-6%

3%

2011-2012

-1%

3% 5% 5%

2% 1%

4%

4%

3%

2%

3%

1% 9%

2%

7%

9%

5%

2010-2011

0%

5%

8%

14%

The decrease in average sales per customer can be a consequence of the penetration of more efficient appliances and increased electricity tariffs (Fig.9).

2004-2005

2005-2006

2006-2007

2007-2008

Variation of number of clients Variation of billed electricity in the sector (MWh)

2008-2009

2009-2010

2012-2013

Variation of billed electricity on average per user (kWh/user)

Fig. 8. The variation in the number of billed electricity customers on average per user (kWh / client), and total billed electricity (MWh) for the residential sector, from 2004-2013. Source: Prepared based on [27-28].

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35

cents US$/kWh

30 25 20 15 10

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

0

Fig.9. Evolution of the average electricity tariff for the residential sector [32]. INDUSTRIAL SECTOR Nicaragua is not an industrial society, so industrial and manufacturing processes have not mastered the direction of national economic activity. Manufacture of food and beverages demand about 50% of the electricity billed in the industry [30], showing that industrial production consists mainly of products of medium or low technological complexity. However, since 2011 the total electricity sold in the industrial sector surpassed the total electricity sold in the commercial sector, showing an average annual growth rate of 7%, reaching a double-digit growth in 2006-2007 and 2007-2008 2009-2010 (Fig. 10). Unlike the residential and commercial sectors, the growth of electricity sales in the industrial sector has been the result of both the increase in average sales per customer (kWh / customer) as the increase in the number of customers (Figure 11) . 80000 70000

MWh

60000 50000 40000 30000

Ja n/ 14

Ja n/ 13

n/ 12 Ja

Ja n/ 11

n/ 10 Ja

n/ 09 Ja

n/ 08 Ja

Ja n/ 07

n/ 06 Ja

n/ 05 Ja

Ja

n/ 04

20000

Fig. 10. Evolution of monthly billed electricity (MWh) for the residential sector and smoothed series using the moving average of 12 months, from 2004 to October 2014 [27].

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2% 4% 8% 2011-2012

2% 2% 2%

4% 6% 9%

11% 5% 4%

12% 6%

2010-2011

-6%

9% 3% 4%

21% 14% 5%

0% -3%

2%

3%

7% -3%

2005-2006

2004-2005

2006-2007

2007-2008

Variation of number of clients Variation of billed electricity in the sector (MWh)

2008-2009

2009-2010

2012-2013

Variation of billed electricity on average per user (kWh/user)

Fig.11. The variation in the number of billed electricity customers on average per user (kWh / client), and total billed electricity (MWh) for the residential sector, from 2004-2013. Source: Prepared based on [27-28]. Since 2002, there has been a significant increase in the volume index of industrial production of food for birds, meat (boneless) and thin wafers (beyond normal demand for electricity from other industrial activities) that allegedly took a growing trend in industrial electricity demand (Figure 12). In addition, the number of industries in the regime of “free zones� in the country has doubled in the period 2004-2014 (76 industrial units in January 2004 to 149 in October 2014). Motor systems (55%), air conditioning, and cooling systems (11%) are the main end-use loads. [30] The average sales price of electricity for industry is presented in Figure 13. 2450 2250 2050

Chicken meat

1850 1650

Boneless Beef

1450

Milk

1250

Chicken food

1050 850

Gourmet cookies

650

Alcoholic beverages

450 250

Non alcoholic beverages

0 Ja 6 n/ 0 Ja 7 n/ 0 Ja 8 n/ 09 Ja n/ 1 Ja 0 n/ 1 Ja 1 n/ 1 Ja 2 n/ 1 Ja 3 n/ 14

n/

05

Ja

Ja

n/

04

03

n/

Ja

n/ Ja

Ja

n/

02

50

Fig. 12. Evolution of the indices of industrial production in volume 2002 - November 2014 (base year = 1994) [33-34].

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Eco_LĂłgicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.


cents US$/kWh

25 20

15 10

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

0

Fig. 13. Evolution of the average electricity tariff for the industrial sector [32]. PUBLIC LIGHTING

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

100 90 80 70 60 50 40 30 20 10 0 1994

GWh

The national electricity consumption for public lighting increased from 70 GWh in 2009 to 85 GWh in 2014 (Fig. 14), representing approximately 3% of total annual sales of electricity in 2014.

Fig. 14. The annual electricity consumption for street lighting (1994-2014) [35]. This is mainly due to an increase in the number of operating points of illumination. In 2008, there were 82.949 points of light under operation, representing 18 010 kW of installed power (0.22 kW / point). [36] In 2012, there were 97.545 points of light under operation, representing 20.262 kW of installed power (0.21 kW / point). [Table 5] This represents an increase of 17.6% in the number of points of light (4% / year) and a small improvement in energy efficiency, explained by the gradual replacement of mercury light bulbs with sodium vapor light bulbs.

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Table 5. Technology types, quantities and installed power (kW) in the system of public lighting in cities of Nicaragua (2012) [37].

