A modified white shark optimizer for optimizing photovoltaic, wind turbines, biomass, and hydrogen storage hybrid systems

Recently, the focus has shifted to using renewable energy resources through off-grid solutions to provide electricity in isolated regions rather than traditional approaches. This paper investigates different configurations for optimal sizing of off-grid hybrid renewable systems, made up of photovoltaic (PV), biomass system (BGS), wind turbines (WT), and hydrogen storage (HS) unit with the intention of reducing the project energy cost (EC), excess energy (EE), and meeting the reliability index, such as the probability of power supply loss (PPSL). This paper focused on satisfying the energy demand of a small health center, and the suggested implementation region for this case study is the desert road of the New Qena area in the Qena Governorate, Egypt. The stochastic sizing of the recommended hybrid system is executed utilizing a novel modified White shark optimizer called mWSO. The proposed mWSO utilizes two improvement techniques: quasi-dynamic opposite learning (QDOL) and Weibull probability distribution (WPD) to enhance the efficiency of the original WSO method. The effectiveness of the new mWSO approach is verified through a comprehensive performance evaluation using cec2022 benchmark function and comparing its results with the original WSO as well as other known optimization approaches using different statistical parameters. After verifying the superiority of the proposed mWSO algorithm, it is employed to find the ideal configuration for four different scenarios of the proposed system (PV/WT/BGS/HS, PV/BGS/HS, PV/WT/HS, and WT/BGS/HS). The outcomes of the proposed mWSO showed superior performance compared to the remaining optimizers used, such as the original WSO, Dung Beetle Optimizer (DBO), Artificial Rabbits Optimization (ARO), Energy Valley Optimizer (EVO), and Artificial Hummingbird Algorithm (AHA) to achieve the lowest EC and reducing the total net present cost of the project (TNPC) in all scenarios of the proposed system.