PhD Thesis - Energy Systems Research Unit - University of Strathclyde

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analysis in the case of the intermediate season) were then extrapolated to obtainmonthly data for the individual scenarios. Aggregation of the monthly resultsproduced the seasonal and eventually the annual results which can then be used tocompare the different scenarios modelled.An important aspect in such a type of modelling is the choice of characteristic weeksused for the analysis. Ortiga et al. in [8], propose a graphical method based on subdivingthe annual cumulative energy demand into a number of representative timeperiods to aid in selecting characteristic days for optimisation processes involvingcogeneration and trigeneration models; they explain that as long as the selected daysare chosen using this method, the number of representative days chosen to representa whole year worth of data does not really influence the results obtained. Theresearch in this thesis was however different as its objective was not to optimise asystem but rather to use a combined deterministic/sensitivity analysis approach tocompare different demand-supply scenarios. In this thesis therefore rather than theabsolute results (which were still important), what was being sought was tounderstand the effect different operating conditions have on micro-trigeneration, bycomparing the results to a base case scenario. As long as the same time periods wereused to create the aggregated seasonal and annual performance metrics for thedifferent scenarios, the selection of typical days was therefore less of an issue. Twoseparate week-long simulations per month were considered to be a reasonablerepresentation of the diversity experienced over a month. In the case of theintermediate season the fact that the micro-trigeneration system supplied only hotwater demand (which shows little climatic influence) during all the months of thatseason, justifies the use of only one week-long simulation for each month.4.2 Analysis metricsThe type of analysis used to assess the different scenarios simulated relies on a‘traditional’ analysis of a project’s energetic, environmental and economicperformance. Using a combined deterministic approach and sensitivity analysismethodology as explained in Chapter 1, for each scenario listed in Table 4.1 anumber of performance metrics from each category were used to assess that scenario.144
In the deterministic approach, performance metrics for the micro-trigenerationsystem calculated for different scenarios were compared. Examples include thesystem’s fuel consumption, the electrical performance of the system and the system’sseasonal and annual overall efficiency. In the sensitivity analysis approach theperformance metrics calculated for each scenario were subjected to a process whereone parameter was varied over a reasonable range of possible values (an explanationas to how the range of values for each individual parameter was selected is given inthe respective sections later in this chapter) to find the sensitivity of the scenario tothat particular parameter. The Primary Energy Savings (PES), Emission Savings –CO 2 savings (ES) and metrics related to the financial aspects of the system areexamples of such an analysis.The data used to create these performance metrics was primarily derived from theraw ESP-r simulation time-series output, which was based on a 1-minute resolutionformat and included (amongst others) the air and water inlet and outlet temperaturesand flow rates of all plant components, fuel consumption (in the case of the CHPunit), the internal temperature in the building zones and other internal parameters ofthe building such as the solar energy absorbed by each individual zone etc. Based onthe type of analysis required this data was used, either:In its minute based format to obtain time-based profiles relating to thebuilding internal conditions (e.g. the temperature profile of a particular zoneduring a particular time period) or the plant network (e.g. the microtrigenerationsystem electrical output, the hot water circuit inlet temperatureto the chiller over a particular time period etc.); orIn time aggregated form to obtain system based parameters such as the CHPunit fuel consumption, the supplied thermal energy etc.4.2.1 Energetic performance metricsThe performance indicators used to determine the energetic performance of the145
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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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Page 134 and 135: Applying basic energy and mass cons
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Page 162 and 163: CHAPTER 4SIMULATIONMETHODOLOGYANDAN
Page 164 and 165: Table 4.1 - Scenarios investigatedS
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Page 174 and 175: 4.2.1.3 Calculating the fuel consum
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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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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
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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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