An optimal sizing framework of a microgrid system with hydrogen storage considering component availability and system scalability by a novel approach based on quantum theory

Abstract

This paper introduces an innovative framework for optimizing a hybrid photovoltaic-wind system with integrated hydrogen storage, taking into account component availability and system scalability. The primary objective is to minimize the total net present cost (TNPC) while enhancing system reliability based on the Loss of Power Supply probability (LPSP). The decision variables include the determination of the number of photovoltaic (PV) panels, wind turbines (WTs), hydrogen storage mass, fuel cell capacity, electrolyzer capacity, and inverter capacity. To achieve this optimization, the Quantum Beluga Whale Optimization (QBWO) algorithm is employed, which is an improved version of the Beluga Whale Optimization (BWO) algorithm. QBWO overcomes some limitations of BWO by incorporating principles from quantum theory and addressing challenges related to the scarcity of population diversity and stagnation in local optima. Simulations were conducted for scenarios involving WT/FC, PV/FC, and PV/WT/FC systems. The results indicate that within a microgrid context, the PV/WT/FC configuration proves to be the most cost-effective approach for supplying local loads. Under certain conditions, it achieved the best TNPC with 0.8176 M$, Levelized Cost of Energy (LCOE) with 0.264 $/kWh, and LPSP with 0.04687. However, considering components availability and system scalability changes indicate a significant effect on the system cost and reliability. Overall, this framework provides a precise method for designing energy systems, considering factors such as electricity generation costs and reliability enhancements in the face of uncertainties. QBWO outperforms original BWO, Particle Swarm Optimization (PSO), and Crow Search Algorithm (CSA) by achieving lower design costs and improved reliability. This study contributes to developing environmentally friendly electrification plans and provides valuable insights for power investments in energy-deprived Southwest Morocco.