Investigation of Distribution Network Energy Storage Configuration Using an Enhanced Particle Swarm Optimization Algorithm With an Explosive Factor

As the integration of photovoltaic systems and battery energy storage devices into distribution networks increases, issues such as node voltage violations and higher network losses have emerged. To address these challenges, this paper proposes an optimal allocation strategy for energy storage systems in distribution networks based on an improved particle swarm optimization algorithm, namely, EP‐MOPSO. This strategy incorporates a nondominated sorting mechanism and grid adaptation to handle multiobjective optimization problems and employs an explosive particle strategy to enhance global exploration capability, suppress premature convergence, and maintain population diversity. An optimization model is established with the objectives of minimizing the economic cost of the energy storage configuration and optimizing its technical performance. The effectiveness of the proposed method is validated through simulations on the IEEE 33‐bus distribution system. Simulation results show that the total network loss is reduced by 1250.90 kW, the voltage profile is significantly improved, and the maximum voltage improvement outperforms other intelligent algorithms by over 37%. On the IEEE 69‐bus distribution system, the total network loss is reduced by 1457.66 kW, and the proposed strategy maintains the voltage stability of each line within a safe range. Compared with other intelligent algorithms, the maximum voltage improvement advantage exceeds 25%, while effectively reducing the installation cost of energy storage systems, thereby contributing to enhanced voltage stability of the distribution network.