Cities

Types of technologies

Mercury

Installed 0,100 0,125 capacity (kW)

Sodium vapor Total

0,175

0,250

0,400

0,070

0,100

0,150

0,250

0,400

Managua

9118

0

1736

7061

1867

673

1805

118

17460

11801

97

43046

Chinandega

1360

0

543

2326

669

26

305

15

1873

846

0

6603

León

1530

0

641

2372

791

49

291

1

2399

1014

1

7559

Estelí

764

0

262

1510

290

1

50

10

902

890

0

3915

Jinotega

375

0

43

576

76

1

155

1

630

184

0

1666

Madriz

369

0

145

558

159

4

79

1

447

353

0

1746

Matagalpa

988

0

575

1366

325

53

241

4

1002

1024

0

4590

Nueva Segovia

541

0

272

984

235

9

180

7

425

331

0

2443

RAAN

6

0

0

17

1

0

4

0

2

0

0

24

Carazo

614

2

305

970

292

37

33

13

1226

442

0

3320

Granada

955

0

385

1250

370

120

243

24

1476

603

8

4479

Masaya

1350

0

436

1659

302

35

223

31

2467

1488

1

6736

Rivas

685

0

502

1031

156

6

209

12

889

479

0

3284

Boaco

395

0

189

438

100

29

42

1

911

369

0

2079

Chontales

715

0

104

1245

271

4

30

0

1307

795

0

3756

RAAS

238

0

74

495

80

0

22

0

568

87

0

1326

Rio San Juan

259

0

18

380

56

0

158

1

251

107

2

973

20262

2

6230

24238

6040

1047

4070

239

109

97545

Total

34235 20813

However, there are 39% of the points of light with mercury vapor technology and the remaining 61% are sodium vapor technology. Only 1% are induction light bulbs or metal halide light bulbs. Most mercury lamps installed are of 125 W and 75 W (6,230 and 24,238, respectively) and the location of points of light is widespread throughout the country. Therefore, replacement of 39% of mercury lamps (37.557) should be a priority to achieve a more economical and efficient technology (Table 6).

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Table 5. Public lighting points for technology and the City (2012). [37] Cities

Mercury

Sodium vapor

Mercury

Sodium vapor

Managua

11337

31281

26%

73%

Chinandega

3564

3039

54%

46%

León

3853

3706

51%

49%

Estelí

2063

1852

53%

47%

Jinotega

696

970

42%

58%

Madriz

866

880

50%

50%

Matagalpa

2319

2271

51%

49%

Nueva Segovia

1500

943

61%

39%

RAAN

18

6

75%

25%

Carazo

1606

1714

48%

52%

Granada

2125

2354

47%

53%

Masaya

2432

4210

36%

63%

Rivas

1695

1589

52%

48%

Boaco

756

1323

36%

64%

Chontales

1624

2132

43%

57%

RAAS

649

677

49%

51%

Rio San Juan

454

519

47%

53%

37557

59466

39%

61%

Total

There are two sets of tariffs for public lighting in Nicaragua: (A) for the capital, there is a fixed amount according to the monthly consumption (kWh) and the type of consumer, (b) for the rest of the country, there is a variable rate based on the monthly consumption, ranging from US $ / kWh to US $ 0.014 / kWh 0,026 depending on the city. The national average price in 2013 was US $ / kWh 0.32 including fixed and variable costs. [32] PUMPING AND IRRIGATION Water and electricity are connected, both through the supply side (power generation and water and wastewater) and as to end-use (residential, commercial, agricultural and industrial) [38]. In 2013, 367.6 million m 3 water were produced in Nicaragua, but only 161.1 million m3 were billed, so technical and nontechnical water losses reached 56% [39]. In the same year, 205,015 MWh were purchased to operate the public water sector. Thus, a kWh is required to produce 1.8 m 3 of water. The relationship between electricity purchased for production and water pump is shown in Figure 15.

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Electricity consumption for pumping (kWh)

20000000 19000000 18000000 17000000 16000000 15000000 14000000 13000000 12000000 11000000 10000000 180000002000000022000000240000002600000028000000300000003200000034000000 Water production (m3) Fig. 15. Relationship between electricity purchases for pumping (kWh) and water production (m 3) From 2006-2014. Source: own calculations based on [27-28]. In 2009, there were officially recorded 475.089 water connections [40] with an average demand of 363 kWh / year per connection. Besides the fact that the number of water consumers is likely to increase, the relationship between water production and energy consumption has remained constant from 2006 to 2014. However, electricity prices have significantly increased in this period, as well as electricity costs of Water Suppliers (Table 7). Consequently, in 2013 the electricity costs accounted for nearly 80% of total revenues from the sale of water. This reflects the importance of efficiency in the water-energy nexus for the financial stability of the concessionaire of water, but also the financial stability of public services. Table 7. Indicators of use of electricity from the national public water company (2004-2013) Year

Electricity consumption (MWh)

Average purchase rate (US $ / kWh)

Expenditure on the purchase of electricity (million US $)

Specific energy consumption (kWh to produce a m3)

Water production (m3) By kWh

2006

149.737

0,126

18,9

0,55

1,8

2007

156.139

0,135

21

0,57

1,8

2008

163.330

0,169

27,5

0,57

1,8

2009

172.523

0,140

24,1

0,58

1,7

2010

180.875

0,162

29,2

0,57

1,7

2011

193.093

0,201

38,9

0,57

1,8

2012

196.351

0,203

39,9

0,55

1,8

2013

205.015

0,200

41,01

0,56

1,8

Source: Prepared based on [27- 39].

