Optimal Configuration and Economic Evaluation of Hybrid Energy Storage Using an Improved Whale Optimization Algorithm

The new distribution network faces the problems of medium and long-period storage difficulties, low carbon and economy to be improved. Optimizing the configuration of hybrid energy storage will help to promote the goal of “double carbon.” The optimal configuration and economic evaluation of the hybrid energy storage composed of lithium battery energy storage and hydrogen energy storage are carried out in order to give full play to the role of energy storage in the new distribution network in this paper. Based on the sensitivity of network losses and the structural characteristics of the distribution network, optimal connection points for the hybrid storage system are identified. A bi-level optimization model is established, incorporating both capital investment and operational costs. The lower-level optimization focuses on minimizing operational costs to determine the optimal energy storage capacity, while the upper-level optimization aims to minimize the overall annual cost of the system. The Topsis method is used to select the Pareto-optimal solutions, yielding the optimal configuration of the hybrid energy storage system's capacity. During the optimization process, the fitness evaluation criterion from the simulated annealing algorithm is integrated into the whale optimization algorithm, which enhances the algorithm's ability to avoid premature convergence and accelerates its convergence speed. An economic evaluation framework for the distribution network is developed, and four different energy storage planning scenarios are assessed using a combination of gray relational analysis and the entropy weighting method. Simulation results based on an improved IEEE 33-node distribution network show that the optimized hybrid energy storage system can reduce line losses, improve voltage quality at nodes, enhance the utilization of distribution transformers, and significantly increase CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> and SO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emission reductions. Both operational and environmental performance indicators demonstrate significant improvements.