Research on Capacity Configuration Technology of Distributed Energy Storage Based on Multi-objective Constraints

GAO Xiang; AN Yulun; ZHANG Yitao; CHENG Peng; ZHOU Wei

doi:10.13296/j.1001-1609.hva.2025.06.023

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Research on Capacity Configuration Technology of Distributed Energy Storage Based on Multi-objective Constraints

Research & Analysis | 更新时间：2025-06-12

- Research on Capacity Configuration Technology of Distributed Energy Storage Based on Multi-objective Constraints
- High Voltage Apparatus Vol. 61, Issue 6, Pages: 200-209(2025)
- 作者机构：
  
  国网冀北电力有限公司廊坊供电公司，河北　廊坊　 065000
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2025.06.023
  CLC：
- Received：25 November 2024，
  
  Revised：18 February 2025，
  
  Published：16 June 2025
- 稿件说明：
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GAO Xiang, AN Yulun, ZHANG Yitao, et al. Research on Capacity Configuration Technology of Distributed Energy Storage Based on Multi-objective Constraints[J]. High voltage apparatus, 2025, 61(6): 200-209.
DOI：

GAO Xiang, AN Yulun, ZHANG Yitao, et al. Research on Capacity Configuration Technology of Distributed Energy Storage Based on Multi-objective Constraints[J]. High voltage apparatus, 2025, 61(6): 200-209. DOI： 10.13296/j.1001-1609.hva.2025.06.023.

摘要

储能作为防止新能源并网引起电网波动问题的调节资源，存在考虑目标单一、不能全面考虑到配电网中的多项因素等局限性。针对此问题，文中提出了一种多目标优化的分布式储能配置模型。模型首先选取储能综合成本、电压波动、网损、负荷波动作为优化目标。接着根据储能配置流程，使用层次分析法对各目标函数赋权，确定最终的适应度函数。最终选取优化效率高的灰狼算法作为优化算法求解分布式储能的最优配置，同时在IEEE33节点配电网中验证算法的有效性，对优化效果以及各目标函数权值的敏感性进行分析。实验结果表明，配置方案下电压波动情况减少7.96%，负荷波动情况减少25.86%，网损情况降低了5.9%，证明了配置方案在降低网损、减少电压波动等方面的均具有效性。

Abstract

Energy storage

as a regulating resource to prevent grid fluctuations due to the integration of new energy

has such limitations as a single objective consideration and inability to comprehensively account for multiple factors in distribution networks. In response to this issue

in this paper a multi-objective optimized distributed energy storage configuration model is proposed. The model firstly selects comprehensive cost of energy storage

voltage fluctuation

network loss and and load fluctuation as optimization objectives. Then

according to the energy storage configuration process

the analytic hierarchy process (AHP) is used to assign weights to the objective functions

determining the final fitness function. The grey wolf optimizer algorithm with high optimization efficiency is finally selected as the optimization algorithm to solve for the optimal configuration of distributed energy storage. At the same time

the effective-ness of the algorithm is verified in the IEEE33-node distribution network

and the optimization effects and the sensitivity to the weights of the multi-objective function are analyzed. The experimental result shows that under the pro-posed configuration scheme

voltage fluctuation is reduced by 7.96%

load fluctuation by 25.86%

and network loss by 5.9%

proving the effectiveness of the configuration scheme in reducing network loss and voltage fluctuation.

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