Heating and Cooling with ASHP and AHU system for Mobile Temporary Tiny House Clusters by Marius Lazauskas

Heaating aand Co ooling with A Air Sou urce Heeat Pu ump and A Air Han ndling Unit ssystem m for M Mobile T Tempo orary TTiny Ho ouse C Clusterrs

Marius Laz M zauskas I D: 075647 72 15tth April 20 016 Jan n Hensen /// Moham med Hassa n Mohame ed // Luyii Xu Building PPhysics an d Servicess Eind dhoven Un niversity o of Technology

Heating and Cooling with Air Source Heat Pump and Air Handling Unit system for Mobile Temporary Tiny House Clusters

Marius Lazauskas ID: 0756472 Supervised by: J.L.M. Hensen, M.H. Hassan Mohamed, L. Xu, A. Papadopoulos Unit Building Physics and Services Eindhoven University of Technology Eindhoven, the Netherlands The possibility of implementing central heating and cooling system for Heijmans ONE mobile temporary Tiny House cluster was analyzed. Heijmans ONE was treated as a case study to examine central heating and cooling system feasibility for Tiny Houses. Thermal comfort and life‐cycle cost were chosen as the performance indicators. TRNSYS was used to model 10 unit Heijmans ONE cluster in Existing Case (Electric Under Floor Heating) and Investigated Case (Central Air Source Heat Pump partially powered by a Photovoltaic system and an Air Handling Unit for heating and cooling within the units). The Existing Case simulation results were validated by previous research outcomes and used to size Air Source Heat Pump for Investigated Case. Indoor temperatures showed that Investigated Case provides better comfort as the Existing Case has no cooling capability. Indoor summer temperatures were on average 0.3°C and during peaks up to 3°C lower for Investigated Case. Underheating hours were also no longer present in Investigated Case. Regarding life‐cycle costs, central heating and cooling pays off after 9 years. All the equipment for central heating and cooling is stationed in a shipping container. This allows the system to be used for the lifespan of Heijmans ONE (30 years).

Nomenclature H1 TH DC DHW CHP mCHP HP ASHP HVAC GSHP PV nZEB ZEB PEB IR

Heijmans ONE Tiny House Direct Current Domestic Hot Water Combined Heat and Power micro Combined Heat and Power Heat Pump Air Source Heat Pump Heating, Ventilation, and Air Conditioning Ground Source Heat Pump Photovoltaic nearly Zero Energy Buildings Zero‐Energy Building Positive‐Energy Building Infra‐Red

FIR PVT AHU RES STE UFH STES ICE TES CBPS COP MRT DHC DH PMV PPD

Far Infra‐Red Photovoltaic Thermal hybrid solar collectors Air Handling Unit Renewable Energy Source Solar Thermal Energy Under Floor Heating Seasonal Thermal Energy Storage Internal Combustion Engine Thermal Energy Storage Computational Building Performance Simulation Coefficient Of Performance Mean Radiant Temperature District Heating and Cooling District Heating Predicted Mean Vote Predicted Percentage of Dissatisfied

1. Introduction Energy sharing among new Positive‐Energy Buildings and old energy inefficient buildings can form part of the solution for net zero energy consumption goals of the built environment [1]. PEBs generate more RES energy annually than they consume. Surplus RES is exported to the grid (Electrical) or the DHC system (Thermal). They differ from ZEBs by having negative net energy consumption over a typical year [2, p. 3067]. Energy inefficient buildings generate less RES energy annually than they consume. Energy deficit is met by burning fossil fuels, importing energy from the grid (Electrical) or DHC system (Thermal). ZEBs are energy‐efficient building where, on a source energy basis, the actual annual delivered energy is less than or equal to the on‐site renewable exported energy [3, p. 4]. The buildings with poor energy performance will form a major part of real estate in the future as they are the houses, offices, shopping centers of today. Energy consumption in these buildings will be eased by renovations, more energy efficient appliances, occupancy sensors and other, but in some cases fundamental limitations will be reached and negative energy balance of these buildings will remain. Local energy sharing in a form of district heating and/or cooling can form part of the solution. Nowadays district heating is centralized with dedicated plants, usually CHP units, providing hot water to consumers in nearby vicinity [4, p. 185, Fig. 31]. As with the case of smart grids, in electricity distribution, future can bring decentralized hot and/or cold water production, when energy positive buildings become widely adopted. This surplus thermal energy can then be used to cover the deficit created by old energy negative buildings. Furthermore energy performance of a building also greatly depends on occupant behavior and the same dwellings can have very different energy consumption profiles. DHC can allow distribution of energy from more efficient units to less deficient ones and as a result reach better cumulative energy performance both in sustainability and financial terms. Heijmans ONE can provide a testbed for evaluating various types of DHC systems for Tiny Houses. Long‐term goal of such THs as H1 is to provide temporary housing solutions at an affordable price [5], [6]. The core of this initiative is formed by using high quality of life housing and temporally placed in derelict sites [7]. These locations are picked within cities with large demand for accommodation, which there is plenty of in the Netherlands. Moreover the chosen sites are

strategiccally located d (To make THs more attractive to t potential tenants): nnext to city centers, transporrtation hubs, etc. The fact that idlingg land is used temporarily until its reedevelopment starts, means tthat costs fo or its interm mittent utilizzation are sm mall. As a result larger investmentss can be allocated for design ning and manufacturing of the build ding, which through its lifespan of 30 years o be relocateed at least 6 times. Planeed fixed rentting price of H1 means tthat the morre energy ought to efficientt the house iis going to be, the smalleer the utilityy costs and g greater pote ntial paybacck for the developer. Consequently Heijma ans is lookingg for ways of improve en nergy perfor mance of H1 1s, which are goin ng to be standing in clusters of 3 to 110, where DH HC systems ccan provide bbetter life‐cyycle costs and com mfort. H1 caase study is going to proovide an inssight into central space conditioningg system feasibilitty in THs.

2. Pro oblem defiinition There arre a couple of TH clusters, which ar e being plan nned to be b built around the Netherlands [8], [9] and A ASHP DHC syystem can in ncrease theirr energy perrformance an nd comfort. Energy conssumption for spacce heating an nd conditioning can varyy greatly for tthe same typ pe dwellingss. This has a lot to do with occcupant behaavior – lifesttyle habits aand indoor comfort c prefferences [4, p. 139]. This makes ASHP caapacity selecction compliccated as it hhas to meet heating and d cooling dem mands, have e enough reserve and avoid ovvercapacity. For this reasson Coincide ence Factors and Load Disstribution Cu urves are used as a single largge ASHP can have lower ccapacity than the combination of sepparate systems – the d does not ap ppear at the same time, so the maxiimum capacity of single ASHP can be e smaller demand [10].

