Stochastic Optimal Dispatch of Power System Considering the Correlation of Multiple Wind Farm Outputs

被引:12
作者
Yang, Hongming [1 ]
Zhang, Yongxi [2 ]
Wang, Shuang [3 ]
Zhao, Junhua [4 ]
Lai, Mingyong [1 ]
Dong, Zhao Yang [2 ]
Huang, Jingjie [1 ]
机构
[1] Changsha Univ Sci & Technol, Sch Elect & Informat Engn, Hunan Prov Key Lab Smart Grids Operat & Control, Hunan Prov Engn Res Ctr Elect Transportat & Smart, Changsha 410114, Hunan, Peoples R China
[2] Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW 2006, Australia
[3] Shishi Power Ltd Liabil Co, Quanzhou, Peoples R China
[4] Chinese Univ Hong Kong Shenzhen, Shenzhen, Guangdong, Peoples R China
基金
中国国家自然科学基金;
关键词
stochastic optimal dispatch; correlation between multiple wind farm outputs; Gumbel copula function; chance constraints; sample average approximation; quantum-inspired evolutionary algorithm; GENERATION; MODEL; OPTIMIZATION;
D O I
10.1080/15325008.2015.1122103
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
As an important way of addressing energy and environmental challenges, the market share of wind power generation has increased dramatically in the past decade and has introduced significant challenges to power system operation. In this article, the tail correlation between multiple wind farms is studied. The joint probability distribution of multiple wind farms is estimated by employing the Gumbel copula function. Based on the estimated joint probability distribution, a stochastic optimal dispatch model is proposed to take into account the chance constraints of energy utilization from multiple wind farms in the power system. The sample average approximation method is employed to handle the chance constraints in the proposed model, so as to transform stochastic optimal dispatch into a deterministic non-linear optimization problem. The quantum-inspired evolutionary algorithm is used to solve the proposed model. The proposed model and algorithm are tested with comprehensive case studies to demonstrate their effectiveness.
引用
收藏
页码:616 / 627
页数:12
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