Risk-based Reserve Coordinative Unit Commitment for a Large-scale Wind-storage System. Issue 19 (14th December 2018)

• Record Type: Journal Article
• Authors: Zhang, Yumin; Han, Xueshan; Xu, Bo; Wang, Yong; Wang, Mengxia; Ye, Pingfeng
• Is Part Of: Electric power components and systems. Volume 46:Issue 19/20(2018)
• Page Start: 2004
• Page End: 2020
• Publication Date: 2018-12-14
• Subjects: conditional value-at-risk (CVaR) -- column and constraint generation algorithm (C&CG) -- energy storage system (ESS) -- reserve transfer (RT) -- reserve coordinative unit commitment; Electric machinery -- Periodicals; 621.3104205
• DOI: 10.1080/15325008.2018.1531444
• Languages: English
• ISSNs: 1532-5008

Abstract

Abstract: The increasingly high integration of wind power into power systems will further increase the power uncertainty and make it difficult to calculate the requirement of the reserve. In this article, a risk-based reserve optimization approach is proposed to quantitatively evaluate the reserve requirement of a large-scale wind-storage system. Conditional value-at risk (CVaR) is adapted to calculate the risk reserve managing the uncertainty of wind generation; this risk reserve achieves the optimal risk without sacrificing system reliability. An energy storage system (ESS) is employed to undertake the role of reserve transfer (RT) by cooperating with wind generation and conventional thermal units in the decision-making of unit commitment (UC); their cooperation and coordination can be achieved by building a bilevel optimization model with a day-ahead risk-constrained unit commitment model and a real-time risk reserve adjustment model. By using the duality principle and the big-M method, the formulations are converted into a mixed integer linear programing problem (MILP) that is solved using a column and constraint generation algorithm (C&CG). The model is tested on the six-bus system and the IEEE 118-bus system. The simulation results show that the ESS can transfer the reserve capacity of the thermal generator and improve the ability to accommodate the uncertainty of wind power generation, thereby demonstrating the effectiveness of the proposed methodology.