Research Article
Multi-objective Optimization of Energy Storage Capacity Using Non-Dominated Sorting Artificial Cooperative Search and Composite Weighted TOPSIS
@INPROCEEDINGS{10.1007/978-3-031-96146-5_5, author={Xiao Ye and Anping Li and Feng Mu and Xiaofeng Chen and Xiangjuan Meng and Shilei Li and Jingyao Yang and Liguo Yang and Hongmei Li}, title={Multi-objective Optimization of Energy Storage Capacity Using Non-Dominated Sorting Artificial Cooperative Search and Composite Weighted TOPSIS}, proceedings={Smart Grid and Innovative Frontiers in Telecommunications. 8th EAI International Conference, EAI SmartGIFT 2024a, Santa Clara, United States, March 23-24, 2024, Proceedings}, proceedings_a={SMARTGIFT}, year={2026}, month={9}, keywords={Energy Storage Capacity Multi-Objective Optimization NSACS Algorithm Pareto Evaluation Composite Weighted TOPSIS}, doi={10.1007/978-3-031-96146-5_5} }- Xiao Ye
Anping Li
Feng Mu
Xiaofeng Chen
Xiangjuan Meng
Shilei Li
Jingyao Yang
Liguo Yang
Hongmei Li
Year: 2026
Multi-objective Optimization of Energy Storage Capacity Using Non-Dominated Sorting Artificial Cooperative Search and Composite Weighted TOPSIS
SMARTGIFT
Springer
DOI: 10.1007/978-3-031-96146-5_5
Abstract
With the promotion and application of renewable energy sources such as wind and solar power, which effectively reduce carbon emissions, the intermittency and volatility of these renewable sources impact power quality and stability. Energy storage systems, with their rapid charge and discharge capabilities and ease of construction, can smooth out fluctuations in renewable energy output, reduce wind and solar curtailment, improve supply reliability, and support the achievement of carbon neutrality goals. Therefore, a multi-objective optimization scheme for energy storage capacity is proposed in this paper. For proposed scheme, a multi-objective optimization model for energy storage capacity under low-carbon constraints is first set up, then, the Non-Dominated Sorting Artificial Cooperative Search (NSACS) algorithm is integrated with Pareto evaluation to generate the Pareto front, moreover, the composite weighted technique for order preference by similarity to ideal solution (CW-TOPSIS) is proposed to determine the optimal solution on the Pareto front. At last, case studies demonstrate that the proposed scheme is able to automatically determine the optimal energy storage capacity effectively and achieve synergistic optimization of energy storage economic efficiency, reliability, and carbon reduction.