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

Lejla Halilbasic, Florian Thams, Andreas Venzke, Spyros Chatzivasileiadis, Pierre Pinson

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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
    ISBN (Print)9781910963104
    Publication statusPublished - 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


    • Security-constrained OPF
    • Small-signal stability
    • Convex relaxation
    • Mxed-integer non-linear programming


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