PhD Thesis - Energy Systems Research Unit - University of Strathclyde

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the building fabric is most effective in the smaller building. An important modellingaspect during the cooling season is therefore to reduce solar gains through theaddition of shading devices. Both building models results shown include the additionof external louvers shading approximately 70% of the buildings’ glazed area.In terms of the individual fabric improvement measures, simulations showed that thetwo most successful measures were the roof and wall insulation, with the formerbeing the most important individual improvement given the high solar inclinationduring summer which renders roofs the main entry point of solar gains into buildings.Finally, an important aspect is the fact that in both buildings, improving the buildingwas most effective for the ground floor apartment. Explicitly this suggests that giventheir location (sheltered from direct sunlight by the floors above) lower located floorstend to benefit from improvements in the thermal envelope of the floors locatedabove them. In this case the thicker (and more insulated) walls and roof serve asbarrier to the flow of solar-based heat gains downwards towards the ground floor.5.2 Effect of improving the electrical efficiency of appliances on residentialelectrical demand profilesIn Chapter 2 a method was presented whereby low-resolution electrical demanddatasets can be used to create high-resolution demand profiles reflecting the effectsof appliance energy-efficiency improvements in future years. Based on theaggregation of these individual appliance based profiles, a number of representativeseasonal daily electrical demand profiles were modelled to represent the current andhigh efficiency electrical demand profiles of individual and aggregated householdswithin the two buildings modelled.A number of these profiles, specifically those for the characteristic days in February,May and August, for both the 3 household building used in Scenario 1 and the 6household building used in Scenarios 2, 3, 4 and 5 were shown in Chapter 2. Table5.3 summarises the key features of the demand profiles shown in Chapter 2,specifically those used to model the current and high electrical efficiency demand168
profiles of the different floors in the 3 household building in Scenario 1. The keycharacteristics used to describe the profiles are the daily electrical energyconsumption and the daily peak demand. Recalling from Chapter 2 whichhouseholds were used to model the individual floors within the 3 household building,GF refers to the ground floor of the 3 household building (modelled using household2A), MF refers to the middle floor (modelled using household 3B) and TF refers tothe top floor (modelled using household 4B).Table 5.3 - Key values obtained for the modelled demand profiles of the 3 householdbuilding - Scenario 1MonthValueAppliances’ electricalefficiencyGF(2A)Floor (Household)MF(3B)TF(4B)EntirebuildingFebruaryMayAugustDaily peak demand(W)Daily electrical energyconsumption(kWh)Daily peak demand(W)Daily electrical energyconsumption(kWh)Daily peak demand(W)Daily electrical energyconsumption(kWh)Current Efficiency 2,134 2,219 2,597 4,013High Efficiency 2,005 1,972 2,129 4,027Percentage Difference -6 -13 -22 0Current Efficiency 3.53 6.92 9.84 20.29High Efficiency 2.59 5.22 7.27 15.08Percentage Difference -36 -33 -35 -35Current Efficiency 2,048 2,276 2,590 6,331High Efficiency 2,055 2,655 2,240 6,026Percentage Difference 0 14 -16 -5Current Efficiency 3.28 6.19 8.78 18.25High Efficiency 2.58 4.7 6.62 13.90Percentage Difference -27 -32 -33 -31Current Efficiency 2,021 2,141 3,042 3,942High Efficiency 2,004 2,009 2,712 3,333Percentage Difference -1 -7 -12 -18Current Efficiency 2.29 4.45 6.95 13.69High Efficiency 1.68 3.36 5.07 10.11Percentage Difference -36 -32 -37 -35Additionally, Figure 5.1 shows the load duration curves for both the current and highelectrical efficiency profiles described in the Table 5.3 for the month of February.169
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University of StrathclydeDepartment
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ACKNOWLEDGEMENTSIt has been a long
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ABSTRACTThe domestic sector account
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TABLE OF CONTENTSACKNOWLEDGEMENTSAB
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2.4.3.3 Aggregated loads frequency
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4.1.1.1 Factoring for electricity g
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5.5.1.2 Present worth assuming a va
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LIST OF FIGURESFig. 1.1 Separate ge
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Fig. 2.38 Aggregated DHW draw profi
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Fig. 5.23 PP for Scenarios 1 and 2
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uilding 95Table 3.5 Control scheme
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ACRONYMS & ABBREVATIONSBMS Building
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CHAPTER 1INTRODUCTIONChapter overvi
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uildings, and Directive 2006/32/EC
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Micro-cogeneration can make use of
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offer reasonable and adequate retur
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system in a residential building, i
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conditions have on the performance
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effective manner. The research in t
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investigate how a residential micro
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Table 1.1 also includes additional
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1.7 Chapter References[1] DG for En
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[18] Pehnt, M., Cames, M., Fischer,
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[39] Cardona, E. and Piacentino, A.
