Distributionally Robust Unit Commitment with N- k Security Criterion and Operational Flexibility of CSP

Younan Pei (), Xueshan Han (), Pingfeng Ye, Yumin Zhang, Mingbing Li and Huizong Mao
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Younan Pei: Key Laboratory of Power System Intelligent Scheduling and Control of Ministry of Education, Shandong University, Jinan 250061, China
Xueshan Han: Key Laboratory of Power System Intelligent Scheduling and Control of Ministry of Education, Shandong University, Jinan 250061, China
Pingfeng Ye: College of Energy Storage Technology, Shandong University of Science and Technology, Qingdao 266590, China
Yumin Zhang: Key Laboratory of Power System Intelligent Scheduling and Control of Ministry of Education, Shandong University, Jinan 250061, China
Mingbing Li: College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
Huizong Mao: College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China

Energies, 2022, vol. 15, issue 23, 1-21

Abstract: In order to reduce the conservatism of the robust optimization method and the complexity of the stochastic optimization method and to enhance the ability of power systems to deal with occasional line fault disturbance, this paper proposes a distributionally robust unit commitment (DRUC) model with concentrating solar power (CSP) operational flexibility and N- k safety criterion under distributed uncertainty. According to the limited historical sample data, under the condition of satisfying a certain confidence level, based on the imprecise Dirichlet model (IDM), an ambiguity set is constructed to describe the uncertainty of transmission line fault probability. Through the identification of the worst probability distribution in the ambiguity set, the adaptive robust optimal scheduling problem is transformed into a two-stage robust optimization decision model under the condition of deterministic probability distribution. The CSP flexibility column and constraint generation (C&CG) algorithm is used to process the model and the main problem and subproblem are solved by using the Big-M method, linearization technique, and duality principle. Then, a mixed integer linear programming problem (MILP) model is obtained, which effectively reduces the difficulty of solving the model. Finally, case studies on the IEEE 14 bus system and the IEEE 118 bus system demonstrate the efficiency of the proposed method, such as enhancing the ability of power systems to cope with occasional line fault disturbances and reducing the conservatism of the robust optimization method.

Keywords: ambiguity sets; distribution uncertainty; imprecise Dirichlet model; N- k security criterion; solar-thermal power generation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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