Enhancing Micro Energy Grid (MEG) Performance by Novel D‐FACTS based on GA‐ANFIS Integration

International Journal of Automation and Power Engineering, Volume 5 2016 www.ijape.org doi: 10.14355/ijape.2016.05.002

Enhancing Micro Energy Grid (MEG) Performance by Novel D‐FACTS based on GA‐ANFIS Integration Hossam A.Gabbar*, Ahmed M. Othman, Aboelsood Zidan, Jason Runge, Owais Muneer, Manir Isham, Mayn Tomal Energy Safety and Control Laboratory (ESCL), University of Ontario Institute of Technology, Canada Corresponding author: Hossam.Gabbar@uoit.ca

Abstract This paper concerns with enhancing Micro Energy Grid (MEG) performance by Novel Developed Flexible AC Transmission System (D‐FACTS) based on the integration of Genetics algorithm (GA) and Adaptive Neuro‐Fuzzy Inference System (ANFIS).

The design and development of MEG, with hardware demonstration, is developed at the Energy Safety and Control Laboratory (ESCL), University of Ontario Institute of Technology. The hardware/software based system includes implementation of control strategies for Distributed Energy Resources (DER) and programmable loads in a laboratory scale; and the appropriate software was developed to monitor all MEG parameters and to control the various components. The interconnection of renewable energy sources, such as wind power, solar PV and others, are implemented, studied and integrated into this MEG. Furthermore, gas based DERs operate as Combined Heat and Power (CHP) to supply both thermal and electrical loads. The design, development, and hardware setup of this MEG has been presented in a planning stage and an operational stage. Firstly, the planning stage optimizes the size and type of DERs for minimum cost and emissions. Then, in the operational stage, there will be the evaluation of the dynamic response to fine tuning the dynamic response. So a novel D‐FACTS device, Green Plug‐Energy Economizer (GP‐EE) with two DC/AC schemes, is proposed and integrated into this MEG. The integrated GA with ANFIS has been applied to control the settings of GP‐EE to fine‐tune the system dynamic response. The proposed controller ensures the adaptation of the global control error of dynamic tri‐loop regulation for GP‐EE. The proposed control strategy leads to get full MEG utilization by increasing the energy efficiency and reliability. Power factor improvement, bus voltage stabilizing, feeder loss minimization and power quality enhancement are realized and achieved. Hardware demonstration with digital simulations have been used to validate the results to show the effectiveness and the improved performance.

Keywords Distributed Energy Resources (DER); Micro Energy Grid (MEG); Dynamic tri‐loop Regulation; Green Plug‐Energy Economizer (GP‐EE), Adaptive Neuro‐Fuzzy Inference System (ANFIS).

Introduction Micro Energy Grid (MEG) is a relatively small‐scale localized energy network with energy sources and loads that normally operate connected to the traditional centralized grid, but can disconnect and function autonomously as physical and/or economic conditions. Due to local energy sources, MEGs require minimum energy transmission from/to remote regions. Hence, a modern distribution system can be composed of a number of interconnected MEGs to reduce: energy loss cost of a transmission network, and the risk of energy supply failure. Furthermore, MEGs are keen candidates to increase the penetration of renewable based Distributed Energy Resources (DER). As MEGs have DERs and energy storage systems, they can operate in grid connected mode or in islanded mode.

MEGs have multiple fuel based generators such as diesel, natural gas, and renewable generators (solar and wind).

DERs have the potential of being more efficient, cheaper and reliable. As DERs are located close to loads, the wasted heat from generators can be used to participate in covering the local heat demand [1‐3]. There are many technical challenges including flow control and reliability issue with the concept of MEG, so the requirement of an efficient and adaptive control is increased. Currently, natural gas is considered as one of the primary fuels for DERs, where one of the main applications of DERs is Combined Heat and Power (CHP) which supplies heat and

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electricity simultaneously. CHP and renewable energy sources are key elements in a MEG. Planning of CHP systems is playing an important role to realize and guarantee self‐sufficient, stable, and efficient MEG performance.

Microgrids, with CHP and renewable energy sources, achieve many positive aspects as increasing the reliability, minimizing the wasted heat and the energy emission, reducing the capital and running cost [4, 5].

