PhD Thesis - Energy Systems Research Unit - University of Strathclyde

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Component ESP-r Database: Annex 42 model for ICE CHP systemsName Assigned:CHPControl Variable:Variable (‘On’ / ‘Off’)Component Data:5500.0 #1 System maximum power (W)0.0000 #2 System minimum power (W)80.000 #3 Maximum cooling water outlet temp ( o C)2.0000 #4 Fuel type (1: liquid fuel, 2: gaseous mixture)0.46150x10 8 #5 Liquid fuel heating value (J/kg):#6 to #19 not used-1.0000 #20 Max rate <strong>of</strong> change in fuel flow (kg/s 2 )-1.0000 #21 Max rate <strong>of</strong> change in power output (W/s)53606. #22 Power system thermal mass (J/K)741.03 #23 Effective heat recovery UA coefficient (W/K)13.748 #24 Effective heat loss UA coefficient (W/K)1000.7 #25 Heat exchanger thermal mass (J/K)90.000 #26 Start-up period duration (s)1500.0 #27 Cool-down period duration (s)2.0000 #28 Cool-down period mode (1: Mandatory, 2: optional)-17.000 #29 Electric output at cool-down (W)-17.000 #30 Net electrical output in standby mode (W)5000.0 #31 Power output correlation minimum bound (W)5500.0 #32 Power output correlation maximum bound (W)60.000 #33 Cooling water temperature correlation minimum bound ( o C)70.000 #34 Cooling water temperature correlation maximum bound ( o C)0.0000 #35 Cooling water flow correlation minimum bound (kg/s)100.00 #36 Cooling water flow correlation maximum bound (kg/s)0.2700 #37 Performance map: Elec. efficiency correlation coeff. a00.0000 #38 Performance map: Elec. efficiency correlation coeff. a10.0000 #39 Performance map: Elec. efficiency correlation coeff. a20.0000 #40 Performance map: Elec. efficiency correlation coeff. a30.0000 #41 Performance map: Elec. efficiency correlation coeff. a40.0000 #42 Performance map: Elec. efficiency correlation coeff. a50.0000 #43 Performance map: Elec. efficiency correlation coeff. a60.0000 #44 Performance map: Elec. efficiency correlation coeff. a70.0000 #45 Performance map: Elec. efficiency correlation coeff. a80.0000 #46 Performance map: Elec. efficiency correlation coeff. a90.0000 #47 Performance map: Elec. efficiency correlation coeff. a100.0000 #48 Performance map: Elec. efficiency correlation coeff. a11270
0.0000 #49 Performance map: Elec. efficiency correlation coeff. a120.0000 #50 Performance map: Elec. efficiency correlation coeff. a130.0000 #51 Performance map: Elec. efficiency correlation coeff. a140.0000 #52 Performance map: Elec. efficiency correlation coeff. a150.0000 #53 Performance map: Elec. efficiency correlation coeff. a160.0000 #54 Performance map: Elec. efficiency correlation coeff. a170.0000 #55 Performance map: Elec. efficiency correlation coeff. a180.0000 #56 Performance map: Elec. efficiency correlation coeff. a190.0000 #57 Performance map: Elec. efficiency correlation coeff. a200.0000 #58 Performance map: Elec. efficiency correlation coeff. a210.0000 #59 Performance map: Elec. efficiency correlation coeff. a220.0000 #60 Performance map: Elec. efficiency correlation coeff. a230.0000 #61 Performance map: Elec. efficiency correlation coeff. a240.0000 #62 Performance map: Elec. efficiency correlation coeff. a250.0000 #63 Performance map: Elec. efficiency correlation coeff. a260.6500 #64 Performance map: Thermal efficiency correlation coeff. b00.0000 #65 Performance map: Thermal efficiency correlation coeff. b10.0000 #66 Performance map: Thermal efficiency correlation coeff. b20.0000 #67 Performance map: Thermal efficiency correlation coeff. b30.0000 #68 Performance map: Thermal efficiency correlation coeff. b40.0000 #69 Performance map: Thermal efficiency correlation coeff. b50.0000 #70 Performance map: Thermal efficiency correlation coeff. b60.0000 #71 Performance map: Thermal efficiency correlation coeff. b70.0000 #72 Performance map: Thermal efficiency correlation coeff. b80.0000 #73 Performance map: Thermal efficiency correlation coeff. b90.0000 #74 Performance map: Thermal efficiency correlation coeff. b100.0000 #75 Performance map: Thermal efficiency correlation coeff. b110.0000 #76 Performance map: Thermal efficiency correlation coeff. b120.0000 #77 Performance map: Thermal efficiency correlation coeff. b130.0000 #78 Performance map: Thermal efficiency correlation coeff. b140.0000 #79 Performance map: Thermal efficiency correlation coeff. b150.0000 #80 Performance map: Thermal efficiency correlation coeff. b160.0000 #81 Performance map: Thermal efficiency correlation coeff. b170.0000 #82 Performance map: Thermal efficiency correlation coeff. b180.0000 #83 Performance map: Thermal efficiency correlation coeff. b190.0000 #84 Performance map: Thermal efficiency correlation coeff. b200.0000 #85 Performance map: Thermal efficiency correlation coeff. b210.0000 #86 Performance map: Thermal efficiency correlation coeff. b220.0000 #87 Performance map: Thermal efficiency correlation coeff. b23271
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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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circuit were obtained and how the p
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the internal high and low pressures
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the imaginary boundary constituting
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the model was then compared to a se
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Fig. 3.9 - Modelled chilled water c
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standard deviation was calculated t
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The selected tank size is based on
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3.5 Summary of Chapter 3This chapte
