Enhanced Wasserstein Distributionally Robust OPF With Dependence Structure and Support Information

Adriano Arrigo, Jalal Kazempour, Zacharie De Greve, Jean-François Toubeau, Francois Vallée

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Abstract

This paper goes beyond the current state of the art related to Wasserstein distributionally robust optimal power flow problems, by adding dependence structure (correlation) and support information. In view of the space-time dependencies pertaining to the stochastic renewable power generation uncertainty, we apply a moment-metric-based distributionally robust optimization, which includes a constraint on the second-order moment of uncertainty. Aiming at further excluding unrealistic probability distributions from our proposed decision-making model, we enhance it by adding support information. We reformulate our proposed model, resulting in a semi-definite program, and show its satisfactory performance in terms of the operational
results achieved and the computational time.
Original languageEnglish
Title of host publicationProceedings of 2021 IEEE PowerTech
Number of pages6
PublisherIEEE
Publication statusAccepted/In press - 2021
Event14th IEEE PowerTech - Virtual Event - from the Alberto Aguilera Campus of Comillas University, Madrid, Spain
Duration: 27 Jun 20212 Jul 2021
https://www.powertech2021.com/

Conference

Conference14th IEEE PowerTech
LocationVirtual Event - from the Alberto Aguilera Campus of Comillas University
CountrySpain
CityMadrid
Period27/06/202102/07/2021
Internet address

Keywords

  • Distributionally robust optimization
  • Space-time dependencies
  • Optimal power flow
  • Out-of-sample analysis

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