湖南电力 ›› 2025, Vol. 45 ›› Issue (3): 95-102.doi: 10.3969/j.issn.1008-0198.2025.03.014

• 电网运行与控制 • 上一篇    下一篇

基于改进红嘴蓝鹊优化算法的光储配电网无功优化方法

马凡烁, 于惠钧, 李嘉轩, 刘泽宇, 康钦泽   

  1. 湖南工业大学电气与信息工程学院,湖南 株洲 412007
  • 收稿日期:2025-01-08 修回日期:2025-01-31 发布日期:2025-07-02
  • 通信作者: 于惠钧(1975),男,博士,教授,硕士生导师,研究方向为电气分析与仿真、系统保护与自动化技术。
  • 作者简介:马凡烁(1999),男,硕士研究生,研究方向为电力系统仿真与配电网优化。李嘉轩(1999),男,硕士研究生,研究方向为光伏发电功率预测、微电网仿真优化。刘泽宇(1999),男,硕士研究生,研究方向为新能源并网控制及逆变器稳定性研究。康钦泽(1999),男,硕士研究生,主要研究方向为交直流混合微电网控制与电能质量优化技术。
  • 基金资助:
    国家重点研发计划项目(2022YFE0105200)

Reactive Power Optimization Method of Photovoltaic Energy Storage Distribution Network Based on Improved Red-Billed Blue Magpie Optimization Algorithm

MA Fanshuo, YU Huijun, Li Jiaxuan, LiU Zeyu, KANG Qinze   

  1. College of Electrical and information Engineering, Hunan University of Technology, Zhuzhou 412007, China
  • Received:2025-01-08 Revised:2025-01-31 Published:2025-07-02

摘要: 针对分布式电源接入配电网造成的有功损耗增加、电压偏移和负荷出力的不确定性等问题,构建储能系统与光伏电源联合调控的多目标优化模型,以有功网损最少和电压偏差最小为目标,通过有载调压变压器与电容器组动作约束和光伏发电、储能出力约束对无功优化模型进行分析。提出一种基于混沌映射透镜初始化、非线性参数因子、自适应惯性权重的改进红嘴蓝鹊优化算法,防止算法早熟收敛并增强全局搜索能力。运用该优化算法对含光储配电网的iEEE 33节点系统进行建模仿真,结果表明该算法具有寻优效果好、搜索效率高的优点,验证了该算法的高效性和稳定性。

关键词: 配电网, 无功优化, 光伏发电, 储能系统, 红嘴蓝鹊优化算法(RBMO)

Abstract: Aiming at the problems of increased active losses, voltage deviation and uncertainty of load output caused by distributed power supply access to the distribution network, a multi-objective optimization model is constructed for the joint regulation of energy storage system and photovoltaic(PV) power supply with the objectives of minimum active network losses and voltage deviation, and the reactive power optimization model is analyzed by means of the constraints of on-load regulating transformer and capacitor bank actions and the constraints of photovoltaic(PV) power generation and energy storage outputs. An improved red-billed blue magpie optimization algorithm based on chaotic mapping lens initialization, nonlinear parameter factors, and adaptive inertia weights is proposed to prevent the algorithm from premature convergence and enhance the global search capability. Finally, the improved algorithm is applied to simulate the IEEE 33-node system containing a photovoltaic-storage distribution network, and the comparison with other algorithms shows that the algorithm has the advantages of good optimization and high search efficiency, which verifies the efficiency and stability of the proposed algorithm.

Key words: distribution network, reactive power optimization, photovoltaic power generation, energy storage system, red-billed blue magpie optimization algorithm (RBMO)

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