Multi-Objective Optimization of Cloud Energy Storage Placement and Sizing in Peer-to-Peer Energy Market Under Line Congestion
Jie Qi Ling, Wen‐Shan Tan, Ze Yang Ding, Yuan‐Kang Wu · 2025
The rising energy demand and growing adoption of distributed energy resources like solar panels highlight the need for efficient energy storage. Cloud energy storage (CES) enables optimal renewable energy utilization by storing surplus energy and acting as a backup during emergencies such as line congestion in distributed systems. To ensure economic and efficient CES deployment in Peer-to-Peer (P2P) energy markets, optimizing CES capacity, placement, and sizing within the distributed system is crucial. This study aims to find the best compromise solution that minimizes power loss, investment cost, and the average electricity cost for participants. Multi-objective particle swarm optimization (MOPSO), multi-objective Lichtenberg algorithm (MOLA) and multi-objective mantis search algorithm (MOMSA) were developed to generate Pareto-optimal solutions. The R-method is then used to identify the best compromise solution from the Pareto fronts. The results indicate that MOMSA outperformed the other algorithms in achieving the most balanced trade-off among the objectives, demonstrating it’s exploration and exploitation capabilities. This highlights MOMSA’s effectiveness in producing high-quality solutions, even under constrained and congested operating conditions.