Fig. 1 Existing C F ase – Local elecctric UFH system m

There arre a couple o of considerattions that haave to be takken into acco ount when sselecting DHC system for H1 clusters:

C equipment has to be as easy to traansport and setup as H1 houses. 20 fft shipping ccontainer 1. DHC (Len ngth – 5.87 m; Width – – 2.33 m; Heeight – 2.65 5 m [11]) is going to hoouse all the heating, coolling, distribution and PV inverter equuipment (Fig.. 2). This is going to satisffy DHC requirements of 3 to 10 Heijmans ONE units; or DHC equip pment to bee mobile abolishes insta allation posssibilities of STES S and 2. Requirement fo GSH HP; 3. ASHP meets thee requiremen nts for mobiility. On the other hand investment costs for local ASHP heatting has been shown to h have long paay‐off period (>9 years) [1 12, p. 39]; 4. DHC C systems require greate er initial finaancial investm ment for the e distributionn network, tthere are heatt loses in the network a and greater rrequirementts for mainte enance – thiis all increasses costs. Life‐‐cycle costs aanalysis will reveal if thee extra investment is worth it, when compared w with local system (Fig. 1) life‐cycle costts. W is not going to be in nvestigated in this rese earch, only space s heatinng and cond ditioning. 5. DHW

Fiig. 2. Investigatted Case – Centtral ASHP and A AHU Heating and Cooling systeem

2.1

Sccientific relevance

Europeaan Parliamen nt 2012/27/E EU energy effficiency dire ective sets go oals for new w buildings to o be built as Passivve Houses byy 2020 [1]. E Even though Passive Hou uses were first built in th e 1990s theyy are still more an n exception tthan the norrm. Besides Passive Hou uses there arre competingg design fram meworks of ZEBs aand PEBs, which take acttive buildingg system desiign approach h. Active eneergy efficientt systems are the ones that caan be retrofitted into exxisting building stock to make it morre sustainable. Many technolo ogical as well as financial hurdles ha ve to be ove ercome in the coming yeears in orderr to allow this to h happen. DHC C can be partt of the soluttion and various options have to be evaluated in n techno‐ economic terms. Th his research will comparre ASHP DHC system for Tiny Housse clusters with w local heating alternativess. Such info ormation proovides value e for selecting active bbuilding systtems for buildingg retrofitting or new consstruction proojects.

3. Research que estions The main questions which are go oing to be annswered via tthis research h are: 1. How w does air conditioning afffect comforrt levels in H1 1? 2. Is Radiative Loccal UFH morre efficient tthan Convecctive Central ASHP AHU U Heating in primary ption terms? enerrgy (Electricity) consump 3. How w does UFH life‐cycle costs compare tto Central ASSHP AHU life e‐cycle costs??

3.1

Previous Ressearch

Previouss research w was conducte ed on selectioon of most suitable heating system ffor H1 [12]. LLocal and DH systems were compared c – 6 in total ( 3 local and 3 district). In the long term (>9 years) DH systems provided beetter energy efficiency a nd financial benefits. H1 1 clusters ougght to be sta anding in the sam me derelict site s for no longer l than 5 years. Th his means th hat for easyy transportation and installation local heating system m was preferrred. Electricc radiators were w selecteed over central ASHP nd electric b oiler for DHW W. AHU sysstem for spacce heating an Addition nal research was carried out on makiing H1 nZEB [13]. Simula ated heating and cooling demand for H1 p prototype was w respectivvely 1464 kW Wh and 471 1 kWh. Infilttration rate was lowere ed, triple glazing installed and external shading s addded – these measures lowered thee proposed H1 nZEB demand to 5 550 kWh for heating and 247 kWh for cooling. Part of the prim mary energy demand energy d 2 for heatting was mett by 8*1.65 m m PV panel s installed o on the roof. H However thiis was not en nough to make H11 a nZEB building.

4. Meethodologyy Simplifieed H1 modeel is going to o be createdd in TRNSYS and validate ed (4.2.3 Moodel validatiion) by a detail m model done in n previous re esearch [13, p. 34]. TRNSSYS Type660 and Type7559 is going to o be used for the simplified model. m The detail d mode l was create ed using TRN NSYS Type566 and it com mes with measureement data.

Fig. 3. Metho odological chartt explaining datta sources used d in the report

3 occup pant behavio or scenarios will be impplemented (4.3 Scenario os) and meddian values of HVAC demand ds, loads and d comfort levvels extracteed. The extraacted values will be usedd to compare e existing

H1 UFH case (4.4 Existing Case) comfort levels and life‐cycle costs with the proposed ASHP AHU DHC case (4.5 Investigated Case).

4.1

Performance indicators

Two performance indicators are used to evaluate the performance of existing case (4.4 Existing Case) and DHC case (4.5 Investigated Case) H1 clusters: Comfort levels and Life‐cycle costs. 4.1.1 Comfort levels For comfort level indication occupied overheating and underheating hours are going to be used. Overheating hours occur when indoor temperature exceeds 28°C [14, p. 4]. Underheating hours occur when indoor temperature drops below 18°C. 4.1.2 Life‐cycle costs Life‐cycle costs are a sum of initial investment, operational and maintenance costs. Initial investment costs will be calculated by referring to equipment supplier pricing databases. Operational costs will be calculated from annual space conditioning energy demand. Literature will be used to find reference DHC system maintenance costs.

4.2

Modeling

To allow greater flexibility it was decided to simplify H1 TRNSYS model. The new model uses TRNSYS Type660 single thermal zone instead of TRNSYS Type56 3 zone model used in previous research [13]. H1 overhang shading was incorporated into the model by adapting SHGC value (Fig. 20). The new Type660 model was validated by using measurements and detail model simulation results (4.2.3 Model validation). Type660 was used for Existing Case as it has integrated heating and cooling capabilities (Fig. 31). UFH converts >95% electricity into usable heat, so inbuilt heating and cooling capabilities of Type660 were enough to simulate the indoor climate. Type759 was used for Investigated Case – it has no heating and cooling capabilities (Fig. 35). Heating and cooling systems as well as the controls were configured separately. The heating and cooling was provided by AHU’s heating and cooling coils, thermostats were used for the controls. 4.2.1 TRNSYS simulations Single H1 unit was simulated with 3 different scenarios in Existing Case and demand, load, overheating and underheating hour values were recorded (Fig. 6). This information was used to select ASHP and AHU power for DHC case (4.5 Investigated Case). The building envelope receives no changes in‐between the existing case and proposed DHC case.

4.2.2

H1 cluster laayout

Fig. 4 4. H1 cluster layyout. Front view w and Top view w. Spaces betwe een the units – 0.7 m

10 unit H H1 cluster iss arranged acccording to tthe Fig. 4 site plan. One unit represeents a single Zone. As with thee detail mo odel ([13, p. 34]) the H H1 10 unit clusters to uses “NL‐A Amsterdam‐SSchiphol‐ 62400.tm m2” weatheer file. File da ata indicatess that weath her station lo ocation is at t 52°18'0"N 4 4°46'2"E. General properties o of H1 are pre esent in Fig. 118 and Fig. 2 20 of this rep ports appenddix. 4.2.3 Model valid dation Heating power of UFH U was calculated to bbe 2.5 kW (4 4.4.1 Electric UFH). Meaasurement data d was th th recorded d from 27 of April 2015 until 15 of Sep ptember 20 015 [13, p. 11]. No additional measureements weree taken. As a result theere are no measurements to validaate heating demand simulations for the peak winterr season. Foor validation purposes th he simplifiedd H1 model was also p time – 6264 h; time steep – 1 h. simulateed for that giiven period: start time – 2856 h; stop Source Measu ured Detail model (Typee56) Simpliffied model (TType660)

emand; Heating de [kWh], [kW Wh/m2] 156.48 (3.4 48) 110.07 (2.4 45) 138.82 (3.0 08)

Fig. 5. H11 heating demaand from 27th o of April 2015 unntil 15th of September 2015 – measurementss and simulatio ons [13, p. 14]

4.3

Sccenarios

Scenario os will allow w comparisons to be maade between local centtral space coonditioning systems. Comfortt and financiial indicatorss will provid e the requirred assessme ent informattion. Scenariio Model validatio on is setup to match sim mulation propperties of prrevious resea arch to provvide simplifie ed model approacch validation n. Scenario is going to be almost the same as Scenario , EExisting Case e has no cooling eequipment, so there is no cooling settpoint.