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Finally, sales of electricity for irrigation have a weight of 5% of total sales. The activity of irrigation is essential for the expansion and productivity of agricultural activities. The aggregate monthly data show a slight upward trend, with well-defined cycles (Fig. 16) related to the intensification of national rains scheme from May to October (reduced volume of irrigation water) and the period of low rainfall from November to April each year (increase the volume of irrigation water) (Fig. 17). 16000 14000 12000 MWh

10000 8000 6000 4000 2000

n/ 14 Ja

Ja n/ 13

12 n/ Ja

Ja n/ 11

n/ 10 Ja

09 n/ Ja

08 n/ Ja

Ja n/ 07

06 n/ Ja

05 n/ Ja

Ja

n/

04

0

Fig. 16. Evolution of monthly billed electricity (MWh) for the residential sector and smoothed series using the moving average of 12 months, from 2004 to October 2014 [27].

14000

350

12000

300

10000

250

8000

200

6000

150

4000

100

2000

50

0

ju au ly gu se st pt em oc ber to no ber ve m de be ce r m be r

e

ju n

ay

m

il ap r

ja nu

a fe ry br ua m ry ar ch

0

2004 2005 2006 Rainfall (mm)

400

MWh

16000

2007 2008 2009 2010 2011 2012 2013 2014 Rainfall (mm)

Fig. 17. Billed monthly electricity (MWh) for irrigation (2004-2013) and the average monthly precipitation (mm) recorded between 1900 and 2009. Source: own calculations based on [27 and 41].

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MATERIALS AND METHODS METHODOLOGY After a brief characterization developed above, the next part of the work was done in 3 steps. First step: It was prepared the economic and financial analysis of two management programs by the demand side (one for street lighting and lighting of buildings in the public sector) and the potential use of LED technology in place traditional lighting technologies. The analysis investigates the economic criteria not only from the perspective of the customer, but also the Concessionaire and Society. Step two: Financial conditions for grid connection of photovoltaic systems for the residential sector were assessed. Step 3: The technical basis was used to integrate the assessed energy efficiency programs into the portfolio of domestic energy resources. Public policy considerations and implications for future research are developed at the end of the article. PUBLIC LIGHTING PROGRAM: REPLACEMENT OPTIONS Two strategies were evaluated: (A) the total replacement of mercury bulbs for sodium vapor bulbs (b) the total replacement of the mercury bulbs by LED 12 technology. Case (a) involving the following cases: (I) Total 37 557 replacement of mercury vapor light bulbs existing in 2012; (ii) 50% of the light bulbs installed in the first year and the remaining 50% will be installed in the second year of the program; (iii) analysis was performed taking into account that the initial investment was financed by the government and assuming that the replacement will be financed by the end of the first cycle of life of the sodium vapor bulbs (5-6 year program) and it will be financed and executed by the dealer. In the case of (b) the initial investment is also financed by the government, and because of the lifetime of the LED technology is about 50,000 hours, no replacement is required and added a residual value after 10 years analysis. The basic parameters and assumptions are shown in Table 8. Table 8. Parameters and assumptions used in the economic and financial evaluation Parameters Discount rate of the concessionaire Official social discount rate [42] Purchase price of the concessionaire (US $ / kWh) [43] Average price for the sale of energy for street lighting (US $ / kWh) [32] Daily use (h) Heat rate official reference power plant (fuel oil) (kWh / Gln) [44] Estimated reference of the reference power plant (%) Reference price of fuel oil (US $ / bbl) Transmission cost ($ / MWh) [45] Emission factor (TCO2 / MWh) [46]

12%/year 8%/year 0,16 0,32 11,5 15,7 33 80 7,91 0,7

Table 9 shows the additional technical and economic parameters used for substitutions proposals. For example, in the first column, the mercury light bulb of 125 W is replaced by a sodium-vapor light bulb of 100 W or LED 12. In order to obtain parameters like lighting, 2 lights LED 12 (including light poles) were considered to replace one sodium vapor 250 W light bulb and one 400 W mercury light bulb.

240

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Table 9 - Definitions and technical and economic assumptions Mercury [36 e 47] Parameters

125W

175W

250W

400W

Luminous Flow (lm)

6000

8000

12500

22000

Wattage (W)

125

175

250

400

Reactor power (W)

32

22

27

32

Total power (W)

157

197

277

432

15000

15000

15000

15000

Reference price (light bulb + accessories) (US $)

12

14

17

24

Labor (US $)

50

50

50

50

Total installed cost (US $)

62

64

67

74

100W

100W

150W

250W

Luminous Flow (lm)

5753

5753

9027

15560

Testing Wattage (W)

112,7

112,7

175,5

256,4

10

10

14

41

123

123

190

297

24000

24000

24000

24000

Reference price (light bulb + accessories) (US $)

175

175

200

225

Labor (US $)

50

50

50

50

Total installed cost (US $)

225

225

250

275

LED 12

LED 12

LED 12

LED 12

7108

7108

7108

14216

Proven testing Wattage (W)

89

89

89

179

Reactor power (W)

0

0

0

0

Total power (W)

89

89

89

179

50000

50000

50000

50000

Reference price (light bulb + accessories) (US $)

750

750

750

1500

Light pole Price ($)

450

450

450

900

Labor (US $)

100

100

100

200

1300

1300

1300

2600

Lifetime (h)

Soodium vapor [47] Parameters

Reactor power (W) Total power (W) Lifetime (h)

LED [47] Parameters Luminous Flow (lm)

Lifetime (h)

Total installed cost (US $)

