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Regular Paper | Open Access

An Efficient Method for Identifying the Inactive Transmission Constraints in a Network-constrained Unit Commitment

Ziming Ma1, Haiwang Zhong1( ), Qing Xia1, Chongqing Kang1, Qiang Wang2, Xin Cao2
State Key Laboratory of Power Systems and the Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
State Grid Hebei Electric Power Company, Shijiazhuang 050000, China
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Abstract

Network-constrained unit commitment (NCUC) is one of the most widely used applications in power system and electricity market operations. According to empirical evidence, some of the transmission constraints in a NCUC are inactive. Identifying and eliminating these inactive constraints can improve the efficiency. In this paper, an efficient method is first proposed for identifying the inactive transmission constraints. The physical and economic insights of NCUC are carefully considered and utilized. Both the generating costs and power transfer distribution factor (PTDF) are considered. Not only redundant constraints but also non-binding constraints can be identified via the proposed method. An acceleration method that combines relaxation-based neighborhood search and improved relaxation inducement is proposed for further reducing the computation time. The case study shows that the proposed method can significantly reduce the number of transmission constraints and substantially improve the efficiency of NCUC without impacting the optimality.

Keywords

Inactive transmission constraintsimproved relaxation inducement (IRI)network-constrained unit commitment (NCUC)relaxation-based neighborhood search (RBNS)

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CSEE Journal of Power and Energy Systems
Volume 9 Issue 6,
November 2023
Pages 2366-2373
DOI: 10.17775/CSEEJPES.2020.00360
Cite this article:
Ma Z, Zhong H, Xia Q, et al. An Efficient Method for Identifying the Inactive Transmission Constraints in a Network-constrained Unit Commitment. CSEE Journal of Power and Energy Systems, 2023, 9(6): 2366-2373. https://doi.org/10.17775/CSEEJPES.2020.00360

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Received: 13 February 2020
Revised: 30 April 2020
Accepted: 23 May 2020
Published: 06 July 2020
© 2020 CSEE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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