4.3.1 Occupant behavior Occupancy behavior accounts for about 30% of the variance in overall heating consumption and 50% in cooling consumption. In addition, overall energy savings of 10%–20% due to simple behavioral adjustments are a reasonable expectation [15]. As a result 3 occupant behavior profiles were created, which would allow getting average values for H1 clusters. For heating setpoint the lowest temperature was chosen to be 18°C [16, p. 4]. For cooling setpoint the highest temperature was chosen to be 28°C, which would require the occupant to be energy conscious and use a local fan [14, p. 4]. Nonetheless 28°C and higher temperatures were recorded during summer time in H1 (Appendix B: Indoor temperature measurements). More detail occupant behavior information for each case is present in the Appendix. 4.3.2 Scenario Model validation Scenario for simplified H1 TRNSYS model validation was matched with scenario from previous research [13, p. 31]. More details in Appendix A‐1: Model validation TRNSYS model properties. 4.3.3 Scenario Existing Case Space heating will be done by an UFH system, no cooling (Fig. 1, Fig. 7, Fig. 9). This scenario will allow assessing the effects of cooling on comfort levels as Investigated Case is going to incorporate air conditioning. More details in Appendix A‐2: Existing Case TRNSYS model properties. 4.3.4 Scenario Investigated Case Central ASHP will be used to satisfy heating and cooling demands. Hot or chilled water will be distributed among H1 dwellings via flexible insulated pipes (Fig. 2, Fig. 12). AHU will be used for space conditioning. 20” shipping container will accommodate the ASHP as well as a central DC to AC inverter for electricity generated by PV panels located on H1 roofs (Fig. 14). More details in Appendix A‐3: Investigated Case TRNSYS model properties.

4.4

Existing Case

Simulations were done for a complete year: start time – 0 hour; stop time – 8760 hour; time step – 1 hour. Scenario Existing Case was used for calculating overheating, underheating and heating demands. Peak loads are going to be used for selecting appropriate power ASHP and AHU for Investigated Case. In total 3 simulations were done: 1 unit with Scenario No. 1; 1 unit with Scenario No. 2; 1 unit with Scenario No. 3. An average was calculated from 3 results. Peak Cooling Demand was found by setting the available cooling power to 280 kW. H1 in the Existing Case does not have cooling capability, but the Peak Cooling Demand is going to be used to select correct capacity ASHP and AHU combination for Investigated Case. Peak Heating Demand was found by setting the available heating power to 280 kW. The Peak Heating Demand is going to be used to select correct capacity ASHP and AHU combination for Investigated Case. Building envelope properties used are present in Appendix A: Properties and boundary conditions.

Scenariio Overhea ating Undeerheating Peak P No. Occupie ed; Occu pied; [h] Cooling C [h] Load; L [kW] [ 1 0 0 1.08 1 2

7.27 7

225

2.15 2

79.333

3.5 3

Averagee 0.0

eating He De emand; [kW Wh/yr], [kW Wh/m2yr] 5398 (11 19.96) 7311 (16 62.47) 11569 57.09) (25 80 092 (17 79.84)

Peak Heating Load; [kW] 2.94 13.07 20.5 12.17

Fig. 6. Existing Case e results for de mand, load, ovverheating and underheating hhours

Overheaating hours are hours, when the inndoor temp perature rise es above 28°°C and the space is occupied d; Undereatting hours – indoor tem mperature drrops below 18°C 1 and th e space is occupied. o Overheaating hours w were also ob bserved in H 1 measurem ment data – A Appendix B: Indoor temperature measureements. Oveerheating in measuremeent data can be explained by the fact that measured m summerr temperatures were hiigher than tthe temperaatures prese ent in the sstandardized TRNSYS weatherr file. As a result Overhea ating hours i n simulation ns might not occur as stanndardized simulation weatherr file summ mer tempera atures are loower than the ones observed in the measurements. Appearaance of Und derheating hours (Fig. 6) indicates that at in ncreased veentilation flo ow rates (Mechan nical ventilattion without heat recoveery) the 2.5 kkW UFH system is not caapable of cop ping with the heatting demand d.

Fig. 7. Existin ng Case space cconditioning energy consumption flowchart

Relative humidity iss omitted as only sensibble heating and a cooling is used in thhis model an nd latent d. TRSNYS m model overvieew is presentt in Fig. 31. effects aare neglected

Scenario Amount oof Single Unit To otal Heating No. Heating H1 Units De emand; Demand d; [kW Wh/yr] [kWh/yrr] 1 3 5398 2 3 7311 84403 3 4 11569 Fig. 8. Existing C F Case H1 cluster heating deman nd

4.4.1 Electric UFH H und floor of H1 is 29.64 m m2. It is not sspecified wh hat size area has UFH. Thhe maximum capacity The grou in the occupied zones is 100 W/m² [17]. Paart of the gro ound floor iss covered in furniture an nd utility 2 T estimate ed heating ppower of H1 UFH is cabinetss, hence it is estimated that 25 m has UFH. The 20*100== 2.5 kW (9000 kJ/h).

Fig. 9. UFH will proovide space heating in Existing Case

4.5

In nvestigated d Case

TRNSYS Type759 mo odel was used to create aa H1 node off Investigated Case DHC ssystem. The H1 node comprises of a singgle Type759 9 zone and AHU compo onents for zone z conditiioning with external controlss (Fig. 35). Ass with Existin ng Case the ssame occupant scenarios have been uused (4.3 Sce enarios). Large heeat pumps geenerally do n not have a laarge share of the DH sup pply. Howeveer, there is rreason to believe tthat they wiill be more ccommon in tthe future. N Not least in ccombination with large sshares of intermitttent power generation,, heat pumpps supplying DH can contribute to the develop pment of smart grrids. [19, p. 2 230]

Scenario o Overheating Unde rheating Cooling C Occupied No. d; Occuppied; [h] Demand; D [h] [kkWh/yr], [kkWh/m2yr] 1 0 0 16 (0.36) 1 2 0 0 56.9 (1.26) 5 3 0 0 262 (5.82) 2 Averagee 0.0 0.0 111.6 (2.48) 1

Heatingg Demandd; [kWh/yyr], [kWh/m m2yr] 3447 (776.6) 4782 (1106.27) 7521 (1167.13) 5250 (1116.6)

Fig. 10. Investigated Case results ffor demand, ovverheating and underheating hhours

Heat pu ump‐based cooling is also becomingg more popu ular globally, due to its iinherent effiiciencies. District sscale heat pu ump systems that use ceentral station n heat pump p(s) have beeen able to ge enerate a useful h heating effecct and a useful cooling eeffect simulttaneously with the samee heat pump p unit(s). Such sysstems offer tthe potentiall for very favvorable overaall system CO OP [19, p. 1884]. Scen nario Amou unt of Siingle Unnit Total Cooling C No. H1 Units Co ooling Demand; [kWh/yyr] Demand; [k kWh/yr] 1 3 16 6 2 3 56 6.9 126 66.7 3 4 26 62

ngle Unitt Total Heating Sin Heating Demand;; Demand; [kWh/yr]] [kW Wh/yr] 344 47 4782 54771 7521

Fig. 11. Invvestigated Casee H1 cluster heaating and coolin ng demand

Averagee Peak Heatin ng Load for H H1 is 12.17 kkW, Average e Peak Coolin ng Load – 3.55 kW. Heatin ng supply temperaature 70°C and a return temperature t e not lower than 35°C [14] [13, p. 2234]. Coolin ng supply temperaature 7°C and d return tem mperature 122°C [15, p. 11 1.4].

