Optimal Allocation of Energy Storage Capacity in Distributed Micro-grid Based on Improved Whale Algorithm

Abstract

At present, the setting of monitoring nodes for optimal allocation of energy storage capacity in micro-grid is generally target-based, with slow allocation speed and weak pertinence, which can easily lead to the increase of the final difference adjustment coefficient and affect the final allocation effect. Therefore, the research and analysis on the optimal allocation method of distributed micro-grid energy storage capacity based on the improved whale algorithm are proposed. According to the actual disposal requirements and standards, the basic operation frequency and reserve capacity of the micro-grid are determined, the multi-level monitoring node deployment form is set, the monitoring range is expanded, and the speed and pertinence of configuration are improved to complete the layout of energy storage capacity monitoring nodes. The optimal allocation model of the improved whale calculation of grid energy storage capacity is constructed. The optimal allocation is realized by constraint programming. The test results verify that the energy storage power of the micro-grid will be enhanced with the increase of the frequency modulation times of energy storage calculated by the whale calculation optimization configuration test group. In this context, the difference adjustment coefficient of the micro-grid configuration becomes lower, only 1.03, indicating that in the process of practical application, the energy storage capacity configuration of micro-grid is optimized. The frequency modulation fluctuation is slowed down, the running frequency is faster, and the method has practical application value.

Battery Storage Use in the Value Chain of Power Systems

“…In Chen et al's. ( 2023) [20] study, the optimal allocation methods based on an improved whale algorithm were implemented to enhance the configuration speed and pertinence of ES capacity monitoring nodes in distributed microgrids. The optimization approach improved the ES power of microgrids and reduced the difference adjustment coefficient.…”