Multi-Objective Exergoeconomic Optimization of an Integrated Solar Combined Cycle System Using Evolutionary Algorithms


In this study, a multi-objective optimization scheme is developed and applied for an Integrated Solar Combined Cycle System that produces 400MW of electricity to find solutions that simultaneously satisfy exergetic as well as economic objectives. This corresponds to a search for the set of Pareto optimal solutions with respect to the two competing objectives. The optimization process is carried out by a particular class of search algorithms known as multi-objective evolutionary algorithms. An example of decision-making has been presented and a final optimal solution has been introduced. The analysis shows that optimization process leads to 3.2% increasing in the exergetic efficiency and 3.82% decreasing of the rate of product cost. Finally, sensitivity analysis is carried out to study the effect of changes in the Pareto optimal solutions to the system important parameters, such as interest rate, fuel cost, solar operation period, and system construction period.