F Fig. 12. Investig gated Case spacce conditioning energy consum mption flowcha rt

Averagee Peak Heatin ng Load for H H1 is 12.17 kkW, Average e Peak Coolin ng Load – 3.55 kW. Heatin ng supply temperaature 70°C and a return temperature t e not lower than 35°C [20] [19, p. 2234]. Coolin ng supply temperaature 7°C and d return tem mperature 122°C [21, p. 11 1.4].

ASHP is to have Heating Power of 75 kW (Fig. 50) and Cooling Power of 15 kW (Fig. 49). Besides Load Duration Curves, H1 cluster with different occupant behaviors was generated and peak heating and cooling loads added together (Fig. 13). The peak cooling loads matched, but heating load was 30 kW greater on the Fig. 13 approach. Scenario Amount of Peak Single Unit Total Cooling Peak Single Total Unit Heating Heating No. H1 Units Cooling Load; Load; [kW] Load; [kW] Load; [kW] [kW] 1 3 1.08 2.94 2 3 7.27 13.07 33.65 130.03 3 4 2.15 20.5 Fig. 13. H1 cluster with different occupant behavior peak heating and cooling loads

There are a couple of ways how load requirements of a DHC system can be estimated. For this research Load Duration Curve and Coincidence Factor were consider as tools for selecting the ASHP capacity: 



Load Duration Curve – illustrates the variation of a certain load in a downward form such that the greatest load is plotted in the left and the smallest one in the right. On the time axis, the time duration for which each certain load continues during the day is given [22]. Load Duration Curve indicates the frequency of when a particular power is used by the DHC plant to satisfy demand. Coincidence Factor – is a measure of the probability that a particular piece of equipment will turn on coincidentally to another piece of equipment. For aggregate systems it is defined as the ratio of the sum of the individual non‐coincident maximum loads of various subdivisions of the system to the maximum demand of the complete system [23]. Coincidence factor is used for determining required heating power in large District Heating networks, where loads vary greatly.

Load Duration Curves were chosen as the tool for ASHP capacity selection. 4.5.1 Modeling district systems To compare the effectiveness of different space conditioning systems the initial TRNSYS Existing Case model was setup with Type660 simplified conditioned zone [24, p. 43]. Type660 comes with internal heating and cooling controls (Fig. 31) and as a result the loads reflect on electric UFH accurately as >95% of electricity is converted into heat. This model was adjusted for Investigated Case and Type660 was replaced with Type759 conditioned zone [24, p. 44]. To control AHU external heating and cooling controls were created, which modulate heat recovery unit and hot or chilled water flow through the AHU water‐to‐air heat exchanger (Fig. 35). For ASHP electrical energy consumption seasonal COP and ERR values were used (Fig. 41).This allowed comparing the heating and cooling demands of the two different systems, while using the same core model data (Appendix A: Properties and boundary conditions).

4.5.2

Central ASH HP

Fig. 14. 2 20” shipping container will houuse the central ASHP and inverter in Investigaated Case

otential of Looad Duration n Curves was not investiggated. After ggraphical Due to ttime constrain the full po investigaation 75% peeak heating load (75 kW W) was selectted as the heating capaccity of the ASHP (Fig. 50) and 50% peak cooling c load d (15 kW) ass the coolingg capacity of o the centraal ASHP (Fig. 49). To DHP‐AQ) witth 15 kW hea ating capacitty and 10 kW W cooling satisfy the demandss 5 ASHP unitts (Danfoss D capacityy is going to o be used. Additional A innformation iss present in Appendix CC‐1: Air Source Heat Pump. 4.5.3 Buffer tank 2000 liter buffer tank was chossen for hot water and 500 5 liter bufffer tank waas chosen fo or chilled water. C Calculations aand addition nal informatioon is presentt in Appendix C‐2: Bufferr tank sizing. 4.5.4 Insulated Pipe ulated flexible RAUTHERM MEX pipes arre going to b be used for h hot and chilleed water distribution Pre‐insu in H1 clu usters [25]. 4.5.5 AHU otential of Looad Duration n Curves was not investiggated. After ggraphical Due to ttime constrain the full po investigaation 75% peak heating load (6.7 kW W) was seleccted as the h heating capaacity of the A AHU (Fig. 52) and 50% peak co ooling load (1 1.6 kW) as thhe cooling caapacity of the central ASH HP (Fig. 51). AHU has 3 apable of 191 1.39–318.98 m /h ventil ation rates ( (Fig. 34). DU PLEX 370 EC CV4 with wat ter‐to‐air to be ca heat excchanger meets the requ uirements. TThe heating//cooling output of the A AHU is contrrolled by modulatting fluid flo ow rate going through thhe heat exchanger. Additional inforrmation is prresent in Appendix C‐3: Air Haandling Unit..

Fig. 15. A AHU will providee space conditio oning and PV w will cover some of the central A ASHP electricityy demand in Invvestigated Case

4.5.6 PV roof outp put d on H1 rooff slope, whicch has an are ea of 19.45 m m2 and is faccing the Soutth. Single PV paneels are placed H1 PV syystem is estiimated to an nnually geneerate 2340 kWh of electrricity. Combiined capacity of a 10 unit clusster is 23400 0 kWh. Addittional inform mation is pre esent in Appendix C‐4: PPV annual production estimatee. 4.5.7 Inverter Total ou utput of PV ssystem installed on H1 c luster roof iss 24 kW. Two 12 kW invverters were selected for DC to o AC converssion [26].

5. Results and d discussion The resu ults of this reeport show that: 1. Heat recovery syystem and co ooling in a foorm of AHU (Investigated d Case) increeases indoorr comfort door tempe ratures and removes underheating u g (Fig. 16)) in a H1 leveels (Lowers summer ind lighttweight struccture residen nce); 2. In electricity con nsumption terms t Invest igated Case (21287.6 kW Wh) with cenntral ASHP require r 4 timees less energgy than Existing Case (844403 kWh) with UFH (Fig. 56, Fig. 58); 3. Inveestments into o more capittal intense Innvestigated C Case pays offf in 9 years (FFig. 17).

5.1

Th he challengges of the re esearch

The main challenge was lack of annual meassurement daata of the H1 1 residence ffor simplified d TRNSYS model vvalidation. Like L with alll niche softtware a considerable amount a of time was spent s on troublesshooting and d getting used to the use r interface and the capab bilities of thee tool.

5.2

Comfort levels

The most demanding occupancy scenario is Scenario No. 3. As a result indoor comfort effects of different cases are going to be visible on this scenario the most (Fig. 16). From the graph it can be seen that Investigated Case indoor temperatures are in a narrower band than Existing Case and with lower summer temperatures – better indoor comfort. As can be observed by overheating and underheating hours in Fig. 6 and Fig. 10, the Investigated Case has none, while Existing Case had occurrences of underheating hours due to lack of mechanical ventilation with heat recovery. The effectiveness of heat recovery can also be observed in lower annual heating demand for Investigated Case. Additional information is present in Appendix D‐3: Comfort levels.

Indoor Temperatures OCS‐3 Temperature, [°C]

35 30 25 20 15 10 5 0

1000

2000

3000

4000

5000

6000

7000

8000

Time, [h] Existing Case OCS‐3

Investigated Case OCS‐3

Fig. 16. Scenario No. 3 Existing Case and Investigated Case indoor temperatures

For the summer months (from 3600 h to 5760 h) the average indoor temperature was 0.3°C lower in Investigated Case (Fig. 16). During peaks indoor temperature was up to 3°C lower in Investigated Case (Fig. 16).