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LIGHTING PROGRAM IN PUBLIC SECTOR BUILDINGS: REPLACEMENT OPTIONS

3 4

The results of energy audits in 20 government institutions: 13 central government institutions (e.g. ministries), 3 public universities, a high school and 3 hospitals indicate that air conditioning systems use between 60% and 70% of total consumption, while lighting uses 12% to 16% [48]. The official program of efficient lighting in public buildings sector expects 20,000 to replace T-12 light bulbs of 40 W to T-8 bulbs of 32 W. However, in this study two other options were assessed: Replacement of T-12 T-5 bulbs (25 W) and light bulb replacement T-12 by LED (22 W). The parameters and assumptions are presented in Table 10. Table 10. Parameters and assumptions used in the economic and financial evaluation Parameters Discount rate of the concessionaire Official social discount rate [42] Purchase price of the concessionaire (US $ / kWh) [43] Energy tariff for government institutions (US $ / kWh) [49] Average price of the power of government institutions (US $ / kW) [49] Average prices for light bulbs T-12, T-8, T-5, LED 22W (US $)3 Daily use (h)4 Heat rate official reference power plant (fuel oil) (kWh / Gln) [44] Estimated reference of the reference power plant (%) Reference price of fuel oil (US $ / bbl) Transmission cost ($ / MWh) [45] Emission factor (TCO2 / MWh) [46]

12%/year 8%/year 0,16 0,20 30 10,9 - 26,8 - 45 - 86 7,2 15,7 33% 80 7,91 0,75

RESIDENTIAL PHOTOVOLTAIC SYSTEMS CONNECTED TO THE NETWORK: PARAMETERS AND ASSUMPTIONS Photovoltaic systems have been used in isolated rural areas of Nicaragua, especially in the autonomous regions of the Atlantic [50]. Nicaragua has an average solar radiation ≈ 1,900 kWh / m 2 / Year. Assuming a rate of return (Performance Coefficient English - PR) of 0.75, national capacity factors (capacity factors English - CF) are between 15% -18% (Table 11).

3 Prices include light bulbs and reactor (for options T-8 and T-5) and a reference installation cost of $ 10 per light bulb applied to the three technologies considered efficient. 4 Office hours for government is typically between 8:00 and 17:00 Monday through Friday. However, to calculate a reduction of 10% in hours of daily use was applied to make a conservative assumption in the hours of operation.

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Eco_LĂłgicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.


Table 11. Approximate solar radiation and capacity factors (CF) for departments Solar radiation (kWh/m2/day) 5,8

Solar radiation (kWh/m2/year) 2117

PR

FC (%)

0,75

18,1

Chinandega

5,8

2098,8

0,75

18

León

5,8

2098,8

0,75

18

Managua

5,5

2007,5

0,75

17,2

Granada

5,5

2007,5

0,75

17,2

Rivas

5,5

2007,5

0,75

17,2

Estelí

5,3

1934,5

0,75

16,6

Madriz

5,3

1934,5

0,75

16,6

Nueva Segovia

5,3

1934,5

0,75

16,6

Masaya

5,3

1916,3

0,75

16,4

Chontales

5

1825

0,75

15,6

Boaco

5

1825

0,75

15,6

Matagalpa

5

1825

0,75

15,6

Jinotega

4,7

1715,5

0,75

14,7

RAAN

4,7

1715,5

0,75

14,7

RAAS

4,7

1715,5

0,75

14,7

Río San Juan

4,5

1642,5

0,75

14,1

Average

5,2

1901,2

0,75

16,3

Standard deviation

0,4

147,9

0

1,3

Departments Carazo

Source: estimates based on the solar map of Nicaragua. [51] The parameters and assumptions used to calculate the leveled cost of electricity generated by the photovoltaic ( normalized electric power cost English - LCOE) are presented in Table 12.

Eco_Lógicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.

243


Table 12. Parameters and assumptions Parameters Useful life (years) Total initial investment (US $ / Wp) Annual operating costs and maintenance (% of the initial investment) Annual reduction in the price of photovoltaic systems (% of the initial investment) Annual discount rate of consumer (%) Annual loss of productivity of the system (%)

25 3,5 1 3 15 1

It is noteworthy that any increase in the residential rate was considered during the review period (20152032). A reduction of 3% in the price of photovoltaic system and a discount rate to the consumer of 15% per year is assumed. Therefore, these parameters and assumptions can be understood as a realistic conservative scenario. Further analysis should include the possibility that the interest expenses (funding) and precise radiation measures. These issues were not considered in this exploratory study.

RESULTS AND DISCUSSION STREET LIGHTING PROGRAM Consumer Perspective Energy efficiency programs in the street lighting do not transfer direct economic benefits to consumers, since the tariff system does not provide any compensation in terms of reducing the monthly bill. The Concessionaries forecast For street lighting, the benefits of the program in two ways (a) reduction in the purchase of electricity and (b) the electricity saved will be charged to customers, regardless of the application of efficient technology or not. For mercury / sodium light bulb increased revenues accumulated in the present value is estimated at US $ 24.2 million (Table 13). Table 13. Financial impacts on the distribution concessionaire (Mercury by sodium vapor)

Year

MWh (economized, but charged)

Estimated cost of substitution (millions of US $)

Reduction Increased of purchase Increased revenues Billing of revenues in current (millions of electricity (in millions figures (in US $) (million US of US $) millions of $) US $) 0,9 1,8 2,7 2,4

Avoided average power (MW)

1

5633

2

11266

1,8

3,6

5,4

4,3

1,3

3

11266

1,8

3,6

5,4

3,8

1,3

4

11266

1,8

3,6

5,4

3,4

1,3

244

0,6

Eco_Lรณgicas: Latin American Monograph Contest on Renewable Energy and Energy Efficiency. Selected Papers.