Various developed devices are installed with DERs to manage the technical impacts. Switching power electronics with converters/inverters are used to allow controllable actions, to operate in the AC/DC systems. Various classifications of inverter designs are currently settled. IEEE standards classify inverters according to their border margins that rate the conversion from DC to AC. The main two categories for inverters are Current Source Inverter (CSI) or Voltage Source Inverter (VSI) [6‐8]. Further classifications can be stated according to the inverter technology such as device design, output voltage shape, firing network, power levels and others.

There are controlled devices as Developed Flexible A.C. Transmission Systems (D‐FACTS) that were built on technologies developed Switched Compensators and high‐power CSI and VSI‐electronic converters and drives. It allows fast and accurate controlled response, so that control action enhances the system dynamics. Green Plug‐ Energy Economizer (GP‐EE) is presented as a D‐FACTS installed with MEG for enhancing its performance. GP‐EE will be applied to control power quality aspects, to improve voltage regulation and power factor by minimizing transient content in voltage and current wave. The response of the system under the study for the operating cases with related GP‐EE parameters will show the effect to get the significant setting of those parameters to enhance the response. Without adapting the GP‐EE parameters, the desired benefits from installing the GP‐EE will be lost, where that may impair the system response.

Modern Artificial Intelligent (AI) techniques have many applications in various areas of power systems. Adaptive AI controllers can be compared with those conventional ones to show their effectiveness. Genetic Algorithm (GA) is an evolutionary technique that searches the global optimal solutions inside the problem space. GA realizes remarkable impacts in solving different numerical optimization problems [9, 11]. Adaptive Neuro‐Fuzzy Inference System (ANFIS) is a merging system between the neural network system and the fuzzy logic system; therefore, it has the resultant benefits of both systems. The integrated GA with ANFIS will be able to achieve the desired performance of the system when it is well designed and tested. Therefore, the integrated GA and ANFIS system will emphasize the proper selection and control of GP‐EE parameters.

In this paper, the first stage concerns with an optimal deployment with respect to capacity sizes and types of DERs and CHP systems within microgrids. The objective is to simultaneously minimize the total net present cost and carbon dioxide emission. A multi‐objective GA is proposed in that stage to handle the planning problem including the optimization of DER type and capacity. The constraints involve power and heat demands constraints.

Candidate DER technologies are CHPs with various characteristics, boilers, renewable generators (wind and photovoltaic), and a main power grid connection. Costs of CHP generators are based on their types and the capacity range. The results of that stage will be fed to the second operational stage to ensure that it works on the optimal patterns of the DERs. The second stage concerns with the operational control aspect. GP‐EE has been validated for enhancing the performance and power quality aspects of the MEG with DERs. A novel control adaptation for error driven dynamic multi‐loop regulator by the integrated GA with ANFIS system has been applied to improve the power quality and energy utilization. The PWM‐optimized pulsing sequence for the GP‐EE utilizes a tri‐loop dynamic error‐driven weighted modified PID controller. The proposed GP‐EE schemes are very effective in enhancing power quality, improving power factor, reducing feeder losses and stabilizing the buses voltage, and mitigating the harmonic distortion.

MEG and GP-EE Design with ESCL Demonstrations Fig. 1 shows MEG configuration included GP‐EE schemes for both AC and DC terminals. That MEG has different AC and DC DER units which are supplying different AC and DC loads. Based on the output of the planning stage, the AC sources are DFIG wind turbine generator and micro gas turbine generator. The DC sources are battery, Fuel cell stack based on Hydrogen and photovoltaic arrays. The design of microgrid will include boost converters

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(AC/DC, DC/AC and DC/DC converters) for reliable operation.

The CHP generators will be presented with various characteristics, boilers, thermal storage, renewable generators (wind and photovoltaic), and a main power grid connection. The candidate technologies included are Natural Gas Turbine (NGT), Hydrogen Fuel Cell (H2FC), Natural Gas Fuel Cell (NGFC), Wind Turbine (WT), Photovoltaic (PV).

NGT, H2FC, and NGFC are assumed to operate as CHPs to generate heat and power with fixed heat to power ratio.