5.3

Life‐cycle costs

The initial capital required for Investigated Case is 90000 € higher than Existing Case. The running costs difference is 10000 €/yr. 9 years are required for Investigated Case to pay itself off (Fig. 17). Additional information is present in Appendix D‐4: Life‐cycle cost calculations

Investigated Case Payback Time 120,000 € 90,804.15 €

100,000 €

92,600 €

Costs, [€]

80,000 € 60,000 € 40,000 € 20,000 € 0 € 1

Years Investment Difference

Running cost Difference

Fig. 17. Investigated Case payback time (9 years)

6. Conclusion Climate change trends indicate that in the future West Europe will receive more summertime heat waves [27]. As a result investments into comfortable indoor environment during summer will become more important for developers and consumers alike. Investigated Case annual electricity demands for space conditioning were met by the PV system (Fig. 58). It has to be taken into consideration that economies of scale can play a major factor in Initial Investment requirements – the costs calculated in this report were off‐the‐shelf, while at greater volumes the cost of ASHP and PV panels can drop significantly. Climate change prospects, PV electricity coverage of heating and cooling demand and H1 lifespan (30 years) indicates that increased indoor comfort outweighs the 9 year payoff period and Investigated Case is more futureproof design, when compared with Existing Case.

7. Further Research With detail TRNSYS Type56 model the MRT of zones can be computed. MRT is required for more informative PMV and PPD indoor comfort indicators. With more precise indoor comfort indicators the significance of H1 Air Conditioning during the summer can be better judged. In the Investigated Case it’s not clear how much of the space conditioning electrical energy demand is directly met by the PV system and how much of the energy has to be exported and later re‐ exported (imported) from the grid. This information would be useful for off‐grid version of H1. Furthermore smart electrical grid feature of running the central ASHP with surplus electrical grid energy can further decrease the running costs of the Investigated Case system.

8. Acknowledgment I would like to like to thank the following people for their guidance and support: PDEng A. Papadopoulos – for helping to define the initial Research Proposal for Combined Graduation in Building Physics and Services Building Performance Chair; prof.dr.ir. J.L.M. Hensen – for accepting my Combined Graduation in Building Physics and Services Research Proposal at Building Performance Chair; dr. M.H. Hassan Mohamed – for constructive feedback and guidance; PhD Stud. L. Xu – for constructive feedback and guidance; PhD Stud R. Kotireddy – for helping to troubleshoot TRNSYS BPS issues; PhD Stud. V. Zavrel – for helping to troubleshoot TRNSYS BPS issues.

9. References [1] “EUR‐Lex ‐ 32012L0027 ‐ EN ‐ EUR‐Lex.” [Online]. Available: http://eur‐lex.europa.eu/legal‐ content/EN/TXT/?uri=uriserv:OJ.L_.2012.315.01.0001.01.ENG. [Accessed: 04‐Oct‐2015]. [2] D. Kolokotsa, D. Rovas, E. Kosmatopoulos, and K. Kalaitzakis, “A roadmap towards intelligent net zero‐ and positive‐energy buildings,” Sol. Energy, vol. 85, no. 12, pp. 3067–3084, Dec. 2011. [3] “A Common Definition for Zero Energy Buildings | Department of Energy.” [Online]. Available: http://energy.gov/eere/buildings/downloads/common‐definition‐zero‐energy‐buildings. [Accessed: 05‐Jan‐2016]. [4] B. R. Establishment, Energy, heating and thermal comfort: practical studies from the building research establishment. Construction Press, 1978. [5] R. Slavid, Micro: Very Small Buildings. Laurence King Publishers, 2007. [6] “What Is The Tiny House Movement?,” The Tiny Life. [Online]. Available: http://thetinylife.com/what‐is‐the‐tiny‐house‐movement/. [Accessed: 08‐Sep‐2014]. [7] design & interactie Fabrique [merken, “Heijmans ONE,” Heijmans N.V. [Online]. Available: http://heijmans.nl/en/heijmans‐one/. [Accessed: 14‐Apr‐2016]. [8] “Bouwexpo Tiny Housing :: Tiny Housing.” [Online]. Available: http://www.bouwexpo‐ tinyhousing.nl/. [Accessed: 15‐Apr‐2016]. [9] A.‐F. service W. design & Development, “30 Heijmans ONE huizen voor Wonen Limburg,” Heijmans ONE. [Online]. Available: http://www.heijmans‐one.nl/nl/nieuws‐en‐ media/2015/10/30‐heijmans‐one‐huizen‐voor‐wo/62. [Accessed: 15‐Apr‐2016]. [10] J. Guan, N. Nord, and S. Q. Chen, “A Case Study of Campus Building End Use of a University in Norway,” Adv. Mater. Res., vol. 1073–1076, pp. 1259–1262, Dec. 2014. [11] “Shipping and Storage Container Dimension Charts :: CONTAINERS DIRECT.” [Online]. Available: http://www.shippingcontainersuk.com/info/shipping_and_storage_container_dimension_chart s.php. [Accessed: 03‐Oct‐2015]. [12] O. Almeida, “Semi‐collective vs. individual heat and hot water production a comparison,” Eindhoven University of Technology, Jan. 2015. [13] B. Giskes, “Optimizing the energy performace of the Heijamns ONE residence,” Eindhoven University of Technology, Dec. 2015. [14] M. Hamdy and J. L. Hensen, “Ranking of dwelling types in terms of overheating risk and sensitivity to climate change,” Build. Simul. 2015 14th Int. Conf. IBPSA Hyderabad India, pp. 8– 16, Dec. 2015. [15] B. Dong, Z. Li, and G. Mcfadden, “An investigation on energy‐related occupancy behavior for low‐income residential buildings,” Sci. Technol. Built Environ., vol. 21, no. 6, pp. 892–901, Aug. 2015. [16] R. R. Kotireddy, P. Hoes, and J. L. M. Hensen, “Optimal balance between energy demand and onsite energy generation for robust net zero energy buildings considering future scenarios,” Build. Simul. 2015 14th Int. Conf. IBPSA Hyderabad India, pp. 1970–1977, Dec. 2015. [17] B. W. Olesen and others, “Radiant floor heating in theory and practice,” ASHRAE J., vol. 44, no. 7, pp. 19–26, 2002. [18] “Energy‐savings of Far Infrared,” Herschel Infrared Ltd. [Online]. Available: http://www.herschel‐ infrared.com/heater‐fundamentals/energy‐savings/. [Accessed: 13‐Jan‐2016]. [19] R. Wiltshire, Advanced District Heating and Cooling (DHC) Systems. Woodhead Publishing, 2015. [20] T. Ommen, W. B. Markussen, and B. Elmegaard, “Lowering district heating temperatures – Impact to system performance in current and future Danish energy scenarios,” Energy, vol. 94, pp. 273–291, Jan. 2016. [21] 2008 ASHRAE Handbook ‐‐ HVAC Systems and Equipment (I‐P): I‐P Edition (includes CD in Dual Units). American Society of Heating, Refrigerating & Air‐Conditioning Engineers, Incorporated, 2008. [22] A. Poulin, M. Dostie, M. Fournier, and S. Sansregret, “Load duration curve: A tool for technico‐ economic analysis of energy solutions,” Energy Build., vol. 40, no. 1, pp. 29–35, 2008.

