A novel hybrid algorithm based on optimal size and location of photovoltaic with battery energy storage systems for voltage stability enhancement

This paper proposes utilizing a recent metaheuristic technique, artificial rabbits’ optimization (ARO), enhanced with the quasi-opposition-based learning (QOBL) technique to improve global search capabilities. Furthermore, the novel line stability index (NLSI) is used to show weak buses in radial distribution systems (RDSs), aiding in the optimal placement and sizing of renewable energy sources (RES) such as photovoltaic (PV) systems. This enhanced algorithm, named the hybrid quasi-oppositional ARO (Hybrid QOARO) algorithm, addresses both single-objective and multi-objective functions. The single-objective approach focuses on reducing active power loss in the RDS, while the multi-objective function seeks to minimize active power loss with total voltage deviation (VD) and maximize the voltage stability index (VSI). This multi-objective approach helps determine the appropriate sizing of PV and battery energy storage systems (BESS) over 96 h (four seasons), considering the variability of photovoltaic power generation. To evaluate the effectiveness of the proposed approach compared to different optimization strategies, the IEEE 33-bus RDS is used. The highest reduction in energy losses and VD, at 92.48% and 99.78%, respectively, is achieved by applying PV + BESS at optimal power factor (PF) compared to PV only, PV + BESS at unity PF, and PV + BESS at 0.95 lagging PF.