On the sizing of a solar thermal electricity plant for multiple objectives using evolutionary optimization

Design, implementation and operation of solar thermal electricity plants are no more an academic task, rather they have become a necessity. In this paper, we work with power industries to formulate a multi-objective optimization model and attempt to solve the resulting problem using classical as well as evolutionary optimization techniques. On a set of four objectives having complex trade-offs, our proposed procedure first finds a set of trade-off solutions showing the entire range of optimal solutions. Thereafter, the evolutionary optimization procedure is combined with a multiple criterion decision making (MCDM) approach to focus on preferred regions of the trade-off frontier. Obtained solutions are compared with a classical generating method. Eventually, a decision-maker is involved in the process and a single preferred solution is obtained in a systematic manner. Starting with generating a wide spectrum of trade-off solutions to have a global understanding of feasible solutions, then concentrating on specific preferred regions for having a more detailed understanding of preferred solutions, and then zeroing on a single preferred solution with the help of a decision-maker demonstrates the use of multi-objective optimization and decision making methodologies in practice. As a by-product, useful properties among decision variables that are common to the obtained solutions are gathered as vital knowledge for the problem. The procedures used in this paper are ready to be used to other similar real-world problem solving tasks.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slideHighlights► Optimal operating conditions of a solar thermal power plant are obtained through a multi-objective optimization procedure. ► Evolutionary multi-objective optimization (EMO) procedure is used to find a set of trade-off solutions for a four-objective problem. ► A multi-criterion decision making procedure is used to find preferred trade-off solutions. ► Further decision-making aid is used to choose a single preferred solution. ► As a by-product, vital properties commonly appearing in optimal trade-off solutions are reported.