[23] “Estimation of actual maximum kVA demand ‐ Electrical Installation Guide.” [Online]. Available: http://www.electrical‐installation.org/enwiki/Estimation_of_actual_maximum_kVA_demand. [Accessed: 14‐Apr‐2016]. [24] J. Thornton, D. Bradley, and T. McDowell, “TESS Component Libraries for TRNSYS 16,” Therm. Energy Syst. Spec. LLC Madison, 2005. [25] “REHAU RAUVITHERM flexible pre‐insulated pipe solutions for district heating.” [Online]. Available: https://www.rehau.com/gb‐en/building‐technology/renewable‐energy/biomass‐ biogas/rauvitherm‐pre‐insulated‐pipe#tab3. [Accessed: 08‐Apr‐2016]. [26] “SolarEdge SE11400A‐US‐U Inverter ‐ Wholesale Solar,” WholesaleSolar.com. [Online]. Available: http://www.wholesalesolar.com/9900118/solaredge/inverters/solaredge‐se11400a‐us‐u‐ inverter. [Accessed: 09‐Apr‐2016]. [27] “The effects of Climate Change in the Netherlands: 2012 ‐ PBL Netherlands Environmental Assessment Agency.” [Online]. Available: http://www.pbl.nl/en/publications/the‐effects‐of‐ climate‐change‐in‐the‐netherlands‐2012. [Accessed: 12‐Apr‐2016]. [28] A. S. of H. Engineers Refrigerating and Air‐Conditioning and I. E. S. of N. America, Energy standard for buildings except low‐rise residential buildings. ASHRAE, 2000. [29] “SHADING COEFFICIENT CALCULATION SALHIA TOWER, BAHRAIN.” . [30] T. Hoyt, K. H. Lee, H. Zhang, E. Arens, and T. Webster, “Energy savings from extended air temperature setpoints and reductions in room air mixing,” Int. Conf. Environ. Ergon. 2009, Aug. 2005. [31] L. Cargill, “Buffer tank sizing guide,” The Green Home, 24‐Oct‐2013. . [32] “2000 liter Buffervat ‐ zonder spiraalbuis,” CVkoopjes.nl. [Online]. Available: https://www.cvkoopjes.nl/buffervaten/2000liter‐buffervat‐zonder‐spiraalbuis.html. [Accessed: 09‐Apr‐2016]. [33] “500 liter Buffervat ‐ zonder spiraalbuis,” CVkoopjes.nl. [Online]. Available: https://www.cvkoopjes.nl/buffervaten/500liter‐buffervat‐zonder‐spiraalbuis.html. [Accessed: 09‐Apr‐2016]. [34] “Underfloor Heating Solutions.” [Online]. Available: http://infraredtechnologies.co.uk/underfloor‐heating‐solutions. [Accessed: 22‐Nov‐2015]. [35] “Rekuperatorius Toshiba VN‐M350HE, 350 m3/h | Vilpra: Warmth for your home.” [Online]. Available: http://www.vilpra.lt/en/air‐conditioning‐and‐ventilation‐equipment/ventilation‐ equipment/w1tos‐vn‐m350he. [Accessed: 08‐Apr‐2016]. [36] N. L. Truong and L. Gustavsson, “Minimum‐cost district heat production systems of different sizes under different environmental and social cost scenarios,” Appl. Energy, vol. 136, pp. 881– 893, Dec. 2014. [37] M. Hamdy, A. Hasan, and K. Siren, “A multi‐stage optimization method for cost‐optimal and nearly‐zero‐energy building solutions in line with the EPBD‐recast 2010,” Energy Build., vol. 56, pp. 189–203, Jan. 2013. [38] “Rauvitherm Pre Insulated Pipe And Fittings Supplies | MyTub Ltd | page 1.” [Online]. Available: https://www.mytub.co.uk/rauvitherm‐pre‐insulated‐pipe‐and‐fittings‐products‐1. [Accessed: 08‐Apr‐2016]. [39] “≥ Vind 20 ft container op Marktplaats.nl.” [Online]. Available: http://www.marktplaats.nl/z.html?query=20+ft+container&postcode=5642. [Accessed: 08‐Apr‐ 2016]. [40] “LG LG‐300N1K‐G4 Black Mono Solar Panel ‐ Wholesale Solar.” [Online]. Available: http://www.wholesalesolar.com/1524617/lg/solar‐panels/lg‐lg‐300n1k‐g4‐black‐mono‐solar‐ panel. [Accessed: 08‐Apr‐2016].

Appendix A: Pro operties an nd boundarry conditio ons Buildingg properties u used to defin ne zones andd other parameters requ uired for sim ulations is present in this appendix. H1 haas glazed surfaces at the front and baack, hence sh hading effectts on the sides of the ble. buildingg are negligib Property Area Volum me (VH1) Width h Length Height

Va alue 45 164.85 3.5 9.26 5.97

Units U m2 m3 m m m

Fig. 18. General H1 building envvelope properties

SHGC (SSolar Heat Gaain Coefficie ent (Fig. 20)) for window ws in NL – 0.4 4 [28, p. 30]. Overhang Projection Factor fo or H1 was caalculated to be PF=P/H==0.13 (P=650 0 mm; H=500 00 mm) hencce SHGC multiplier is 0.91 (Figg. 19), which results in SH HGC of 0.4*00.91=0.36.

Fig. 19. Overhang Projeection Factor and SHGC Multip plier [29]

Orientation Area; [m2] West 2.04 East 8.23

U‐Value; [W/m2K] 1.4 1.4

SHGC

Transmittance

0.36 0.36

0.622 0.622

Fig. 20. H1 window properties

The thermal capacitance (Fig. 21) of the zone includes capacitances of building materials, furnishings and conditioned air (Fig. 22): Ctotal = Cmat+Cfurn+Cair=10675.54+306+213.71=11195.25 kJ/K Cmat=dfloor*Afloor*cfloor*ρfloor+dcross*Across*ccross*ρcross=1035.3+9640.24=10675.54 kJ/K Cfurn= Vwood*ρwood*cwood=0.5*1.7*360=306 kJ/K Cair=VH1*ρair*cair=164.84*1.29*1.005=213.71 kJ/K Type660 H1 heat loss coefficient (U‐value*surface area) (Fig. 21) = 0.294*149.57 + 0.337*29.75 = 54 W/m2K [13] Property Type660 or Type759 H1 zone capacitance Type660 or Type759 H1 zone heat loss coefficient U‐value ground floor Ground floor surface area U‐value external wall/roof External wall/roof surface area

Value 11195.25 54 (164.4) 0.337 29.75 0.294 149.57

Units

kJ/K W/m2K (kJ/hK) W/m2K m2 W/m2K m2

Fig. 21. General TRNSYS model properties

Material

Air Floor finish Crosslam panel Wood

Volume (V); [m3] 164.84 n/a n/a 0.5

Area (A); [m2] n/a 29.75 179.32 n/a

Thickness (d); [m] n/a 0.04 0.07 n/a

Specific heat (c); [kJ/kgK] 1.005 1.45 1.6 1.7

Density (ρ); [kg/m3] 1.29 600 480 360

Fig. 22. Material properties for thermal capacitance calculations [13, p. 30]

Fig. 23. H1 grounnd and first floo orplans [13, p. 2 28]

Fig. 24.. H1 facades [13 3, p. 29]

Fig. 25. H1 sections [13, p. 29]