5

11266

1,8

3,6

5,4

3,1

1,3

6

11266

1,8

3,6

-2,4

-1,2

1,3

7

11266

1,8

3,6

5,4

2,4

1,3

8

11266

1,8

3,6

5,4

2,2

1,3

9

11266

1,8

3,6

5,4

2

1,3

10

11266

1,8

3,6

5,4

1,7

1,3

17,1

34,2

43,6

24,2

12,2

TOTAL

107.028

-8

-8

In the case of the substitution of mercury / LED 12; economies in purchasing power were estimated at US $ 28.3 million and the total energy invoiced at $ 56.5 million. There are no replacement cost (LED 12 lifetime 50,000 h), which caused an increased income accumulated in the current and estimated value at US $ 46.5 million; that is almost double of the economic benefits of sodium vapor mercury option (Table 14). Table 14. Financial impacts on the distribution concessionaire (Mercury by sodium vapor) Year

MWh (economized, but charged)

Reduction of purchase of electricity (million US $)

Billing (millions of US $)

Increased revenues (in millions of US $)

Increased revenues to current amount (in millions of US $)

Avoided average power (MW)

1

9300

1,5

3

4,5

4

1,1

2

18600

3

6

8,9

7,1

2,1

3

18600

3

6

8,9

6,4

2,1

4

18600

3

6

8,9

5,7

2,1

5

18600

3

6

8,9

5,1

2,1

6

18600

3

6

8,9

4,5

2,1

7

18600

3

6

8,9

4

2,1

8

18600

3

6

8,9

3,6

2,1

9

18600

3

6

8,9

3,2

2,1

10

18600

3

6

8,9

2,9

2,1

176.700

28,3

56,5

84,8

46,5

20,2

TOTAL

Society’s Perspective The financial benefits are estimated thereof including: (A) the initial investment of government (b) increase the income of the concessionaire (c) avoided costs for fuel oil (d) transmission costs avoided. Table 15 shows that from the society’s point of view, the best strategy is to replace the mercury vapor technology

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for technology and sodium vapor and expect a reduction of the cost of LED technology. Although the technology LED 12 is preferable from an environmental point of view and from a financial point of view, the concessionaire, the high initial investment (about US $ 50 million) is less attractive from the society’s point of view. Table 15. Society Perspective: financial indicators Indicators

Mercury / Sodium

Mercury / LED 12

Social VPL (millions of US $)

27,3

22,4

Social TIR

61%

17%

Rate of social return

3,4

0,6

Return time (years)

2

5-6

Cost of energy saved (US $ / kWh)

0,08

0,25

Avoided tCO2 emissions (thousands)

74,9

186

LIGHTING PROGRAM IN PUBLIC SECTOR BUILDINGS Government Perspective Table 16 shows that the T-12 / T-5 option is the most profitable from the point of view of the government. In the case of LED technology, it is important to note that the residual value in year 10 is approximately 27% of the initial investment (US $ 0.47 million in present value) because daily use of assumed bulbs (7, 2 h) and weekdays (Monday to Friday) increase the overall life of the operation for nearly 25 years. Still, the results indicate that the best strategy is to replace the bulb technology T-12 bulbs with T-5 technology and expect a 20% -30% of the cost of LED technology. Table 16. Government Perspective: financial indicators Indicators

T-12 / T-8

T-12/T-5

T-12/LED

4679

8733

10602

Total avoided peak power (MW)

34

68

91

VPL (millions of US $)

0,7

1,5

1,9

TIR

32%

37%

26%

Profitability index

1,5

1,8

1,2

Return time (years)

3-4

3-4

7

Cost of energy saved (US $ / kWh)

0,07

0,08

0,93

Avoided cost of peak power (US $ / kW)

9,3

9,9

11,4

Total electricity saved (MWh)

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The Concessionaries forecast The dealer faces up to $ 0.7 million in accumulated losses of income replacement in cases T-12 / T-8 between US $ 1.3 - US $ 1.7 million replacement T-12 / T 5 and T-12 / LED, respectively (Fig. 18). 0 -0.2

Reduction of invoicing (millions US$)

-0.4 -0.6

T-12/T-8

-0.8

T-12/T-5

-1

T-12/LED

-1.2 -1.4 -1.6 -1.8 -2

Fig. 18. Cumulative reduction in the turnover of the concession due to the substitutions T-12 / T-8, T-12 / T-5 and T-12 / LED. Society’s Perspective The net balance to society were estimated as well as the results including (a) the net present value to the government (b) losses of revenue from the dealership, (c) the costs avoided fuel oil, (d) the avoided transmission costs. Table 17 also shows that from the point of view of the company’s best strategy is to replace T-12 lamps with T-5 technology. Therefore, the current official program T-12 / T-8 should be discontinued and replaced by a replacement program T-12 / T-5. Table 17. Society Perspective: financial indicators Indicators

T-12 / T-8

T-12/T-5

T-12/LED

Social VPL (millions of US $)

0,35

0,63

0,76

Social TIR

21%

24%

16%

Rate of social return

0,7

0,9

0,5

Return time (years)

4

4

6

CONNECTED PHOTOVOLTAIC SYSTEMS TO THE HOME NETWORK (GRID’S PARITY ANALYSIS). The cost of electricity level (The leveled electricity costs English - LCOEs) for capacity factors (DC) between 15% and 18% were calculated and plotted with residential rates (Figure 19, 20, 21 and 22). Currently, for residential customers with a monthly consumption of 1,000 kWh / month, the installation of a PV system

