电力系统自动化2017，Vol.41Issue(13)：30-38,9.DOI:10.7500/AEPS20161026003
基于随机响应面法的主动配电网无功优化
Optimal Reactive Power Flow in Active Distribution Network Based on Stochastic Response Surface Method
摘要
Abstract
Considering the reactive power regulation and control ability of the distributed generator (DG), the stochastic optimal reactive power flow (SORPF) model is proposed, in which the state variables are constrained by chance constraints.Nonparametric kernel density estimation is used to obtain the probability density function of random factors.The probabilistic power flow (PPF) calculation based on the stochastic response surface method is analyzed, which can be suitable for a variety of probabilistic models.The variables' correlation processing method based on Nataf transformation of PPF is also proposed.The SORPF model can be solved by referring to particle swarm optimization.Finally, the modified IEEE 33-bus distribution system and American PG&E 69-bus distribution system are utilized to test the correctness and effectiveness of SORPF.It follows from the test results that the proposed PPF method has higher accuracy when computing the cumulative distribution function.The test of different scenarios further verifies the model and algorithm proposed in this paper can adapt to different DG control strategies, find the probabilistic cross-border risk and have the ability to fix it.关键词
主动配电网/无功优化/机会约束规划/随机响应面法/非参数核密度估计/无功控制策略Key words
active distribution network/optimal reactive power flow/chance constrained programming/stochastic response surface method/nonparametric kernel density estimation/reactive power control strategy引用本文复制引用
张世达,孙永辉,赵景涛,卫志农,孙国强..基于随机响应面法的主动配电网无功优化[J].电力系统自动化,2017,41(13):30-38,9.基金项目
国家自然科学基金资助项目(61673161) （61673161）
江苏省自然科学基金资助项目(BK20161510) （BK20161510）
国家电网公司科技项目"分布式新能源规模接入下的配电网运检安全防护技术研究与关键设备研制".This work is supported by National Natural Science Foundation of China (No.61673161), Jiangsu Provincial Natural Science Foundation of China (No.BK20161510) and State Grid Corporation of China. （No.61673161）