Appendix A‐1: Model validation TRNSYS model properties Simulation properties used for simplified H1 model validation. ACH to kg/h conversion: H1 volume – 164.85 m3; Air density – 1.29 kg/m3. W to kJ/h conversion: 1 W=3.6 kJ/h. Heating set‐point, ventilation rate, infiltration, schedule were selected to match the previous research H1 model validation simulation properties [13, p. 14]. Scenario Heating; Cooling; Gains; Schedule – Schedule – No. [°C] [°C] [W], [kJ/h] Weekday Weekend 07:00‐08:00 1 21 n/a 500 (1800) 08:00‐00:00 17:00‐00:00 Fig. 26. Model validation H1 model heating and cooling properties

Scenario Infiltration; Ventilation; Schedule No. ACH (kg/h) ACH (kg/h) 1 0.25 (53.16) 0.28 (60) fixed Fig. 27. Model validation H1 model ventilation properties

Appendix A‐2: Existing Case TRNSYS model properties Simulation properties used for local UFH H1 model. ACH to kg/h conversion: H1 volume – 164.85 m3; Air density – 1.29 kg/m3. W to kJ/h conversion: 1 W=3.6 kJ/h. Heating set‐points were chosen to be 18°C, 21°C and 23°C [16, p. 4]. Ventilation rates were chosen to be 0.9 ACH, 1.2 ACH and 1.5 ACH [16, p. 4]. Gains were calculated to be 500 W [13, p. 31]. Scenario No. 1 2 3

Heating; [°C] 18 21 23

Cooling; [°C] n/a n/a n/a

Gains; [W], [kJ/h] 500 (1800) 500 (1800) 500 (1800)

Schedule – Weekday

Schedule – Weekend

07:00‐08:00 17:00‐24:00 17:00‐24:00 08:00‐24:00

12:00‐24:00 12:00‐24:00 12:00‐24:00

Fig. 28. Existing Case model heating and cooling properties

Heating; [°C] 18

Cooling; [°C] n/a

Gains; [W], [kJ/h] 0

Fig. 29. Existing Case H1 model outside of occupancy hour heating and cooling properties

Scenario No. 1 2 3

Infiltration; (kg/h) 0.20 (42.53) 0.20 (42.53) 0.20 (42.53)

ACH Ventilation; (kg/h) 0.9 (191.39) 1.2 (255.19) 1.5 (318.98)

ACH Schedule Fixed Fixed Fixed

Fig. 30. Existing Case H1 model ventilation properties

Fig. 31. Exxisting Case TRN NSYS model

Append dix A‐3: Invvestigated C Case TRNSYYS model prroperties Simulation propertiees used for ccentral ASHPP AHU H1 m model. ACH to kg/h convversion: H1 vvolume – 3 3 164.85 m ; Air denssity – 1.29 kg/m . W to kJ/h conversion: 1 W=3.6 kJ/h. Heaating set‐points were chosen tto be 18°C, 21°C and 23 3°C [16, p. 44]. Cooling se et‐points we ere chosen too be 28°C, 2 26°C and 24°C [144, p. 4], [30]. Ventilation n rates weree chosen to b be 0.9 ACH, 1.2 ACH andd 1.5 ACH [1 16, p. 4]. Gains were calculateed to be 500 W [13, p. 311]. Scenario No. 1 2 3

Heating;; [°C] 18 21 23

Cooling; [°C] 28 26 24

Gains; [W W], [kJ/h] 500 (1800)) 500 (1800)) 500 (1800))

Schedu ule – Weekda ay

Schhedule – We eekend

07:00‐0 08:00 17:00‐24:00 17:00‐2 24:00 08:00‐2 24:00

12::00‐24:00 12::00‐24:00 12::00‐24:00

Fig. 32. In nvestigated Casse model heatin ng and cooling p properties

He eating; [°C C] 18 8

Cooling; [°C] 28

Gains; [W W], [kJ/h] 0

Fig. 33. Investigated Case e H1 model outtside of occupancy hour heating and cooling properties

Sce enario No.. 1 2 3

Infilttration; (kg//h) 0.20 0 (42.53) 0.20 0 (42.53) 0.20 0 (42.53)

A ACH Ventillation; (kg/h)) 0.9 (19 91.39) 1.2 (25 55.19) 1.5 (318.98)

AC CH Schedulle Fixed Fixed Fixed

Fig. 34 4. Investigated CCase H1 model ventilation pro operties

Fig. 35. Invesstigated Case T TRNSYS model

Appendix B: Indoor tempe erature meeasuremen nts The indo oor temperaature measurrements werre done from m 27th of Ap pril 2015 unttil 15th of Se eptember 2015 [133, p. 39].

Fig. 36. Indoorr temperature A April [13, p. 39]

Fig. 37. Indoorr temperature May [13, p. 39]

Fig. 38. Indoorr temperature JJune [13, p. 40]

Fig. 39. Indooor temperature July [13, p. 40]

Fig. 40. Indoor t F temperature August [13, p. 41 1]

Figg. 41. Indoor te mperature Sep ptember [13, p. 41]

Appendix C: Building serviices equipm ment speccifications HVAC and other bu uilding servicces equipmeent specificaation data and calculatioons are goin ng to be present here.

Appendix C‐1: Air Source Heat Pump For electricity consumption calculations ASHP performance data for Dutch climatic conditions was taken from previous research [12, p. 20]. Month January February March April May June July August September October November December

COP 2.2 2.5 3.0 3.5 3.7 3.9 4.0 4.0 3.5 3.0 2.5 2.2

ERR 4.1 4.0 3.8 3.7 3.5 3.5 4.0 4.1 4.2

Fig. 42. Investigated Case ASHP performance data for Dutch climatic conditions.

Single Unit Scenario Amount of Single Unit Total electricity electricity No. H1 Units electricity demand for demand for demand for Heating; Cooling; Cooling; [kWh/yr] [kWh/yr] [kWh/yr] 1 3 4.6 1384.8 2 3 16.1 1876.1 352.1 3 4 72.5 2788.2

Total electricity demand for Heating; [kWh/yr] 20935.5

Fig. 43. Investigated Case H1 cluster electricity demand for heating and cooling

Appendix C‐2: Buffer tank sizing Buffer tanks volumes for hot water and chilled water were calculated. The following information was used to calculate the size (Vbuff) of the buffer tanks [31]: Symbol Vbuff t P PZ dT

Value n/a 10 255911 22900 10

Unit Gallon Minutes BTU/h BTU/h °F

Note Buffer tank volume; 3.78541; [l] ASHP on (cycling) time ASHP Power; 75 [kW] Zone Load; 6.7 [kW] Temperature difference; 5.56 [°C]

Fig. 44. Hot water buffer tank sizing parameters

Vbuff = t *(P – Pz) / (500*dT)=10*(255911‐22900)/(500*10) = 466 Gallons = 1764 l The required buffer tank volume (Vbuff) for hot water was calculated to be 1764 liters. 2000 liter buffer tank was chosen [32].

Symbol Vbuff t P PZ dT

Value n/a 10 51182 5460 10

Unit Gallon Minutes BTU/h BTU/h °F

Note Buffer tank volume; 3.78541; [l] ASHP on (cycling) time ASHP Power; 15 [kW] Zone Load; 1.6 [kW] Temperature difference; 5.56 [°C]

Fig. 45. Chilled water buffer tank sizing parameters

Vbuff = t *(P – Pz) / (500*dT)=10*(51182‐5460)/(500*10) = 91 Gallons = 350 l The required buffer tank volume (Vbuff) for chilled water was calculated to be 350 liters. 500 liter buffer tank was chosen [33].

