PhD Thesis - Energy Systems Research Unit - University of Strathclyde

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