A robust optimization method for power systems with decision-dependent uncertainty

被引：0
|
作者：
Tan, Tao [1 ]
Xie, Rui [1 ]
Xu, Xiaoyuan [2 ]
Chen, Yue [1 ]
机构：
[1] Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong
[2] Key Laboratory of Control of Power Transmission and Conversion (Shanghai Jiao Tong University), Ministry of Education, Shanghai, China
来源：
Energy Conversion and Economics | 2024年 / 5卷 / 03期
关键词：
Robust optimization is an essential tool for addressing the uncertainties in power systems. Most existing algorithms; such as Benders decomposition and column-and-constraint generation (C&CG); focus on robust optimization with decision-independent uncertainty (DIU). However; increasingly common decision-dependent uncertainties (DDUs) in power systems are frequently overlooked. When DDUs are considered; traditional algorithms for robust optimization with DIUs become inapplicable. This is because the previously selected worst-case scenarios may fall outside the uncertainty set when the first-stage decision changes; causing traditional algorithms to fail to converge. This study provides a general solution algorithm for robust optimization with DDU; which is called dual C&CG. Its convergence and optimality are proven theoretically. To demonstrate the effectiveness of the dual C&CG algorithm; we used the do-not-exceed limit (DNEL) problem as an example. The results show that the proposed algorithm can not only solve the simple DNEL model studied in the literature but also provide a more practical DNEL model considering the correlations among renewable generators. © 2024 The Author(s). Energy Conversion and Economics published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and the State Grid Economic & Technological Research Institute Co; Ltd;
D O I：
10.1049/enc2.12117
中图分类号：
学科分类号：
摘要：
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页码：133 / 145
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