考虑多风电场相关性的场景概率潮流计算及无功优化

邱宜彬; 欧阳誉波; 李奇; 陈维荣

引用本文:	邱宜彬,欧阳誉波,李奇,等.考虑多风电场相关性的场景概率潮流计算及无功优化[J].电力系统保护与控制,2017,45(2):61-68.
	QIU Yibin,OUYANG Yubo,LI Qi,et al.Scenario probabilistic load flow calculation and reactive power optimization considering wind farms correlation[J].Power System Protection and Control,2017,45(2):61-68

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考虑多风电场相关性的场景概率潮流计算及无功优化
邱宜彬¹,欧阳誉波²,李奇¹,陈维荣¹
(1.西南交通大学电气工程学院，四川成都 610031;2.国网湖南省电力公司张家界供电分公司，湖南张家界 427000)

摘要:

针对结合K-means聚类和Copula函数建立场景概率模型时，K-means聚类不能根据风电出力数据分布特点自发确定最佳聚类数这一不足，提出通过基于密度的聚类有效性指标确定最佳聚类数。并以此建立最优场景概率模型，采用改进型回溯搜索算法(BSA)进行无功优化。以澳大利亚的两个相邻风电场实测出力为例，在含多风电场的IEEE30节点系统中对所提方法进行验证，算例结果表明采用所提方法确定的最优场景概率模型能准确描述多风电场输出功率之间的相关性。

关键词: 场景概率潮流无功优化 K-means最佳聚类数 Copula函数改进型回溯搜索算法

DOI：10.7667/PSPC160100

分类号:

基金项目:国家科技支撑计划(2014BAG08B01);国家自然科学基金(51177138，61473238，51407146);四川省杰出青年基金(2015JQ0016)

Scenario probabilistic load flow calculation and reactive power optimization considering wind farms correlation

QIU Yibin¹,OUYANG Yubo²,LI Qi¹,CHEN Weirong¹

(1. School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China;2. Zhangjiajie Power Supply Branch, State Grid Hunan Electric Power Company, Zhangjiajie 427000, China)

Abstract:

When combining K-means clustering with Copula theory to build scenario-based probabilistic model, K-means clustering can’t spontaneously determine the optimal number of clustering. For this deficiency, this paper proposes a density-based index to determine the optimal number of clustering. Then the optimal scenario-based probabilistic model is built, based on which reactive power optimization is conducted by adopting modified backtracking search algorithm (BSA). At last, the proposed method is verified in IEEE30 system with data measured from two adjacent wind farms in Australia, and simulation results indicate that adopting optimal number of clustering determined by proposed method could get an accurate description of correlation between wind farms. This work is supported by National Key Technology Support Program (No. 2014BAG08B01), National Natural Science Foundation of China (No. 51177138, No. 61473238, and No. 51407146), and National Science Fund for Distinguished Young Scholars (No. 2015JQ0016).

Key words: probabilistic load flow reactive power optimization optimal number of k-means clusters Copula function modified backtracking search algorithm

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