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could generate energy less expensive than the energy delivered by the Concessionaire in all interconnected regions. Based on the assumptions made in this study, before 2020 there we will be a balance of the year for customers with monthly consumption of between 501 and 1,000 kWh in all regions of the country and for customers between 151 and 500 kWh at the end of 2020 before 2030. If the subsidy for customers with consumption below 151 kWh / continuous month and rates remain close to US $ / kWh 0.10 to grid parity, it will not be achieved in the period under review. 0.55

Residential rate (US$/kWh)

0.50 0.45

LCOE (US$/kWh with CF=15%)

0.40

Range 0-50 kWh (subsidized)

0.35

Range 26-50 kWh (subsidized)

0.30

Range 51-100 kWh (subsidized) Range 101-150 kWh (subsidized)

0.25

Range 151-500 kWh

0.20

Range 501-1000 kWh

0.15

Range > 1001 kWh

2032

2031

2030

2029

2028

2027

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

0.05

2015

0.10

Fig. 19. Timeframe to achieve grid parity for residential customers of all ranks of consumption capacity factor (CF) = 15%. 0.60 0.55 Residential rate (US$/kWh)

0.50 0.45

LCOE (US$/kWh with CF=16%)

0.40

Range 0-50 kWh (subsidized)

0.35

Range 26-50 kWh (subsidized)

0.30

Range 51-100 kWh (subsidized) Range 101-150 kWh (subsidized)

0.25

Range 151-500 kWh

0.20

Range 501-1000 kWh

0.15

Range > 1001 kWh

2032

2031

2030

2029

2028

2027

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

0.05

2015

0.10

Fig. 20. Timeframe to achieve grid parity for residential customers of all ranks of consumption capacity factor (CF) = 16%.

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0.55

Residential rate (US$/kWh)

0.50 0.45

LCOE (US$/kWh with CF=17%)

0.40

Range 0-50 kWh (subsidized)

0.35

Range 26-50 kWh (subsidized)

0.30

Range 51-100 kWh (subsidized) Range 101-150 kWh (subsidized)

0.25

Range 151-500 kWh

0.20

Range 501-1000 kWh

0.15

Range > 1001 kWh

2032

2031

2030

2029

2028

2027

2026

2025

2024

2023

2021

2022

2020

2019

2018

2017

2016

0.05

2015

0.10

Fig. 21. Timeframe to achieve grid parity for residential customers of all ranks of consumption capacity factor (CF) = 17%. 0.55

Residential rate (US$/kWh)

0.50 0.45

LCOE (US$/kWh with CF=18%)

0.40

Range 0-50 kWh (subsidized)

0.35

Range 26-50 kWh (subsidized) Range 51-100 kWh (subsidized)

0.30

Range 101-150 kWh (subsidized)

0.25

Range 151-500 kWh

0.20

Range 501-1000 kWh

0.15

Range > 1001 kWh

2032

2031

2030

2029

2028

2027

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

0.05

2015

0.10

Fig. 22. Timeframe to achieve grid parity for residential customers of all ranks of consumption capacity factor (CF) = 18%. Photovoltaic systems connected to the grid are a potentially viable option for residential customers with high income and will be for all residential customers with an average income over the next 10 years. However, to allow penetration of photovoltaic systems, the basic technical (such as the implementation of two-way electric meters) and regulatory issues should be a priority on the agenda of energy policies. However, from the perspective of the distribution concessionaire, customers with over 150 kWh / month account

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for 14% of all residential customers, but represent 40% of total residential electricity sales and, more importantly, they represent 64% of total residential revenues of the Concessionaire (Table 18). Therefore, special attention should be given to the potential for decreased revenues dealer because the impact can be transferred to customers who do not participate (low-income clients) through upward adjusted rates. Table 18. Residential customers with over 150 kWh / month, bills in (kWh) and gross income from these strata (US $) using data from December 2013. Range

151-500 kWh 104.896

501-1000 kWh 7.153

> 1000 kWh

Total

1.561

113.610

Percentage of total customers

13%

1%

0,2%

14,2%

Sales (million kWh)

23,4

4,6

2,2

30,2

Percentage of total turnover

31%

6%

3%

40%

Total revenues (millions of US $)

6,3

1,6

1

8,9

Percentage of residential income of the concessionaire

45%

11%

7%

64%

Number of clients

Source: own calculations based on [29]. EFFICIENCY PROGRAMS INTEGRATED AS ENERGY RESOURCES

Annualised economized energy costs (US$/kWh); 8% social discount rate and 12% for clients

As energy efficiency and management of demand side reduce the required capacity, it is possible to reduce the investment required for the construction of new plants and the expansion of transmission and distribution systems [52]. In this section, the results of the residential lighting program were included. [22] Figure 23 shows the curve of the energy saved in the residential lighting programs (incandescent / LFC), bulb replacement T-12 / T-5 in public sector buildings and the replacement of mercury vapor bulbs / sodium with associated costs. It is applied simultaneously; almost 145 GWh / year could be saved. Mercury/vapor sodium

0.12 0.10

T-12/T-5

0.08 0.06 0.04

Incandescent/LFC

0.02 0.00

0

10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 GWh (economized)

Fig. 23. Energy economized curve of residential efficiency programs (incandescent / LFC), lighting in public sector buildings (T-12 / T-5) and lighting (Mercury / sodium vapor).

