Optimal Sizing and Placement of Photovoltaic, Battery Energy Storage and EV Charging Stations in Distribution Networks Using a Hybrid Swarm Intelligence Algorithm

The increasing integration of photovoltaic (PV) generation and electric vehicles (EVs) into distribution networks creates new challenges related to energy management, voltage regulation and network losses. Battery energy storage systems (BESSs) can provide additional flexibility by storing surplus renewable energy and supporting EV charging demand. However, the simultaneous sizing and placement of PV systems, BESSs and electric vehicle charging stations (EVCSs) constitutes a nonlinear mixed optimization problem involving discrete and continuous decision variables. This paper develops a hybrid swarm-intelligence framework for the coordinated planning of PV, BESS and EVCS infrastructure in a radial distribution network. The proposed Multi-Agent Adaptive-Acceleration Particle Swarm Optimization without Velocity and Grey Wolf Optimizer (MA-AA-PSWV-GWO) combines multi-agent search, adaptive acceleration, velocity-free particle swarm optimization and grey-wolf search mechanisms. The Baran–Wu 33-bus distribution network is considered using Saudi-oriented solar generation, electricity tariff and EV charging parameters. Four planning scenarios are investigated, ranging from fixed-location sizing to joint placement, sizing and 24 h BESS energy management. The proposed method is compared with PSO, MAPSO, GWO and AA-PSWV using a common objective function and network constraints. Over 10 independent runs, the proposed method achieves the lowest mean objective values and the standard deviations. The results show that the proposed hybrid framework provides competitive solution quality, consistency and feasibility for coordinated PV, BESS and EVCS planning.