Robust Generator Maintenance Schedule for Frequency-Secure Power Systems

• 成果类型: Article
• 署名作者: Yang, Yang; Sun, Qiuzhuang; Peng, Jimmy Chih-Hsien; Tang, Loon Ching; Ye, Zhisheng
• 署名单位: University of Macau; University of Macau; Singapore Management University; National University of Singapore; National University of Singapore
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.2023.0664
• 发表日期: 2026
• 关键词: stochastic unit commitment; optimization; uncertainty; OPERATIONS; MARKET; IMPACT

摘要

Problem definition: Normal operations of a power system require that alternating current frequency be maintained at a nominal value, for example, 50 Hz, whereas severe deviation from this value due to power deficiencies can cause cascading generator trips. Maintaining the frequency requires adequate inertia and frequency regulation reserve, which are primarily provided by online generators. In daily operations, generators due for preventive maintenance must be taken offline, and thus an improper maintenance schedule could jeopardize frequency security, as exemplified by the recent Texas power blackout. However, this natural nexus between frequency security and maintenance has been overlooked largely in the literature. Methodology/results: We fill the gap by developing a longterm generator maintenance scheduling model that incorporates frequency security constraints with hourly fidelity to meet industrial standards. These constraints amount to scheduling adequate inertia and frequency regulation reserve by considering uncertain power deficiency and inertia from intermittent renewable energy. We hedge the uncertainties by employing a robust optimization approach in which historical data are used to construct ambiguity sets. This inevitably results in an ultra-large-scale robust model because of the hourly fidelity. We reformulate it as a large-scale, mixed-integer linear program. An algorithm based on the progressive hedging idea is proposed to decompose the model into subprograms that can be solved in parallel. An explicit-dual cutting-plane method for the subprograms and a novel lower bound for the model are developed to accelerate computation in each iteration. Compared with the standard progressive hedging algorithm and an L-shaped algorithm with strengthened Benders cuts, our algorithm is approximately 10 times faster and avoids the out-of-memory issues encountered by these benchmarks. Managerial implications: Integrating frequency security enforces generator maintenance to distribute more evenly across the planning horizon. This leads to a more stable maintenance crew size and a significant reduction in out-of-sample costs in our simulation using real data. Additionally, our study reveals that inertia is crucial for frequency security and that low-cost inertia resources like synchronous condensers can enhance frequency security.