基于DG无功参与的配电网综合无功优化

doi:10.11985/2023.03.028

摘要/Abstract

摘要：

基于碳达峰碳中和的理念，配电网系统中分布式电源(Distributed generation，DG)的占比越来越高，探讨DG对于配电网的无功优化具有实际意义。基于配电网的经济性与安全性，提出了计及DG无功出力的配电网综合优化模型，采用基于组合损失函数的BP神经网络(BP neural network)来预测风电出力曲线，用标准日的数据来生成光伏出力曲线，考虑风机与光伏共同参与无功优化。根据风机和光伏机组的结构，得出其无功出力的上下限。为了克服粒子群算法(Particle swarm optimization，PSO)容易陷入局部最优值的情况，采用了改进的粒子群算法。算例测试结果验证了DG无功出力对于配电网的影响，提出的模型可以进一步降低损耗，提高电压质量，并可以实现DG与静止无功补偿器(Static var compensator，SVC)的共同优化。

关键词: 分布式电源, 无功优化, 改进粒子群算法, 静止无功补偿器

Abstract:

Based on the concept of emission peak and carbon neutrality, the proportion of distributed generation in distribution network system is higher and higher, so it is of practical significance to explore DG for reactive power optimization of distribution network. Based on the economy and safety of distribution network, a comprehensive optimization model of distribution network considering DG reactive power output is proposed. BP neural network based on combined loss function is used to predict the wind power output curve, and the photovoltaic output curve is generated with the data of standard day. The participation of wind turbine and photovoltaic in reactive power optimization is considered. According to the structure of fan and photovoltaic unit, the upper and lower limits of reactive power output are obtained. In order to overcome the situation that particle swarm optimization algorithm is easy to fall into local optimal value, an improved particle swarm optimization algorithm is adopted. The example test results verify the influence of DG reactive power output on distribution network. The proposed model can further reduce loss and improve voltage quality, and realize the joint optimization of DG and static var compensator.

Key words: Distributed generation, reactive power optimization, improve particle swarm optimization, static var compensator

中图分类号:

TM561

万晓东, 马平, 万东红. 基于DG无功参与的配电网综合无功优化[J]. 电气工程学报, 2023, 18(3): 260-267.

WAN Xiaodong, MA Ping, WAN Donghong. Comprehensive Reactive Power Optimization of Distribution Network Based on DG Reactive Power Participation[J]. Journal of Electrical Engineering, 2023, 18(3): 260-267.

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训练样本数	训练时间/s	平均绝对误差	均方根误差
900	3.1	0.121 5	0.151 7
1 800	4.5	0.136 2	0.165 2
4 500	8.8	0.119 7	0.155 2
7 200	13.8	0.098 6	0.135 5

方案	全天总网损/kW	减少率(%)
未接入DG	5 648.39	—
接入DG未优化	4 582.79	18.9
接入DG优化后	4 378.25	22.5

名称	收敛到最优解的次数	平均迭代次数
传统PSO	4	65.7
改进PSO	10	52.5