Introduction to Exergoeconomic and Multi- Objective Optimisation of Energy- Conversion Systems using Excel

The multi-objective optimisation (MOO) analyses presented in previous chapters of this book used the free version of the MIDACO solver [1] that is limited to accept only four design variables. In general, MOO analyses may require more than this number of variables, but the development of such special solvers is both expensive and time-consuming [2,3]. These two chapters present a technique for using Excel’s single-objective solver, Solver [4], with the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) decisionmaking method [5] for MOO analyses of energy-conversion systems. As a posteriori decision-making method, TOPSIS is usually used for selecting the most favourable solution from the set of optimal solutions generated by an MOO solver [6-8]. With the present technique, TOPSIS and Solver are coupled for conducting MOO analyses with a priori objectives preference. Four cases are presented in Chapter 8 to demonstrate the application of the Solver-TOPSIS technique (STT) for different types of energy-conversion systems. The first case is the hot-water generation (HWG) system considered in Chapter 3, the second case is the two-stage intercooled air compressor (TSIAC) considered in Chapter 4, the third case is the two-stage vapour-compression refrigeration (VCR) system considered in Chapter 6, and the fourth case is the cascade VCR system considered in Chapter 7. The results of the STT are validated by comparison with those obtained earlier for the same systems by using the MIDACO solver. Chapter 9 applies the Solver-TOPSIS technique described in Chapter 8 for a dual-objective optimisation analysis of the regenerative gas-turbine (RGT) powergeneration cycle. The objective of the optimisation analysis is to maximise the system’sthermal efficiency while reducing its total cost rate that includes both the initial cost of equipment and the operation cost.  Seven design parameters are considered in the analysis but, in order to illustrate the effect of increasing the number of changing variables on the optimisation result, the analysis is done in three steps with four, six, and seven variables in the step. The analysis with four design variables is initially used to check the accuracy of the STT technique by comparing its result with that obtained by using the free version of the MIDACO solver. The analyses with six and seven design parameters show that including more design variables in the optimisation analysis leads to a better system design. Finally, the chapter illustrates the use of the technique with optimisation analysis of the system with different a priori preferences of the two objectives.