CONCENTRATED SOLAR THERMAL OPTIMIZATION WITH HYBRID FUZZY AND METAHEURISTIC APPROACHES

Abstract: Concentrated Solar Thermal (CST) systems are vital for dispatchable renewable energy but face challenges from nonlinear subsystem interactions and operational uncertainties. Deterministic and conventional metaheuristic models often neglect variability in heliostat alignment, receiver degradation, and turbine efficiency, limiting robustness. This study introduces a hybrid fuzzy–metaheuristic framework that integrates fuzzy logic for uncertainty modeling with Particle Swarm Optimization (PSO) for global search. Applied to the Noor CSP complex in Morocco, results showed optical efficiency improved by 5.6%, turbine efficiency by 7.9%, and LCOE reduced by 11.5%. The framework embeds uncertainty directly into optimization, ensuring technically feasible and economically competitive solutions.