A distributionally robust chance constrained optimization approach for security-constrained optimal power flow problems considering dependent uncertainty of wind power

Huang, Wenwei, Qian, Tong, Tang, Wenhu and Wu, Jianzhong ORCID: https://orcid.org/0000-0001-7928-3602 2025. A distributionally robust chance constrained optimization approach for security-constrained optimal power flow problems considering dependent uncertainty of wind power. Applied Energy 383 , 125264. 10.1016/j.apenergy.2024.125264

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Abstract

The integration of wind power generation introduces uncertainty into transmission line power, potentially increasing N -1 failure risks. This research proposes an N -1 line security-constrained optimal power flow (SCOPF) to mitigate such risks by considering wind power dependent uncertainty. Initially, a modified ambiguity set that integrates copula constraints to capture dependencies among wind farms is established, reducing conservatism. Then, the chance constraints (CC) representing security constraints (SC) are established through distributionally robust optimization, and the tractable forms of the proposed model are derived. Subsequently, dependence sensitivity indexes are proposed to identify components significantly affected by dependent uncertainty, and dependence-sensitivity-based ambiguity sets based on the dependence sensitivity indexes for the CC are established to reduce the solution complexity. Benders decomposition is then utilized to enable parallel processing and reduce computational time. Finally, the efficacy of the proposed strategy is demonstrated using IEEE 24-bus and IEEE 118-bus systems. Experimental results indicate that compared to SCOPF based on stochastic optimization or conventional distributionally robust optimization, the proposed model reduces cost while maintaining robustness, with significant reductions in computational burden attributed to dependence-sensitivity-based ambiguity sets and Benders decomposition.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by-nc-nd/4.0/, Start Date: 2027-01-16
Publisher:	Elsevier
ISSN:	0306-2619
Date of Acceptance:	30 December 2024
Last Modified:	21 Jan 2025 12:30
URI:	https://orca.cardiff.ac.uk/id/eprint/175451

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