Optimal Sizing and Operational Strategy of Hybrid Wind-Photovoltaic Systems with Energy Storage Using Crisscross Moss Growth Optimization

Hybrid wind-photovoltaic (PV) systems with energy storage play an important role in sustainable and reliable power generation, offering flexibility to meet variable energy demands. However, large-scale deployment of such systems faces challenges due to high costs, inefficient sizing, and the limited lifespan of energy storage components, which reduce overall system performance and economic feasibility. To address these issues, this manuscript proposes a Crisscross Moss Growth Optimization (CCMGO) technique for the optimum sizing and operational strategy of hybrid wind-photovoltaic systems with energy storage. The main objective of the proposed technique is to minimize total system costs, encompassing capital, replacement, disposal, and operational and maintenance expenses, while improving power management and control strategies. The CCMGO is used to optimize the strategy of the wind/ PV hybrid power plant, aiming to attain the lowest feasible cost of energy (COE). To assess its effectiveness, the proposed method is implemented and benchmarked against modified multi-objective particle swarm optimization (M-MOPSO), Caracal Optimization Algorithm (CAO), hybrid particle swarm optimization-shuffled frog leaping algorithm (PSO-SFLA), hybrid metaheuristic non-dominated sorting dung beetle optimizer (HMNSDBO) and hybrid Grey Wolf Optimizer-Whale Optimizer Algorithm (GWO-WOA) on the MATLAB platform. Across 30 independent runs, CCMGO achieves the lowest mean COE of 0.1167 US$/kWh compared with M-MOPSO, CAO, PSO-SFLA, HMNSDBO and GWO-WOA. CCMGO also requires the lowest computational time of 8.23 s and memory usage of 141.74 MB. The results demonstrate the potential of CCMGO for cost-oriented sizing and operational optimization of hybrid wind-PV systems with energy storage under the considered simulation conditions.