Hybrid optimization for sustainable design and sizing of standalone microgrids integrating renewable energy, diesel generators, and battery storage with environmental considerations

Designing and sizing standalone microgrids integrating Solar PV, wind turbines (WT), diesel generators (DG), and battery energy storage systems (BES) involves balancing reliability, economic feasibility, and environmental sustainability. Conventional optimization methods often fail to achieve these objectives effectively, highlighting the need for hybrid approaches. In this context, this paper presents a hybrid optimization methodology for designing and sizing standalone microgrids incorporating Solar PV, WT, DG, and BES, with a focus on environmental sustainability. This study proposes the Hybrid Particle Whale Optimization Algorithm (HPWOA), combining Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA), to overcome the convergence issues of traditional methods. Three configurations (Solar PV/WT/BES/DG, Solar PV/BES/DG, WT/BES/DG) were assessed, considering weather patterns and demand fluctuations. The results show that the HPWOA outperforms other established algorithms, including the original PSO, WOA, and Stochastic Fractal Search Algorithm (SFSA), in determining the ideal microgrid configuration. The Solar PV/WT/BESS/DG setup, with 45 Solar PV units, 3 WT, 68 BESs, and 3 DGs, was found to be the most cost-effective, achieving a levelized cost of energy (COE) of 0.4912 $/kWh, a total net present cost (TNPC) of 324,003.40 $, a 4 % loss of power supply probability (LPSP), and CO2 emissions of 30.12 tons per year. The HPWOA outperforms traditional algorithms like PSO, WOA, and SFSA, providing better reliability, lower energy costs, and environmental benefits. This study offers a framework for advancing renewable microgrid systems, particularly for remote and rural areas.