Append dix C‐3: Air Handling U Unit

Append dix C‐4: PV annual pro oduction esttimate LG‐300N N1K‐G4 PV monocrystall m ine panel w was chosen for Scenario . Panel Stanndard Test Condition C 1 (STC ) peeak power rating (Pmax) is 300W. Dim mensions (L xx W x H) – 16 640 x 1000 xx 40 mm. H1 length is 8.5 m, sso 8 PV paneels fit on the e roof with combined peak p power of o 8*300=24400W. PV pa anels are placed o on H1 roof slope, which h has an area oof 19.45 m2 and is facing g the south. Location use ed comes from weeather data ffile – 52°18'0 0"N 4°46'2"EE. Single H1 PV system (8 panels) prroduces 2340 0 kWh of electricitty annually. PVGis w was used to caalculate annual PV panell production estimate for a single H1 : PVGIS eestimates of solar electricity generatiion Locationn: 52°18'0" Noorth, 4°45'54" East, Elevatioon: -4 m a.s.l.,,

Solar raddiation databasse used: PVGIIS-CMSAF Nominal power of the PV system: 2.4 2 kW (crystaalline silicon) Estimatedd losses due too temperaturee and low irraddiance: 7.7% (using ( local am mbient temperrature) Estimatedd loss due to angular a reflectance effects: 3.2% Other lossses (cables, innverter etc.): 14.0% 1 Combineed PV system losses: 23.1% % Fixed syystem: inclinaation=29°, orientattion=0° Month

Jan

2.33

72.3

1.17

36.2

Feb

3.76

105

1.91

53.5

Mar

6.87

213

3.58

111

STC (Staandard Test Condition): Irra adiance 1000 W/m², Module Temperature 25 °C, AM 1.5

Apr

9.64

289

5.22

156

May

9.88

306

5.48

170

Jun

9.97

299

5.59

168

Jul

9.53

296

5.38

167

Aug

8.62

267

4.80

149

Sep

6.96

209

3.81

114

Oct

4.71

146

2.49

77.3

Nov

2.47

74.0

1.27

38.0

Dec

1.88

58.2

0.94

29.2

Yearly averagee

6.40

195

3.48

106

Total foor year

2340

1270

Ed: Averaage daily electtricity producttion from the given system (kWh) Em: Averaage monthly electricity e prod duction from tthe given systtem (kWh) Hd: Averaage daily sum m of global irraadiation per sqquare meter reeceived by the modules of thhe given systeem

(kWh/m2) Hm: Averaage sum of global irradiatio on per square meter receiveed by the modu ules of the givven system (kW Wh/m2)

Appendix D: Ressults calcullations Charts, ttables and caalculations o of simulation results

Append dix D‐1: Heaating and co ooling demaands

Heatting Dem mand 1400

Demand, [kW] ,[ ]

1200 1000 800 600 400 200 0 1

100

nth Mon Investigated Case OC CS‐1

Invvestigated Case OCS‐2

Investigateed Case OCS‐3 3

Fig. 46. Investtigated Case he eating demand

Demand, [kW]

Heating Demand 1800 1600 1400 1200 1000 800 600 400 200 0 1

Month Existing Case OCS‐1

Existing Case OCS‐2

Existing Case OCS‐3

Fig. 47. Existing Case heating demand

Demand, [kW]

Cooling Demand 90 80 70 60 50 40 30 20 10 0 1

Month Investigated Case OCC‐1

Investigated Case OCC‐2

Investigated Case OCC‐3

Fig. 48. Investigated Case cooling demand

Appendix D‐2: Load Duration Curves

Cooling Load Duration Curve 35 30.69

Load, [kW]

30 25 20 15 10 5 0 0

100

150

200

250

300

350

400

Time, [h] Cooling Load Average

75%

50%

Fig. 49. 10 unit H1 cluster cooling load duration curve

Heating Load Duration Curve 120 100.65

Load, [kW]

100 80 60 40 20 0 0

1000

2000

3000

4000

5000

6000

7000

8000

Time, [h] ASHP

75%

50%

Fig. 50. 10 unit H1 cluster heating load duration curve

Cooling Load Duration Curve 3.5

3.24

Load, [kW]

3 2.5 2 1.5 1 0.5 0 0

100

150

200

250

300

350

400

Time, [h] Average

75%

50%

Fig. 51. Existing Case single H1 cooling load duration curve

Heating Load Duration Curve 10 8.91

Load, [kW]

8 6 4 2 0 0

1000

2000

3000

4000

5000

6000

7000

8000

Time, [h] Average

75%

50%

Fig. 52. Existing Case single H1 heating load duration curve

Appendix D‐3: Comfort levels Comfort levels were analyzed by looking at indoor temperatures. The differences between temperature peaks and lows of Existing Case and Investigated Case indicate space conditioning effectiveness of each case.

Indoor Temperatures OCS‐1 Temperature, [°C]

35 30 25 20 15 10 5 0

1000

2000

3000

4000

5000

6000

7000

8000

Time, [h] Existing Case OCS‐1

Investigated Case OCS‐1

Fig. 53. Scenario No. 1 Existing Case and Investigated Case indoor temperatures

Indoor Temperatures OCS‐2 Temperature, [°C]

35 30 25 20 15 10 5 0

1000

2000

3000

4000

5000

6000

7000

8000

Time, [h] Existing Case OCS‐2

Investigated Case OCS‐2

Fig. 54. Scenario No. 2 Existing Case and Investigated Case indoor temperatures

Appendix D‐4: Life‐cycle cost calculations Life‐cycle costs calculations were done for initial investment and running costs of Existing Case and Investigated Case space conditioning systems. Equipment

Quantity

Price/Unit

Total

UFH

2000 €

20000 €

Total

Notes Electric Underfloor Heating; [34]

20000 € Fig. 55. Existing Case equipment (Initial investment) costs

Services

Quantity

Price/Unit

Total

Electricity

84403 kWh

0.12 €/kWh

10128.36 €

Total

Notes Annual electricity consumption of Underfloor Heating system. It’s considered that the efficiency of UFH is 100%; Fig. 8, [12, p. 32]

10128.36 € Fig. 56. Existing Case annual services costs

Equipment

Quantity

Price/Unit

Total

AHU

1500 €

15000 €

Heat Exchanger

650 €

13000 €

ASHP

75 kW

520 €/kW

39000 €

Piping Buffer Tank Buffer Tank 20” TEU Inverter PV panels Total

200 m 1 1 1 2 80

40 €/m 1800 € 800 € 1000 € 1800 € 380 €

8000 € 1800 € 800 € 1000 € 3600 € 30400 € 112600 €

Notes Air Handling Unit also known as Recuperator; [35] Water‐to‐Air Heat Exchanger; [12, p. 32] Air Source Heat Pump – 5*DHP‐ AQ‐16; [36], [37] Rehau RAUVITHERM; [25], [38] 2000 l buffer hot tank; [32] 500 l buffer cold tank; [33] 20” Shipping Container; [39] 12 kW inverter; [26] 300 W PV panel; [40]

Fig. 57. Investigated Case equipment (Initial investment) costs Services

Quantity

Price/Unit

Total

Electricity

21287.6 kWh

0.12 €/kWh

2554.5 €

PV Electricity 23400 kWh

‐0.12 €/kWh

‐2808 €

DHC 75 kW Maintenance

3.9 €/kW

292.5 €

Total

Notes Annual electricity consumption of Central Air Source Heat Pump; Fig. , [12, p. 32] Annual H1 cluster PV electricity production (2340*10); Appendix C‐4: District Heating and Cooling Operations and Maintenance costs; [36], [37]

39.01 € Fig. 58. Investigated Case annual service costs