The load profile for a year is divided into four seasons, each of which was represented by one day with total sampling hours equals 4*24=96 hour/year. Various types of DC and AC load are represented, such as resistive loads, motorized dc series motor load, linear AC loads, non‐linear AC loads and three phase induction motorized loads. The proposed GP‐EE Schemes are installed and selected according to the terminal where it will be inserted in. Proposed Design (A) of (GP‐EE) will be connected to the AC side where Proposed Design (B) of (GP‐EE) will be connected to the DC side. The global benefits from both of them will enhance the ripple and harmonics in the voltage profile, reducing power loss and enhancing the power factor. The detailed parameters of the micro grid, loads, DGs and D‐FACTS will be given later in the following sections.

FIG. 1. CONFIGURATION OF MG INCLUDED GP‐EE SCHEMES

The design MEG with hardware demonstrations is presented in Energy Safety and Control Laboratory (ESCL), University of Ontario Institute of Technology (UOIT). Starting from 2011, the members of ESCL are working on MEG with more than 5 projects and 20 publications. The data and analysis are simulated by using the UOIT models and Geographical Information System as indicated in Fig. 2

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FIG. 2. UOIT GEOGRAPHICAL INFORMATION SYSTEM

The proposed Green Plug‐Energy Economizer (GP‐EE) devices have two main schemes, as shown in Fig. 3 and Fig. 4, based mainly on a combination of capacitors, filters connections and MOSFET switches [12].

FIG. 3. PROPOSED DESIGN (A) OF (GP‐EE) FOR AC SIDE

FIG. 4. PROPOSED DESIGN (B) OF (GP‐EE) FOR DC SIDE

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Typically, a hybrid combination of shunt capacitors with a tuned arm power filter constitutes the main GP‐EE scheme (A) structure. Shunt capacitor banks are connected in parallel where they provide reactive power compensation and improve the regulation level. The capacitor works for dynamic voltage boosting and limiting the transient currents. The path of solid‐state switches has six pulses converter‐bridge plus resistance (Rf) and inductance (Lf) branch that structures a tuned arm filter. The main structure of Universal Bridge rectifier has six solid‐state switches (six pulse converter‐bridge) where it receives from 3‐phase source and delivers to 2‐wires load, where Vgg and Igg are the terminal voltage and current signals at GP‐EE device. The two MOSFET switches (S1 and S2) are controlled by two complementary switching pulses (P1 and P2) that are supplied by the dynamic tri‐ loop error driven modified dynamic PWM controller, which will be described later. First, pulse P1 directs S1, while second, pulse P2 directs S2. Procedure of the complementary PWM pulses can be explained as follow: Case 1: If P1 is high and P2 is low, the resistor and inductor will be fully short‐circuited and the device will provide the required compensation to the load.

Case 2: If P1 is low and then P2 is high, the resistor and inductor will be connected into the circuit as a tuned arm filter.

GP‐EE scheme (B) structure depends on switchable capacitor behind universal bridge with resistance (Rf) and inductance (Lf) branch, where Vd and Id are the terminal voltage and current signals at GP‐EE device. MOSFET controller is fed by errors signal of PWM module that generates switching pulses to filter switch.

Enhanced Dynamic PID Control The proposed modulated filter‐capacitor compensation scheme is governed by adapting the PWM pulsing signal switching of GP‐EE by selecting dynamic optimal gain values that dynamically reduce the global dynamic error.

The resultant of error signal will initiate the enhanced dynamic controller according to Tri‐loop driven error which is firing to feed the control unit of PWM switching circuit which will depend on the MOSFET gate to control the modified converter controller as shown in Fig. 5.

4

1

VS1

20e-3s+1 Product2

Transfer Fcn5

3 Transport Delay3

IS1 magnitude

Scope

0.9

signal angle

-K-

cos

globle_error

V_Fundamental1 Deg->Rad1

To Workspace

magnitude signal

To Workspace3

Scope2

angle

SB Kpp

I_Fundamental1 1 1

1

1/550

5e-3s+1

VL

RMS

Gain2

1

signal rm s

Kii

Transfer Fcn Gain

1 s

Signal(s) Saturation

Integrator

1

Pulse s

PWM Generator

Transfer Fcn2 Kdd

1

Gain1

20e-3s+1 Product

du/dt

2

Derivative

To Workspace2

0.25

Transport Delay1

SA Kee Product1

Gain3 Keea1 Scope1

0.5

Keeb1

1

To Workspace1

To Workspace4

20e-3s+1 Transfer Fcn3 0.25

2

-K-

signal rm s

IL

RMS1

1 5e-3s+1 Transfer Fcn1

Transport Delay

FIG. 5. ENHANCED DYNAMIC CONTROLLER BASED ON TRI‐LOOP DRIVEN ERROR

There will be GP‐EE devices with 4 variables, both VS and IS are the composite voltage and current of the source

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bus of GP‐EE; and both VL and IL are the composite voltage and current of the load bus of GP‐EE. The global error (e1) for the first GP‐EE is the resultant of the six‐loop individual errors including: (1) VL‐ Load Bus Voltage Stabilization Loop by tracking the error of the load voltage and regulating the voltage to near unity.