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[56] Shin, Y., Seo, J.A., Cho, H.C,
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2.1 Modelling residential demand -
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minute resolution data. The researc
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2.3.1 Example: specifying the build
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Table 2.1 - Dimensions of the model
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In this second enlarged building, t
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Table 2.3 - Main characteristics of
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Table 2.4 - Main characteristics of
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esolution demand data for individua
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the original data (see equation 2.1
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the course of a year. The end resul
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specific appliance at a particular
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consumption during this period is e
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awarded on a random basis following
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Table 2.7 - Scaling factors for the
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NVF values for washing machines, el
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Table 2.10 - Annual electrical ener
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2.4.4 Creating the profiles used in
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Fig. 2.16 - Electrical demand profi
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Fig. 2.19 - High efficiency electri
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2.4.4.3 6 Household buildingAs ment
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2.29 and Figure 2.30 show the resul
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period [11].In order to improve on
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(as discussed by Richardson et al.
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similar fashion to the modelling of
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observed measurements from a number
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Fig. 2.37 - Total internal heat gai
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comparing water consumption related
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various aspects which make up the h
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natural Resources" - (LN 238 of 200
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in Domestic Appliances and Lighting
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[49] DEFRA (2010). "Dishwashers Gov
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CHAPTER 3MODELLINGMICRO-TRIGENERATI
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the same research also suggests tha
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cooling device in conjunction with
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simulation tool ESP-r by explaining
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Fig 3.2 - Basic micro-trigeneration
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3.2.3 Control strategies for the pl
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or decrease the flow rate of the co
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efrigerant, which in this case is w
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arbitrary cycle configuration, usin
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coefficients for the future and pre
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Applying basic energy and mass cons
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CH Power , the chiller’s refriger
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general form of equation (3.16) [33
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functions of hot water circuit inle
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Page 156 and 157: 3.5 Summary of Chapter 3This chapte
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Page 162 and 163: CHAPTER 4SIMULATIONMETHODOLOGYANDAN
Page 164 and 165: Table 4.1 - Scenarios investigatedS
Page 166 and 167: The building fabric, building size
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Page 174 and 175: 4.2.1.3 Calculating the fuel consum
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Page 178 and 179: the present 1.088 kgCO 2 per kWh de
Page 180 and 181: Table 4.4 - Explanation of the cash
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Page 188 and 189: CHAPTER 5RESULTSANDDISCUSSIONThis c
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Page 204 and 205: Comparing Figure 5.3 and 5.4, respe
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Page 214 and 215: Table 5.10 - Micro-trigeneration sy
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Page 218 and 219: Table 5.11 - Micro-trigeneration pr
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Page 228 and 229: Fig. 5.8 - Sensitivity of Emissions
Page 230 and 231: Similarly to the effect on PE SEPAR
Page 232 and 233: 5.5 Micro-trigeneration system econ
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of the system diminishes with incre
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Fig. 5.17 - CF and CF component ter
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shows that exporting all the cogene
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LPG price level when the micro-trig
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Minimum threshold viability of the
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Similarly to the PW and the IRR, it
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Table 5.13 - Summary of micro-trige
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performance of the micro-trigenerat
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directly affect the system’s ener
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micro-trigeneration system. Convers
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CHAPTER 6CONCLUSION:OUTCOMESANDFUTU
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and power demands of the building.
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circuits associated with the absorp
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the subject. More work therefore ne
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plant models using the building sim
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APPENDIX AEquation factors for sele
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Factors for use in equation (2.1) f
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APPENDIX BElectrical demand profile
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Fig. B4 - Current efficiency electr
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Fig. B10 - High efficiency electric
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Fig. B16 - Current efficiency elect
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Component ESP-r Database: Annex 42
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0.0000 #88 Performance map: Thermal
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99.999 #1 Evaporator Total Mass (Fl
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100.00 #13 Boiling temperature of f
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One way to solve a partial differen
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The ‘self coupling term’ is def
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C ******************** CMP73C *****
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&COMMON/PCOND/CONVAR(MPCON,MCONVR),
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C and the inlet point into the solu
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IF(CSVF(INOD1,1).LE.BDATA(IPCOMP,17
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C Calculate T1 (DegC): T1 = ((T16-T
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XST=4010 DST=-(9.133128) + (0.94396
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& + 2500.559)*1000C Refrigerating p
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End IfC Calculate h3 (J/kg): h3=h2+
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&&ElseEnd IfActPow=0ActPow = PCONDR
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C Node 1, thermal mass effected by
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WRITE(ITU,*) ' Connection(s) ',ICON
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Fig. F.1 - IRR for scenarios with d
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Fig. F.5 - PP for scenarios with di
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PhD Thesis - Energy Systems Research Unit - University of Strathclyde

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