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 quasioppositional
ARO (Hybrid QOARO) algorithm, addresses both single-objective and multi-objective functions. The singleobjective
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.