250

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In order to characterize the impact of management programs for the demand side as resources that can be “removed” from the load curve, the charge conservation factor was calculated (Conservation Load Factor English - CLF). The CLF is defined as the ratio of the average power economized by a conservation measure and economized power at the peak period. Consequently, it is analogous to the capacity factor of the power generation and / or load factor of the distribution’s concessionaire (average system’s load / peak system’s load). Table 19 shows that the program CFLs residential lighting and street lighting program are 0.25 and 0.49, respectively. Therefore, both the residential lighting program and street lighting should reduce peak demand (from 18:00 to 23:00) and public lighting program affects off-peak hours (12:00 h to approximately 05:30 h). As the office hours of public institutions (8:00 to 17:00) does not coincide with the peak hours of domestic demand (18:00 to 22:00), lighting programs in public buildings are more than 1 CLFS. That is, the impact on reducing peak demand is virtually nonexistent. Table 19 summarizes the technologies on a comparable basis. Table 19. The parameters to integrate energy efficiency programs and resources 5 Parameters

Daily power reduction (MW) Annual operating time (h) Peak time Match factor 5 Demand Reduction of peak (MW) Annual economized energy (MWh) Conservation load factor (CLF)

Incandescent / LFC

Mercury / LED 12

T-12/ T-8

T-12/T-5

T-12/LED

122

Mercury / Sodium Vapor 2,6

4,4

0,3

0,6

0,68

1106

4198

4198

1728

1728

1728

0,5

0,97

0,97

0,05

0,05

0,05

61,6

2,5

4,3

0,02

0,028

0,034

134.926

10.914

18.469

518

968

1.175

0,25

0,49

0,49

3,9

3,9

3,9

CONCLUSIONS, RECOMMENDATIONS AND FUTURE DIRECTIONS Nicaragua is a small underdeveloped country in Latin America, with a predominance of agricultural economic structure and medium-low technology industry. Moreover, until 2005 only half of the population had access to electricity. Currently, approximately 79% of the population has access to electricity. This explains the historical and recent dynamics of the use of electricity in society and it is partly responsible for the low labor productivity. If the economy does not suffer recession or crisis, the demand for energy and power will continue to grow in the coming years in all sectors. As expected, energy efficiency as energy and economic resource is not yet used; it has been only partially explored in the electricity sector in Nicaragua. For example, it can be noted that in October 2014 the central government and the national water company had an accumulated debt with the energy dealer for 5 Defined NREL, 2013 [53] as “... the fraction of the maximum demand of the population is under operation at the peak of the system. For example, if at the time of peak system, only 3 of the 7 compact fluorescent light bulbs are lit, then the coincidence factor is 7.3 ... “

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$ 4.1 million and $ 22.8 million respectively. Thus, programs for the management of demand in the public sector are important not only in the field of energy planning per se, but also to improve the balance sheets (government and public services). The analyzes shown in this study indicate the feasibility of the implementation of the assessed programs. For lighting options in public buildings, instead of replacing T-12/T-8, T-12/T-5 replacement should be implemented. Although there are indications that most of the technologies are LED lighting in 2035 [54] technologies, market Nicaragua, the best strategy is to wait for a reduction of the costs of LED technology. It is recommended to test the technical characteristics of all lighting technologies available on the market in order to compare with national and international standards. In addition, “task lighting” must not be forgotten. Significant penetration of PV systems could reduce demand outside the peak hours during the day. The financial evaluation of photovoltaic systems for residential consumers showed that the cost of electricity level (LCOEs) generated by photovoltaic systems in areas with a load factor between 17% and 18% is less expensive than, current rates for residential customers with consumption exceeding 500 kWh respectively. It is estimated that by 2032 the positive financial results could be extended to customers · 150 kWh / month.This raises the need to assess distributed generation in terms of regulation, tariffs, etc.; with particular attention to the potential for decreased revenues because Dealer impacts could be transferred to customers who do not participate (low-income clients) through upward rates adjusted. Although the analysis has only been developed for residential customers, it is recommended to broaden and deepen the public, industrial and commercial sectors. It is essential to continue expanding access to electricity to 100% of the population, especially in rural areas (~ 1.4 million people remain without power, mostly in North and South Atlantic of the country [31]). Along with photovoltaic systems for isolated regions, single-core systems with earth return (MRT) and the multiearth neutral phase-phase systems (MRN) also could be evaluated and implemented. More research should be conducted on the following topics: (A) research update and improve end-use energy for all sectors. This will improve the information, forecasting and decision-making process, (b) the evaluation and implementation of a greater number of energy efficiency programs (c) monitoring the implementation and operation of programs is important for ex post evaluation (D) measurement and incorporation boomerang effect (Geller and Atalli see [55]) (e) economic and social impacts of energy savings in the private and public consumption and investment, (g) the possible consequences of the implementation of an integrated resource planning (IRP) to the electricity sector in Nicaragua. Finally, the construction of a inter oceanic channel in the southern Nicaragua is under study [56] and the potential implications of energy as a result of the intensification of economic activity should not be overlooked.