(2) IL‐Dynamic RMS‐Current Minimization Loop to compensate any sudden current changes or oscillations.

(3) PL‐Excursion Dynamic Damping Loop for power oscillations.

(4) Load Ripple and Transient Current Damping Loop to reduce the harmonic ripple content in the system.

And there will be two novel extra supplementary loops to ensure the source power factor correction and the oscillations in the source current. The main per‐unit three dimensional error signals are (eVL ,eIL, ePL) and the other error signals are derivates form them, those main error signals are governed by the following equations:

∶

∶

. .

. .

. .

1

∗

. .

1 1

. .

1

1 1

∶

1

. .

∗ 1

1 ∗

2

1

3

4

The global error (e2) for second GP‐EE and and the global error (e3) for third GP‐EE are calculated in the same way of e1. The optimization search algorithm is implemented for tuning PID controller gains (Kp, Ki and Kd), for the three D‐FACTS devices to minimize the system objective function. The proposed objective is defined by the global errors of all Controllers: OF = minimization (e1+e2+e3)

(5)

Inverter Design and MOSFET Choice within the Designed MEG This section concerns with the design of the inverter elements inside the proposed MEG in Fig. 1 due to its impact.

A full h‐bridge inverter shown in Fig. 6 is proposed to clarify the inverter design. The DC terminals are connected to the inverter input. The PWM sub‐circuits are connected as switch trigger of the circuit. The inverter output is connected to the load.

FIG. 6. INVERTER H‐BRIDGE

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It is important to appropriate select and control of MOSFET, because it has a critical impact on the inverter operation. Exact models of MOSFET are used to realize the full bridge micro‐inverter. Wide range of MOSFET manufacturers are tested, the best selection is n‐channel IXYS GigaMOS MOSFET due to its characteristics as voltage rate, current rate, switching times, its minimal on‐state resistance and power losses [13, 14]. The current handling capability of this MOSFET is rated at 320A continuous current, and as it is mentioned that is the best selection of MOSFET and it is matched with the rating of MEG under operartion. With refereeing to Fig. 1 which indicated MEG configuration, the inverter will be connected to two places within MEG, one in the link between utility grid and DC section between buses V1 and Vd1, in addition to between the DC section and AC section between buses Vd1 and Vg. The switch gate can operate at 4V without any excess in the thermal losses. The maximum drain voltage is 170V due to the spikes and transient contents. At the operating conditions for the inverter of 600V and maximum of 85A, the required power to be handled by the inverter is 50kW, and the closest power handling capability is by 55kW MOSFET. DC boost converter is applied to support the voltage for the inverter to realize the required voltage levels with keeping much efficiency [15].

Integrated GA with ANFIS The resultant global error is the summation of individual multi error loops; each multi dynamic leading loop is used to minimize the global error based on a tri‐loop functional error signal in addition to other supplementary power factor correction and/or feeder currents limiting for loss reduction [16, 17].

To enhance the dynamic response of the system, the integrated GA with ANFIS will be applied to control the parameter settings of PID section to fine‐tune the system dynamic response.

GA system mainly depends on initial population, natural selection, mating, and the mutations. In the case of continuous fitness function characteristics [18], it is better to apply the continuous genetic algorithm due to its accuracy and needs less memory size. Fig. 7 shows an overview of a continuous GA flowchart.

