融合双重策略粒子群算法的分布式电源配网无功优化  

Reactive Power Optimization for Distribution System with DG by Particle Swarm Optimization Algorithm Integrating Dual Strategies

作　　者：李子健 郭佩乾 马宁宁 吴爱军 杨敏珑 LI Zijian;GUO Peiqian;MA Ningning;WU Aijun;YANG Minlong(Shanghai Urban Electric Power Supply Company,State Grid Shanghai Municipal Electric Power Company,Shanghai 200080,China;State Key Laboratory of Power System and Generation Equipment Department of Electrical Engineering,Tsinghua University,Beijing 100084,China)

机构地区：[1]国网上海市电力公司市区供电公司,上海200080 [2]电力系统及大型发电设备安全控制和仿真国家重点实验室(清华大学电机系),北京100084

出　　处：《南方电网技术》2022年第6期14-22,81,共10页Southern Power System Technology

基　　金：电力系统国家重点实验室资助课题(SKLD21M11)。

摘　　要：大规模、高密度的可再生能源分布式接入配网不仅导致原有系统网络系统结构复杂化,其随机性和波动性等特点也对网络潮流和系统电压带来诸多不利的影响。针对以上情况,以含分布式能源的配电网有功网络损耗与系统电压偏差为控制目标,以引入的静止无功补偿器为基础展开研究。融合天牛须搜索算法,结合吸引排斥和双向学习,提出一种改进式粒子群优化算法,自适应调整惯性权重与学习因子实现高效全局寻优。据此,结合上述研究内容和增强型IEEE 33节点配电系统模型,对所提配电网的无功电压控制效果与改进粒子群优化算法进行了验证。结果对比表明,所提融合天牛须搜索的双向学习粒子群优化算法在保障系统电压稳定性的前提下能够优化系统损耗。同时,与传统粒子群算法相比,计算所用时长、算法收敛速度和最优解寻求方面有较大提升。。The distributed integration of large-scale and high-density renewables energy complicates the distribution system structure.Besides,the fluctuation and randomness features of renewable energy bring great adverse effect on network power flow and voltage stability.To solve these problems,taking the active power loss and system voltage deviation as the control objectives,the reactive voltage optimization is studied in this paper based on the equipped static var compensator.An improved algorithm combining beetle colony antennae search algorithm with attractive repulsion and bidirectional learning is proposed.On the basis of the above research,together with the enhanced IEEE 33-node distribution system model,both the reactive voltage control effect of the proposed distribution system and the particle swarm optimization algorithm are verified.The results show that the proposed optimization algorithm can optimize the system power loss on the premise of ensuring the system voltage stability.Besides,compared with the conventional particle swarm optimization algorithm,the proposed optimization algorithm has great improvement in calculation time,convergence speed,and optimal search solution.

关 键 词：分布式可再生能源并网 配网损耗 无功功率优化 天牛须搜索 粒子群优化算法 吸引排斥和双向学习 

分 类 号：TM743[电气工程—电力系统及自动化]