Genetic Algorithm for Multi-Objective Exergetic and Economic Optimization of Parabolic Trough Collectors Integration Into Combined Cycle System (ISCCS)

Abstract

Thermoeconomic analyses of any thermal system design are always based on the economic objectives. However, knowledge of economic optimization may not be sufficient for decision making process, since solutions with higher thermodynamic efficiency, in spite of small increases in total costs, may result in much more interesting designs due to changes in the energy market prices or in the energy policies. In this paper a multi-objective optimization scheme is developed and applied for an Integrated Solar Combined Cycle System (ISCCS) that produces 400 MW of electricity to find solutions that simultaneously satisfy exergetic as well as economic objectives. This corresponds to search for a 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 (MOEAs). For such MOEAs, an example of decision-making is presented and a final optimal solution has been introduced. The final optimal solution that is selected in this analysis belongs to the region of Pareto Frontier with significant sensitivity to the costing parameters. However, the region with lower sensitivity to the costing parameter is not reasonable for the final optimum solution due to a weak equilibrium of Pareto Frontier in which a small changes in exergetic efficiency of plant due to variation of operating parameters may lead to the danger of increasing the cost rate of product, drastically. 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. Also optimization leads to the 2.73% reduction on the fuel exergy, 5.71% reductions in the total exergy destruction and also 3.46% and 7.32% reductions in the fuel cost rate and cost rate relating to the exergy destruction, respectively.