Define Parameters fitness function Create population Evaluate fitness Select mate Perform crossover Mutate Test convergence Stop FIG. 7 A CONTINUOUS GA FLOWCHART

The fitness trends will control the initial population selection. It is sorted according to the fitness value. Individuals with the highest probability will be chosen. If P1 and P2 are chosen according to crossover, the offspring based on crossover are:

P`1 = r.P1 + (1-r).P2

(6)

P`2 = (1-r).P1 + r.P2

(7)

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where r is a random number between 0 and 1 Mutation presents a new solution in the generation for trial to allow random effect in various individuals. When element Pk of a parent P is selected for mutation, the result would be:  P  (UB  Pk ) f ( gen) Pk   k  Pk  ( LB  Pk ) f ( gen)

if a random digit is 0 if a random digit is 1

(8)

where UB and LB are the upper and lower bounds of the individual Pk respectively, gen is the current generation. f(gen) will give output a value in the range [0,1]. And where the probability of f(gen) increases at gen increases. The following function is applied to confirm searching in uniform in the solution space: f ( gen)  (r.(1 

gen )) b gen max

(9)

where r is a random number in the range [0, 1], genmax is generation’s maximum, and b is called non-uniformity shape.

Adaptive Neuro‐Fuzzy Inference Systems (ANFIS) ANFIS applies rules to optimize the fuzzy system parameters of a Sugeno system. The Sugeno fuzzy system is proposed to allow a systematic method to produce fuzzy rules of a certain input/output patterns. The ANFIS training systems use a gradient descent algorithm to optimize the antecedent parameters and a least‐square algorithm to get the consequent parameters [19, 20]. In the first part, least squares algorithm is applied for adapting consequent parameters and then the antecedent parameters are modified by backpropagating the errors.

ANFIS result is expressed as: y m  w1 f 1m  .........  w n f nm (10)

Where m is the output number and n the number of the input membership functions. fnm is expressed as follow: f nm  p nm x1  q nm x 2  rnm (11)

So ANFIS result will be modified to:

y m  [ w1 x1

w1 x 2

w1  wn x1

wn x1

 p1m  q   1m   r1m    wn ]     XWm (12)  p nm     q nm  r   nm 

Pseudo inverse will be applied to estimate the vector Wm, i.e.

Wm  (X

T

X ) 1 X

T

y m (13)

The output of antecedent will be expressed as:

a(T  1)  a(T ) 

 a E k a

(14)

where: a: Parameter to be updated

T: Epoch number a: Learning rate

k: Number of input patterns E: Sum of square error (SSE) Each derivative to modify the antecedent parameters will be estimated by the chain rule as:

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E E y wi  iL (15) . . .  a iL y wi  iL a iL

Where i : membership function number , L and y : sum of ANFIS outputs , S : inputs number.

Fig. 8 depicts a novel integrated GA with adaptive ANFIS controller to self‐regulate PID tri‐loop stage for SS‐EE Device. The proposed technique will achieve reliable and efficient performance. It can realize both criteria of power quality and enhancement of the voltage, current and power factor profiles. The adaptive controller is shown in structural layers. ANN will be co‐operative with Fuzzy Logic (FL) to construct ANFIS system. GA will be in co‐ operative layer for resultant ANFIS system to save pre‐training patterns to ANFIS to act as the training patterns.

FIG. 8. STRUCTURAL LAYERS OF THE ADAPTIVE CONTROLLER

GA will be able to reach the optimal solutions and will realize the desired performance for the system under any considering various operating conditions. ANFIS merges between artificial neural networks and fuzzy logic algorithms. So it has comprehensive options of both of them. FL is an effective technique control process while ANN has distinguished characteristics in classifications and recognitions. ANN are linked with FL to update the parameters of the fuzzy systems to achieve the best parameters tuning leading to the requested controlling process.

Therefore, the positive behaviors from both of ANN and FL are realized by merging them to catch the global benefits.

Case Study Description And Simulation Results The digital simulations, Matlab‐Simulink‐SimPower platform with support of hardware demonstrations will concern with MEG Study System as the following specifications: 

Utility Grid: 138 kV, 5GVA, X/R=10



Micro Gas Turbine: V=1.6 kV, P= 2000 kW.



Wind Turbine Generator: V= 1.6 kV, P= 1 MW.



PV: 240V, 500 kW, Ns = 318, Np = 150,Tx = 293,Sx = 100, Iph = 5, Tc = 20, Sc = 205



Fuel Cell: 240 V, 2000 kW, number of Cells = 220, nominal Efficiency, 55%,



Battery: 240 V, Rated capacity: 300 Ah, Initial State‐Of‐Charge: 100%, discharge current: 10, 5 A,



Hybrid AC Load 1: linear load: 0.1 MVA, 0.8 lag pf, non‐linear load: 0.2 MVA, Motorized load is an induction motor: 3phase, 0.3 MVA, 0.85 pf.

