Multi-objective optimization of a CSP-based dish Stirling field layout using Genetic Algorithm and TOPSIS method: Case studies in Ouarzazate and Madrid

Abstract

CSP systems have experienced significant growth with great ability to supply clean and eco-friendly electrical power. In this paper, multi-objective optimization is carried out to design the most energy and cost-effective Solar Dish Stirling (SDS) power plant. Meteorological data from two different sites have been considered to simulate the technical and economical performances of 10 MW SDS power plant. Two main energy and economic conflicting objective functions were considered with the ultimate goal to maximize of the annual Capacity Factor and minimize the Total Cost of Site Improvements simultaneously. Several parameters such as the separation distance between collectors, ground slope, slot gap height and width have been parametrically assessed to find out effects on the Capacity Factor and the Total Cost of Site Improvements. First, a third-order multiple non-linear surrogate model was constructed. The model validity was demonstrated by the R-square values which were found to be 0.995 and 0.9964 for Ouarzazate and Madrid, respectively. Then, multi-objective optimization using Elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II) has been applied to address the trade-off between the two objective functions. The obtained Pareto Front was identified accordingly and final optimal solution was determined using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision-making method. Final optimal solution corresponded to Capacity Factor (CF) values of 22.99% and 12.91% and a Total Cost of Site Improvements (TCSI) of 2.69 M$ and 2.60 M$, for Ouarzazate and Madrid, respectively.