Multi-objective Site Selection and Capacity Optimization of Distributed PV Energy Storage in Smart Distribution Network Based on Non-cooperative Game

• Published in: Periodica polytechnica. Electrical engineering and computer science Vol. 69; no. 3; pp. 318 - 333
• Main Authors: Su, Hongshen, Zhao, Tian, Che, Yulong, Ge, Leijiao, Ma, Xiping, Zheng, Yangyang
• Format: Journal Article
• Language: English
• Published: Budapest Periodica Polytechnica, Budapest University of Technology and Economics 18.09.2025
• Subjects: Carbon; Electric power loss; Emissions; Energy distribution; Energy storage; Game theory; Multiple objective analysis; Optimization; Optimization models; Pareto optimum; Particle swarm optimization; Photovoltaic cells; Smart grid
• ISSN: 2064-5260; 2064-5279; 2064-5279
• DOI: 10.3311/PPee.40676

The disordered integration of high-penetration distributed photovoltaics (DPVs) into smart distribution networks has caused critical challenges including transformer reverse overloading and degraded power quality. Strategically deploying grid-level energy storage systems (ESSs) presents an effective solution to address these issues while enhancing operational efficiency and power quality. This paper proposes a non-cooperative game theory-driven optimal siting and sizing method for DPVs and ESSs in smart distribution networks. A tri-objective optimization model is formulated to mitigate grid vulnerability, reduce power losses, and minimize life-cycle carbon emissions of PV generation. To resolve conflicting interests among multiple stakeholders (DPV owners, ESS operators, and grid companies), a non-cooperative game framework with equilibrium strategies is established. An improved multi-objective particle swarm optimization (IMOPSO) algorithm is developed to solve the Nash equilibrium point that maximizes benefits for all participants. Case studies on IEEE 33 bus and IEEE-69 bus distribution systems demonstrate that the proposed method achieves: 2.43% reduction in grid vulnerability index, 4.29% decrease in network losses, and 44.44% reduction in PV life-cycle carbon emissions – all while maintaining voltage quality requirements and realizing Pareto-optimal allocation solutions for multi-stakeholder interests.