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Conservation in Buildings and Commu
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uilding simulation." Doctoral Thesi
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CHAPTER 4SIMULATIONMETHODOLOGYANDAN
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Table 4.1 - Scenarios investigatedS
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The building fabric, building size
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analysis in the case of the interme
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micro-trigeneration system are thos
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profile for that same time period (
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4.2.1.3 Calculating the fuel consum
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cogeneration technologies [16].- (4
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the present 1.088 kgCO 2 per kWh de
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Table 4.4 - Explanation of the cash
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FIT and considering the cost involv
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impact they have on the energetic,
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Conservation in Buildings and Commu
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CHAPTER 5RESULTSANDDISCUSSIONThis c
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increases so does energy-efficiency
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the building fabric is most effecti
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Instances where the load duration c
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households, GF refers to the ground
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Finally, and this will be the discu
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In the table, the low and high effi
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The scenario with the lowest number
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Comparing Figure 5.3 and 5.4, respe
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number of cycles during the cooling
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5.3.3 Chiller’s performanceAs dis
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5.3.4 Electrical performance of the
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decrease in net imports and net dem
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Table 5.10 - Micro-trigeneration sy
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trigeneration system primary energy
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Table 5.11 - Micro-trigeneration pr
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The PES obtained for the different
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Difference inprimary energybetween
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Similar results are obtained for al
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2 High/High efficiency3 High/Curren
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Fig. 5.8 - Sensitivity of Emissions
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Similarly to the effect on PE SEPAR
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5.5 Micro-trigeneration system econ
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As electricity costs increase (rela
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ii.In the latter case the magnitude
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Resultingdifference incash flowDiff
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Multiple of current electricity tar
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of the system diminishes with incre
Page 244 and 245: Fig. 5.17 - CF and CF component ter
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Page 252 and 253: Similarly to the PW and the IRR, it
Page 254 and 255: Table 5.13 - Summary of micro-trige
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Page 260 and 261: micro-trigeneration system. Convers
Page 262 and 263: CHAPTER 6CONCLUSION:OUTCOMESANDFUTU
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Page 266 and 267: circuits associated with the absorp
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Page 270 and 271: plant models using the building sim
Page 272 and 273: APPENDIX AEquation factors for sele
Page 274 and 275: Factors for use in equation (2.1) f
Page 282 and 283: APPENDIX BElectrical demand profile
Page 284 and 285: Fig. B4 - Current efficiency electr
Page 286 and 287: Fig. B10 - High efficiency electric
Page 288 and 289: Fig. B16 - Current efficiency elect
Page 296 and 297: 0.0000 #88 Performance map: Thermal
Page 298 and 299: 99.999 #1 Evaporator Total Mass (Fl
Page 300 and 301: 100.00 #13 Boiling temperature of f
Page 302 and 303: One way to solve a partial differen
Page 304 and 305: The ‘self coupling term’ is def
Page 306 and 307: C ******************** CMP73C *****
Page 308 and 309: &COMMON/PCOND/CONVAR(MPCON,MCONVR),
Page 310 and 311: C and the inlet point into the solu
Page 312 and 313: IF(CSVF(INOD1,1).LE.BDATA(IPCOMP,17
Page 314 and 315: C Calculate T1 (DegC): T1 = ((T16-T
Page 316 and 317: XST=4010 DST=-(9.133128) + (0.94396
Page 318 and 319: & + 2500.559)*1000C Refrigerating p
Page 320 and 321: End IfC Calculate h3 (J/kg): h3=h2+
Page 322 and 323: &&ElseEnd IfActPow=0ActPow = PCONDR
Page 324 and 325: C Node 1, thermal mass effected by
Page 326 and 327: WRITE(ITU,*) ' Connection(s) ',ICON
Page 328 and 329: Fig. F.1 - IRR for scenarios with d
Page 330 and 331: 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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