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

Hybrid AC Load 2: linear load: 200 kVA, 0.8 lag pf., non‐linear load: 200 kVA, Motorized load is an induction motor: 3phase, 100 kVA, 0.8 pf.



DC Load: resistive load: 100 kW, motorized load dc series motor: 100 kW.



GP‐EE(A): Cf1= 50 μF, Rf1 = 0.25 Ω, Lf1 = 3mH



GP‐EE(B): Cs= 100 μF , Rs=0.1 Ω , Ls = 10 mH

The first stage is a planning stage, concerning with an optimal deployment with respect to capacity sizes and types of DERs and CHP systems within microgrids. The objective is to simultaneously minimize the total net present cost and carbon dioxide emission. A multi‐objective GA is proposed in that stage to handle the planning problem including the optimization of DER type and capacity. The constraints involve power; and heat demands constraints.

Candidate DER technologies are CHPs with various characteristics, boilers, renewable generators (wind and photovoltaic), and a main power grid connection. Costs of CHP generators are based on their types and the capacity range.

The Planning Stage The candidate technologies included are: natural gas turbine (NGT), hydrogen fuel cell (H2FC), natural gas fuel cell (NGFC), wind turbine (WT), and photovoltaic (PV). NGT,H2FC, and NGFC are assumed to operate as CHPs (generate heat and power with fixed heat to power ratio). The load profile for a year is divided into four seasons, each of which was represented by one day, with total equals 4*24=96 hours. Then, total cost (capital, operational, and maintenance) and CO2 emission are calculated. It will change based on the daily load profile, and Table 1 summarizes the results of optimal planning.

TABLE 1 OPTIMAL PLANNING RESULT

Case Min cost only Min emission only Compromised

NGT (kW)

H2FC (kW)

NGFC (kW)

WT (kW)

PV (kW)

Total cost ($)

1048

13

0

27

102

1.3679×105

Total CO2 emission (kg) 8380

391

1300

0

90

6

7.2310×105

4306.94

1179

751

0

35

406

6.5109×10

4876.4

5

The results of that stage will be fed to the second operational stage to ensure that it works on the optimal patterns of the DERs. The second stage concerns with the operational control aspect. GP‐EE has been validated for enhancing the performance and power quality aspects of the MEG with DERs. The Operational Stage A novel control adaptation for error driven dynamic multi‐loop regulator of proposed GP‐EE by the integrated GA with ANFIS system has been applied to improve the power quality and energy utilization. The proposed GP‐EE schemes with controlled GA with ANFIS are very effective in enhancing power quality, improving power factor, reducing feeder losses and stabilizing the buses voltage, and mitigating the harmonic distortion. The integrated GA and ANFIS system is designed with the following parameters as shown in Table 2; and then it is applied to get the optimal PID values of GP‐EEs as shown in Table 3.

TABLE 2 GA AND ANFIS SYSTEM PARAMETERS

GA Parameters Options. PopulationType Options. PopulationSize Options. EliteCount Options. CrossoverFraction Options.MigrationDirection Options. MigrationInterval Options.MigrationFraction: Options. Generations

Double Vector 35 Adapted in simulations Adapted in simulations Forward 35 0.21 280 ANFIS Parameters

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Number of nodes: 43 Number of linear parameters: 70 Number of nonlinear parameters: 31 Total number of parameters: 83 Number of training data pairs: 38 Number of checking data pairs: 29 Number of fuzzy rules: 22 Designated epoch number ‐‐> ANFIS training completed at epoch 325.

TABLE 3 VALUES OF OPTIMIZED PID CONTROLLERS GAINS

Kp 12 15 11

GP‐EE(A) 1 GP‐EE(A) 2 GP‐EE(B)

Optimal Values of PID Controllers Gains Ki 2.4 5 1.7

Kd 0.5 0.2 0.2

The digital simulation is carried out with and without the controlled GP‐EE located in order to show its performance in voltage stabilization, harmonic reduction and reactive power compensation at normal operating condition. Also, reduction of the enhancement in the power factor managing the exchange power between MG and utility grid is totally achieved as shown in Fig. 9 to Fig. 14. The dynamic responses of voltage, current, active power, reactive power, apparent power, power factor, frequency spectrum (for voltage and current), (THD)v and (THD)i at source buses and load buses with comparison of harmonics at each bus are made in the case of with and without GP‐EE. Voltage and current harmonic analysis in term of the total harmonic distortion (THD) is given. It is obvious that the voltage harmonics are significantly reduced and also the THD of current waveform at each bus is decreased.

