Multi-objective exergoeconomic optimization of an Integrated Solar Combined Cycle System using evolutionary algorithms

Abstract

SUMMARYIn this study, a multi-objective optimization scheme is developed and applied for an Integrated Solar Combined Cycle System that produces 400 MW 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.

Exergoeconomic Analysis and Optimization of Solar Thermal Power Plants

“…In order to define a cost function which depends on the optimization parameters of interest, component costs have to be expressed as functions of thermodynamic variables (Baghernejad & Yaghoubi, 2011a, 2011b. These relationships can be obtained by the statistical correlations between costs and the main thermodynamic parameters of the component performed on real data series.…”

“…The mathematical formulation of objective functions is as following (Baghernejad & Yaghoubi, 2011b):…”

“…A comprehensive sensitivity analysis can be performed to examine the impact of the variation of important factors on electricity costs of any STPP. The most important factors which influence electricity cost of a solar thermal power plant are fuel specific cost, interest rate, plant lifetime, solar field operation period and construction period (Baghernejad & Yaghoubi, 2011a, 2011b.…”

“…In this table, states 0, 0 ' and 0'' are the dead states for air, water and oil, respectively. In this model, the variables selected for the optimization are (Baghernejad & Yaghoubi, 2011a, 2011b):…”

Optimum Integration of a Large Size Collector to a Solar Thermal Power Plant

“…To assess the overall performance of various configurations in a comparative manner, exergy method has been applied to the corresponding cycles (as a representative of the second law analysis). Exergy and exergoeconomic methods have received considerable attention during the past decade in the analysis and optimization of power plant cycles to achieve higher efficiencies and to reduce the operating cost of the corresponding power plants [3,5].…”