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

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

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Abstract

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
PublisherIEEE
Publication date2018
ISBN (Print)9781910963104
DOIs
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
http://www.pscc2018.net/index.html

Conference

Conference20th Power Systems Computation Conference
Number20
LocationO’Brien Centre for Science at University College Dublin
CountryIreland
CityDublin
Period11/06/201815/06/2018
Internet address

Keywords

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

Cite this

Halilbasic, Lejla ; Thams, Florian ; Venzke, Andreas ; Chatzivasileiadis, Spyros ; Pinson, Pierre. / Data-driven Security-Constrained AC-OPF for Operations and Markets. Proceedings of 20th Power Systems Computation Conference. IEEE, 2018.
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title = "Data-driven Security-Constrained AC-OPF for Operations and Markets",
abstract = "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.",
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Halilbasic, L, Thams, F, Venzke, A, Chatzivasileiadis, S & Pinson, P 2018, Data-driven Security-Constrained AC-OPF for Operations and Markets. in Proceedings of 20th Power Systems Computation Conference. IEEE, 20th Power Systems Computation Conference, Dublin, Ireland, 11/06/2018. https://doi.org/10.23919/PSCC.2018.8442786

Data-driven Security-Constrained AC-OPF for Operations and Markets. / Halilbasic, Lejla; Thams, Florian; Venzke, Andreas; Chatzivasileiadis, Spyros; Pinson, Pierre.

Proceedings of 20th Power Systems Computation Conference. IEEE, 2018.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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