Vs Bus

Vs Bus

Vs Bus

1

Vs Bus

1.2

1

1 1 0.8

0.4

0.6

0.4

0.6 0.4 0.2

0

-0.5

0.2

0.2 without with FACTS 0

0.5

0.8 power factor

0.6

Reactive power (pu)

Current (pu)

Voltage (pu)

0.8

0

0.5 Time (sec)

0

without with FACTS 0

1

0

0.5 Time (sec)

-0.2

1

without with FACTS 0

0.5 Time (sec)

-1

1

without with FACTS 0

0.5 Time (sec)

1

FIG. 9. VOLTAGE, CURRENT, REACTIVE POWER AND POWER FACTOR AT BUS VS, WITHOUT/WITH D‐FACTS V1 Bus

V1 Bus

1

1

0.8

0.8

V1 Bus

V1 Bus

1.2

1

0.4

0.2

0

0.6

0.4

0.5

0.8 power factor

0.6

Reactive power (pu)

Current (pu)

Voltage (pu)

1

0.6 0.4 0.2

0

-0.5

0.2

0

without

without

with FACTS

with FACTS

0.5 Time (sec)

1

0

0

0.5 Time (sec)

0

without

without

with FACTS -0.2

1

0

0.5 Time (sec)

with FACTS 1

-1

0

0.5 Time (sec)

1

FIG. 10. VOLTAGE, CURRENT, REACTIVE POWER AND POWER FACTOR AT BUS V1, WITHOUT/WITH D‐FACTS

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VL Bus

VL Bus

VL Bus

1

1

without with FACTS

1 0.8

0.8

0.4

Reactive power (pu)

Current (pu)

0.6

0.6

0.4

power factor

0.5

0.8 Voltage (pu)

VL Bus

1

0.6

0.4

0

-0.5 0.2

0.2

0.2

without 0

0

without

without

with FACTS 0.5 Time (sec)

0

1

0

0.5 Time (sec)

0

1

with FACTS

with FACTS 0

0.5 Time (sec)

-1

1

0

0.5 Time (sec)

1

FIG. 11. VOLTAGE, CURRENT, REACTIVE POWER AND POWER FACTOR AT BUS VL, WITHOUT/WITH D‐FACTS

Vg Bus

Vg Bus

Vg Bus

Vg Bus

0.8

0.8

0.6

0.4

1

without with FACTS 0.5

0.6

0.4

0.5 power factor

1

Reactive power (pu)

1

Current (pu)

Voltage (pu)

1

0

-0.5 0.2

0

0

-0.5

0.2

0

without

without

with FACTS

with FACTS

0.5 Time (sec)

0

1

0

0.5 Time (sec)

-1

1

0

without

0.5 Time (sec)

with FACTS 1

-1

0

0.5 Time (sec)

1

FIG. 12. VOLTAGE, CURRENT, REACTIVE POWER AND POWER FACTOR AT BUS VG, WITHOUT/WITH D‐FACTS

Vdc Bus

Vdc Bus

Vdc Bus 1

0.8

0.8

0.8

Voltage) Voltage (pu)

0.6

0.4

Power (pu) Power)

1

Current (pu) Current)

1

0.6

0.4

0.2

0.4

0.2

0.2

without with FACTS 0

0

0.2

0.4 0.6 Time (sec)

0.8

0.6

without with FACTS 1

0

0

0.2

0.4 0.6 Time (sec)

0.8

without with FACTS 1

0

0

0.2

0.4 0.6 Time (sec)

FIG. 13. VOLTAGE, CURRENT AND POWER AT BUS VDC, WITHOUT/WITH D‐FACTS

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0.8

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International Journal of Automation and Power Engineering, Volume 5 2016 www.ijape.org

error e2

error e1 0.05

error e3

0.016

0.14 0.12

0.04

0.014 0.1

0.03

0.08

0.02

error

error

error

0.012

0.01

0.06 0.04

0.01

0.02 0.008

0 -0.01

0 0

0.5 Time (sec)

1

0.006

0

0.5 Time (sec)

1

-0.02

0

0.5 Time (sec)

1

FIG. 14 GLOBAL ERRORS E1, E2 AND E3 OF MULTI‐LOOP CONTROLLERS OF THE D‐FACTS

The high performance operation of this micro grid is achieved using the optimal values of PID controller’s gains.

