Optimal power flow under uncertainty: An extensive out-of-sample analysis

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The uncertainty induced by high penetration of stochastic generation in power systems requires to be properly taken into account within Optimal Power Flow (OPF) problems to make informed day-ahead decisions that minimize the social cost in view of potential balancing actions. This ends up in a two-stage OPF problem that is usually solved using two-stage stochastic programming or adaptive robust optimization. Another alternative is the use of chance-constrained programming that allows to control the conservativeness of the decisions. In this paper, we aim at defining a fair basis for assessing the performance of these three techniques, using an extensive out-of-sample evaluation. Considering a common wind power database, each technique leads to optimal day-ahead decisions that are a posteriori assessed through the real-time stage on unseen realizations of the uncertainty. Our main conclusion is that undertaking conservative decisions results in lower standard deviations of the cost, but at the expense of higher expected cost.
Original languageEnglish
Title of host publicationProceedings of 2019 IEEE PES Innovative Smart Grid Technologies Europe
Number of pages5
PublisherIEEE
Publication statusAccepted/In press - 2019
Event2019 IEEE PES Innovative Smart Grid Technologies Europe - Universitatea Politehnica din București, Bucharest, Romania
Duration: 29 Sep 20192 Oct 2019
Conference number: 8
http://sites.ieee.org/isgt-europe-2019/

Conference

Conference2019 IEEE PES Innovative Smart Grid Technologies Europe
Number8
LocationUniversitatea Politehnica din București
CountryRomania
CityBucharest
Period29/09/201902/10/2019
Internet address

    Research areas

  • Stochastic programming, Adaptive robust optimization, Chance-constrained programming, Optimal power flow, Out-of-sample analysis

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