Fast identifying redundant security constraints in SCUC in the presence of uncertainties. Issue 13 (14th May 2020)

• Record Type: Journal Article
• Authors: Ding, Tao; Qu, Ming; Bai, Jiawen; Jia, Wenhao; Wu, Jiang; He, Yuankang; Chen, Tianen
• Is Part Of: IET generation, transmission & distribution. Volume 14:Issue 13(2020)
• Page Start: 2441
• Page End: 2449
• Publication Date: 2020-05-14
• Subjects: power system security -- power generation dispatch -- computational complexity -- greedy algorithms -- power markets -- power generation scheduling -- power grids -- renewable energy sources; day‐ahead market -- computational complexity -- uncertain feasible region -- SCUC -- high dimensional space -- fast identification method -- classical greedy algorithm -- redundant security constraints -- security constrained unit commitment -- renewable resources -- power grid -- over‐relaxation prevention -- feasible‐based bound tightening strategy -- large‐scale test systems; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2019.1275
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : Security constrained unit commitment (SCUC), with renewable resources integrated into power grid, is one core function in the day‐ahead market. However, it confronts with critical challenges as the heavy complicated security constraints are considered. Usually, it is observed that lots of security constraints are redundant, which can be identified and eliminated, so as to reduce the computational complexity. In this study, the authors first lift the uncertain feasible region of SCUC into a high dimensional space. Furthermore, a fast identification method is proposed to relax the original feasible region, which thus can be solved by the classical greedy algorithm. In order to prevent the over‐relaxation and find more redundant constraints, an efficient feasible‐based bound tightening strategy is utilised to provide a tighter bound. Numerical results on large‐scale test systems verify the effectiveness of the proposed method.