APPENDICES A. Street Lighting program The Concessionaries forecast CEM(x) por E(j) = (Nmx • px • h) - (Nej • pj • h)

Eq. (A.1)

The energy is saved annually (MWh) due to replacing the mercury vapor technology M(x) by efficient technology E (j), Nmx the amount of mercury bulbs, px is the power of each type of mercury bulb, h is the

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annual number of hours in operation, Nej is the number of bulbs efficient technology, pj It is the power of each type of lamp efficient technology. Epr (US$) = CEM(x) with E(j) • Epp

Eq. (A.2)

Epr (US$) is the annual energy economy (US $) in energy purchased as the result of the substitution of mercury technology by efficient technology, CEM(x) with E(j) which is the energy saved annually in the equation. (A.1) and Epp the average purchase price of electricity (US $ / MWh). URI (US$) = ∑ n=10 i=1

Epr (US$) + Es (US$) - Rc (US$) + Rv (US$) (1 + r)i

Eq. (A. 3)

Where URI (US$) is the cumulative increase in revenue of the Concessionaire to the present value, Epr (US$) reductions in the purchase of energy (Eq. (A.2)), Es (US$) are regular sales of electricity that remain unchanged despite the implementation of the efficiency program, Rc (US$) replacement costs if sodiumvapor lamp, Rv (US$) is the residual value over the 10 for the year if the LED 12 r is the discount rate dealer. Society’s Perspective

SNPV = -Io + URI (US$) + ∑ n=10 i=1 (

Fa (1 + r)i

+ Tca

(1 + r)i

)

Eq. (A. 4)

SNPV is the social net current value of an efficiency program, Io is the initial government investment in efficient technology, URI (US$) is the cumulative increase in revenue at current value of the concessionaire (Eq.A.3) Fa and the cost of avoided fuel oil (US $) Tca are avoided transmission costs (US $) r is the official social discount rate. B. Lighting program in public sector buildings Government Perspective CET-12 with E(j) = (NmT-12 • pT-12 • h) - (Nej • pj • h)

Eq.

(B.1)

Where CET-12 with E(j) is (MWh) is annually saved due to the replacement of T-12 technology for efficient technology E (j), NmT-12 is the amount of T-12 bulbs (in this case 20,000), pT-12 is the power of T-12 bulbs (including reactor), h is the annual number of hours in operation, Nej is the number of efficient technology light bulbs, pj is the power of each type of bulb efficient technology (including the reactor, except LED). A similar concept was applied to calculate the power preserved. Epr (US$) = CET-12 with E(j) • Epp

Eq. (B.2)

Epr (US$) is the annual economy by reducing energy expenditure (MWh) in manufacturing due to the replacement of mercury technology for efficient technology E(j), CET-12 with E(j) is the energy savings (MWh) (Eq. (B.1)) and Epp is the type of energy (US $ / MWh). A similar concept was applied to reduce peak power purchase using the rate of peak power ($ / MW).

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NPV (US$) = -Io + ∑ n=10 i=1

Es (US$) + Ps (US$) - Rc (US$) + Rv (US$) (1 + r)i

Eq. (B.3)

Where NPV (US$) is the net present value of the government, -Io is the initial investment in efficient technology, Epr (US$) is the annual savings by reducing energy consumption (MWh) on the bill due to the replacement of T -12 efficient technology (Eq. B.2), Ps (US$) is the annual savings by reducing the cost of power (MW) in manufacturing due to the replacement of T-12 efficient technology, Rc (US$) is the cost of replacing T 8 and T-5 light bulbs to the end of the first cycle of life, Rv (US$) is the residual value in year 10 for LED technology and r is the official social discount rate. The Concessionaries forecast RI (US$) = ∑ n=10 i=1

-Eps (US$) + Esr (US$) + Elsr (US$) (1 + r)i

Eq. (B.4)

Where Rl (US$) loss of income obtained in the present value due to the replacement of T-12 efficient technology, Eps (US$) is the reduction in the purchase of electricity, Esr (US$) is the reduction of electricity sales to government (US $), Elsr (US$) is the reduction in energy sales to government and r is the discount rate dealer. Society’s Perspective SNPV = NVP (US$) - RI (US$) + ∑ n=10 i=1

Fa (1 + r)i

+ Tca

(1 + r)i

Eq. (B.5)

Where SNPV is the social net present value NPV (US$) is the net present value of the government (Eq. (B.3)), RI are the revenue losses of the Concessionaire to the present value Ec. (B.4) Fa are avoided costs of fuel oil, Tca avoided costs of transmission and r is the official social discount rate. C. General formulas Annualized cost of saved energy (CSE) CSE annualised (

US$ MWh

) = Tnecj • CRF(r,n) - (Tnecx • CRF(r,n)) annual CEMWh

Eq. (C.1)

Where CSE is the annualized cost of energy saved, TNECj are the total non energy costs of efficient technology option (it includes costs of initial investments and replacement), TNECx are the total non energy costs of conventional technology (investment and replacement services and initial costs), CRF(r,n) is the capital recovery factor for a social discount rate r a period n analysis and CEMWh is the energy saved annually by the energy efficiency program. Social profitability index (SPI) SPI = Valor actual de los flujos de caja futuros / Io

Eq. (C.2)

The relationship between the net present value of investments in energy efficiency and Io is the initial investment.

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D. Parity Analysis with the Network Leveled cost of electricity (LCOE) for photovoltaic systems T

t LCOE = ∑ t=0 (It + Ot + Mt) / (1+r) T ∑ t=0 (St)(1-d)t / (1+r)t

Eq. (D.1)

Where It the initial investment in the system (US$), Ot operating costs (US$), Mt maintenance costs (US$), St is the energy produced in the period t, d is the rate of degradation of modules (%) and r is the discount rate t (%). Eq. (D.1) is a small modification of Branker et al [57] because interest expenses were not considered in this exploratory study.

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