Data-driven Security-Constrained AC-OPF for Operations and Markets

Research output: Research - peer-reviewArticle in proceedings – Annual report year: 2018

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In this paper, we propose a data-driven preventive security-constrained AC optimal power flow (SC-OPF), which ensures small-signal stability and N-1 security. Our approach can be used by both system and market operators for optimizing redispatch or AC based market-clearing auctions. We derive decision trees from large datasets of operating points, which capture all security requirements and allow to define tractable decision rules that are implemented in the SC-OPF using mixed-integer nonlinear programming (MINLP). We propose a second-order cone relaxation for the non-convex MINLP, which allows us to translate the non-convex and possibly disjoint feasible space of secure system operation to a convex mixed-integer OPF formulation. Our case study shows that the proposed approach increases the feasible space represented in the SC-OPF compared to conventional methods, can identify the global optimum as opposed to tested MINLP solvers and significantly reduces computation time due to a decreased problem size.
Original languageEnglish
Title of host publicationProceedings of 20th Power Systems Computation Conference
Number of pages7
Publication date2018
StateAccepted/In press - 2018
Event20th Power Systems Computation Conference - O’Brien Centre for Science at University College Dublin, Dublin, Ireland
Duration: 11 Jun 201815 Jun 2018
Conference number: 20


Conference20th Power Systems Computation Conference
LocationO’Brien Centre for Science at University College Dublin
Internet address

    Research areas

  • Security-constrained OPF, Small-signal stability, Convex relaxation, Mxed-integer non-linear programming
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ID: 149724412