Fast identifying redundant security constraints in SCUC in the presence of uncertainties

• Published in: IET generation, transmission & distribution Vol. 14; no. 13; pp. 2441 - 2449
• Main Authors: Ding, Tao, Qu, Ming, Bai, Jiawen, Jia, Wenhao, Wu, Jiang, He, Yuankang, Chen, Tianen
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 03.07.2020; Wiley
• Subjects: classical greedy algorithm; computational complexity; day‐ahead market; fast identification method; feasible‐based bound tightening strategy; greedy algorithms; high dimensional space; large‐scale test systems; over‐relaxation prevention; power generation dispatch; power generation scheduling; power grid; power grids; power markets; power system security; redundant security constraints; renewable energy sources; renewable resources; Research Article; SCUC; security constrained unit commitment; uncertain feasible region; security constrained unit commitment; high dimensional space; renewable energy sources; renewable resources; greedy algorithms; fast identification method; large-scale test systems; classical greedy algorithm; power grid; redundant security constraints; power generation scheduling; feasible-based bound tightening strategy; power system security; day-ahead market; power generation dispatch; uncertain feasible region; over-relaxation prevention; SCUC; power markets; power grids; computational complexity
• ISSN: 1751-8687; 1751-8695
• DOI: 10.1049/iet-gtd.2019.1275

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.