Multi-objective optimization of hybrid CSP+PV system using genetic algorithm

Abstract

Renewable energy has experienced a significant growth on its rate of deployment as a clean and competitive alternative for conventional power sources. The reduction on the installation costs for PV systems has converted this technology into a relevant player regarding the electricity matrix. However, a larger penetration of PV systems is restricted to the availability of affordable technological options for storage. The integration of thermal energy storage to CSP systems is, on the other hand, straightforward through technologies already available in the market. Hence, the hybridization of CSP and PV systems has the potential for reducing operational and installation costs, as well as increasing significantly the capacity factor of solar power plants. The present study describes a methodology for design and sizing such hybrid plants, by implementing a transient simulation model, coupled to an evolutionary optimization algorithm, allowing to address the trade off between costs and capacity factor. The simulation model is applied to a case study considering the characteristics of a location in northern Chile. The results are presented in terms of the Pareto Frontiers that summarizes the compromise between the economic performance and the capacity factor of the plant. It is observed that the capacity factor achieves values higher that 85%, and the LCOE is lower than those observed for stand alone CSP plants. The methodology developed constitutes a useful tool for decision makers, who can assess the performance of the hybrid plant based in a detailed transient simulation and selecting the best configuration according to market constraints or its willingness for achieving certain level of capacity factor.