基于改进人工蜂群算法的配电系统储能优化控制策略

doi:10.19799/j.cnki.2095-4239.2025.0031

摘要/Abstract

摘要：

为解决高比例间歇性新能源接入对电网造成的波动影响，提高配电网对新能源的接纳能力，本工作提出了基于改进人工蜂群算法的配电系统储能优化控制策略。首先，对储能系统结构解析，并构建包含功率-容量模型、能量转换效率模型、荷电状态(SOC)模型的储能系统数学模型；接着，提出分布式储能系统双层优化控制模型，上层以系统电压偏差最小、系统稳定为目标构建稳定控制模型，下层以新能源消纳、系统有功网损为目标构建优化控制模型，对分布式储能进行功率分配；然后，引入人工蜂群算法(artificial bee colony，ABC)求解模型，采用柯西变异增强算法全局搜索能力，结合动态惯性权重机制提升收敛速度，提高模型求解精度和速度。最后，以典型IEEE33节点配电网络为例，验证所提策略有效性。结果表明所提控制策略能有效提升新能源消纳，充分发挥储能系统的调节作用，有利于保障配电网灵活可靠运行。

关键词: 储能系统, 控制策略, 人工蜂群算法, 柯西变异, 惯性权重, 新能源

Abstract:

To address voltage fluctuations caused by the high proportion of intermittent renewable energy access to the power grid and enhance the acceptance capacity of the distribution network for clean energy, this study proposes an energy storage optimization strategy for distribution systems using an improved artificial colony (ABC) algorithm. First, the structure of the energy storage system is analyzed, and mathematical models are constructed for the energy conversion efficiency, power-capacity relationships, and state of charge. Second, a two-layer optimal control model was proposed for a distributed energy storage system. The upper control layer prioritizes minimizing voltage deviation and maximizing system stability, while the lower layer optimizes renewable energy consumption and reduces system active power network loss by coordinating distributed energy storage power allocation. The ABC algorithm was introduced to solve the model, integrating Cauchy variation to enhance the global search ability of the algorithm, and a dynamic inertia weight mechanism was combined to accelerate convergence and improve solution accuracy. Finally, the proposed strategy was validated on a typical IEEE33-node distribution network, demonstrating its effectiveness in enhancing renewable energy utilization, maximizing the regulatory role of energy storage systems, and ensuring flexible, reliable distribution network operation.

Key words: energy storage system, control strategy, artificial colony algorithm, Cauchy variation, inertia weight, new energy

中图分类号:

TM 727

肖俊阳, 罗金阁, 马伟哲, 程武平, 曾通. 基于改进人工蜂群算法的配电系统储能优化控制策略[J]. 储能科学与技术, 2025, 14(6): 2567-2574.

Junyang XIAO, Jinge LUO, Weizhe MA, Wuping CHENG, Tong ZENG. Energy storage optimization control strategy in distribution system based on improved artificial bee colony algorithm[J]. Energy Storage Science and Technology, 2025, 14(6): 2567-2574.

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