Risk-Based Admissibility Analysis of Wind Power Integration into Power System With Energy Storage System

• Published in: IEEE access 2018-01, Vol.6, p.57400-57413
• Main Authors: Zhang, Yumin, Han, Xueshan, Xu, Bo, Wang, Mingqiang, Ye, Pingfeng, Pei, Younan
• Format: Article
• Language: English
• Subjects: column and constraint generation method (C&CG); Conditional value-at-risk (CVaR); Economic models; Electric energy storage; Electric power systems; Energy storage; energy storage system (ESS); flexible uncertainty set; Indexes; Load shedding; Optimization; Power systems; Reliability; Risk analysis; risk-based unit commitment; System reliability; Uncertainty; Unit commitment; Wind farms; wind generation uncertainty; Wind power; Wind power generation
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2870736

The increasing high penetration of wind power will further increase the uncertainty in power systems, and three key issues should be addressed: 1) determining the maximum accommodation level of wind power without sacrificing system reliability; 2) quantifying the potential risk when the wind generation realization is beyond the prescribed uncertainty sets; and 3) how to reduce the risk loss. Motivated by these, a risk-based two-stage robust unit commitment (RUC) model is proposed to analyze the admissibility of wind power. In this model, the electricity storage system (ESS) is utilized for managing the wind power uncertainty to reduce the risk loss. Different from a determined uncertainty set in the previous RUC, the proposed method can flexibly adjust the uncertainty set by optimizing the operational risks including the wind spillage risk and load shedding risk. Conditional Value-at Risk (CVaR) is adopted to describe the risk loss when the real wind power output is beyond the predefined uncertainty set. Meanwhile, the low-probability, high-influence events are taken into the account based on CVaR to determine the optimal acceptable wind generation considering the tradeoff between reliability and economics. The proposed model is solved effectively by the modified column and constraint generation method. Case studies on two benchmark systems illustrate that the ESS can reduce the risk loss of power system and improve the ability to accommodate the uncertainty of wind generation.