Optimal multi-objective planning model for solar PV and energy storage systems in distribution systems: Long-term technical, economic, and environmental tradeoff analysis

The global shift toward renewable energy has intensified the need for intelligent distributed energy resource (DER) planning. However, integrating solar photovoltaic distributed generation (PV-DG) poses challenges due to its weather-dependent variability, which impacts system reliability. Coordinated planning of PV-DGs and battery energy storage systems (BESSs) can mitigate these issues; however, it introduces concerns such as higher capital costs and integration complexity. To address these issues, this study develops a long-term planning model for PV-DG and BESS integration, employing a metaheuristic approach to balance competing technical, economic, and environmental tradeoffs. This model introduces a novel solar-PV-focused BESS control strategy to enhance the operational efficiency and reliability of distribution systems (DSs). The optimization problem is solved using a multi-objective particle swarm optimization (MOPSO) algorithm. The optimization aims to determine the optimal siting and sizing of PV-DGs and BESSs to minimize the total expected power loss, voltage deviation, and system cost. To support decision-making among the set of Pareto-optimal solutions obtained by MOPSO, the technique for order of preference by similarity to ideal solution is applied. This method ranks solutions based on their closeness to the ideal trade-off point, enabling the selection of the most balanced configuration across technical, economic, and environmental objectives. The model is applied to the IEEE 33-bus DS over a 10-year planning horizon using MATLAB R2023b, evaluating three scenarios that include PV-DG and BESS integration. The results demonstrate that the model integrating PV-DGs and BESS significantly reduces power losses, voltage deviations, and total system cost by 52.55%, 41.99%, and 26.07%, respectively. These findings demonstrate the effectiveness of the model in achieving sustainable, cost-efficient, and technically reliable DER integration under realistic long-term planning conditions.