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A comparative analysis has been carried out between eight metaheuristic algorithms, namely; genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), grey wolf optimization (GWO), improved grey wolf optimization (IGWO), artificial bee colony (ABC), grasshopper optimization algorithm (GOA), and a proposed hybrid genetic-grasshopper (GA-GOA) to optimize the staggered heliostat field of the PS10 plant. The annual weighted efficiency is taken as the objective function for field layout optimization. In addition, the investigated algorithms have been assessed in terms of best energy yield, levelized cost of energy (LCOE), land use factor (LUF), and computational cost. It has been found that evolutionary algorithms outperform swarm intelligence algorithms in terms of efficiency, whereas GOA and GWO converge faster. To get high efficiency with low computational cost, a hybrid GA-GOA algorithm has been proposed. This study found that the hybrid GA-GOA algorithm does indeed deliver improved performance, with an optimum weighted efficiency boosted by 1.45% at a computation cost of ~63.7 h. In addition, it provides the best optimum LCOE of 26.22 c€/kWh and successfully enhances LUF by 11.2% compared to the PS10 reference plant. Based on these results, the authors can conclude that the proposed hybrid GA-GOA algorithm represents a suitable tool to cost-effectively optimize the design of heliostat field layouts and reduce their land footprint.
heliostat field optimization; hybrid ga-goa algorithm; land use factor; levelized cost of energy; metaheuristic (population-based) algorithms; solar tower power plant