As shown in Fig. 9‐14, the used D‐FACTS have the capability to compensate the reactive power at the AC and DC buses to stabilize the voltage profile and enhance the power factor. Fig. 9 shows the voltage, current, reactive power and power factor at the utility grid bus, Vs. The power factor is improved to 0.9 lag from 0.07 lead, this means the reactive power is transferred from the microgrid to the utility grid after using the D‐FACTS. Also with using the D‐FACTS, the voltage profile is stabilized at 1 per unit and the power factor is improved at the AC bus V1, as shown in Fig. 10. The reactive power compensation by D‐FACTS is very clear at the AC sub‐grid bus, Vg and the voltage profile is improved and stabilized at 1 per unit, as shown in Fig. 12. Also, the DC sub‐grid bus voltage is improved and stabilized, as shown in Fig. 13, using the D‐FACTS. From all the previous figures, it can be observed that controlled GP‐EE mitigates the harmonic and can achieve other technical benefits. Comparing the dynamic response results without and with the proposed GP‐EE, it is quite apparent that the proposed GP‐EE enhanced the power quality, improved power factor, compensated the reactive power and stabilized the buses voltage.

The proposed technique solved the power quality problems associated with voltage quality and with current quality considered as “Harmonics distortions, Waveform distortions” by THD% that results in over current, voltage flickering, different type at the loads side.

The THD at all AC buses are calculated and are summarized in Table 4. In this table the reduction of the harmonic orders of microgrid voltage and current without D‐FACTS connection as compared the harmonic order values with D‐FACTS connection are shown and all THD values for voltage and current waveforms are enhanced to be within limits of the voltage.

TABLE 4 PERCENTAGE THD OF VOLTAGE AND CURRENT AT THE BUSES

Without D‐FACTS With D‐FACTS

VS

V1

VL

Vg

THDv

THDi

THDv

THDi

THDv

THDi

THDv

THDi

0.62

7.25

35.5

21

29.3

36.8

26.7

18.5

0.1

4.55

4.82

4.56

4.4

4.92

4.61

4.2

Conclusions This paper presents a design of Micro Energy Grid (MEG) with hardware demonstrations in Energy Safety and Control Laboratory (ESCL) , University of Ontario Institute of Technology (UOIT). Distributed Energy Resources (DER) are widely used in MEGs to match the various load types and profiles. DERs include Solar PV Cells, wind energy, fuel cell, battery, and micro gas‐engine and storage elements. MEG will have both of DERs and gas power

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sources as Combined Heat and Power (CHP), supplying thermal and electrical loads. MEG will include AC/DC Circuits, FACTS, inverters and power electronics controllers. Novel DFACTS device and Green Plug‐Energy Economizer (GP‐EE) with two DC/AC schemes will be installed in MEG design. The integrated Genetics algorithm (GA) with Adaptive Neuro‐Fuzzy Inference System (ANFIS) will be applied to control the parameter settings of GP‐EE to fine‐tune the system dynamic response. The integrated GA and ANFIS will adapt the dynamic gains of PID regulation tri‐loop. The proposed controller ensures the adaptation of the global control error to operate with online optimal profile of. The proposed strategy leads to get full MEG utilization by increasing the energy efficiency and reliability that ensure various technical impacts. Power factor improvement, bus voltage stabilizing, feeder loss minimization and power quality enhancement are realized and achieved. The article will concern with two stages: Planning platform and Operational platform. The planning stage concerns with collecting load profile for one year, 4 seasons, for both thermal and electrical loading conditions and to optimize the size of each power source type based on cost and emission. The operational stage will evaluate the dynamic response, based on the assigned rates from the planning stage, then to fine tuning of the dynamic response by DFACTs based on integrated GA with ANFIS. Hardware demonstration with digital simulations have been used to validate the results to show the effectiveness and to improve performance using the proposed strategy.

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