Distribution-Agnostic Stochastic Optimal Power Flow for Distribution Grids

Kyri Baker, Emiliano Dall-Anese, Tyler Summers

Research output: Contribution to conferencePaper

27 Scopus Citations


This paper outlines a data-driven, distributionally robust approach to solve chance-constrained AC optimal power flow problems in distribution networks. Uncertain forecasts for loads and power generated by photovoltaic (PV) systems are considered, with the goal of minimizing PV curtailment while meeting power flow and voltage regulation constraints. A data- driven approach is utilized to develop a distributionally robust conservative convex approximation of the chance-constraints; particularly, the mean and covariance matrix of the forecast errors are updated online, and leveraged to enforce voltage regulation with predetermined probability via Chebyshev-based bounds. By combining an accurate linear approximation of the AC power flow equations with the distributionally robust chance constraint reformulation, the resulting optimization problem becomes convex and computationally tractable.
Original languageAmerican English
Number of pages6
StatePublished - 2016
Event2016 North American Power Symposium (NAPS) - Denver, Colorado
Duration: 18 Sep 201620 Sep 2016


Conference2016 North American Power Symposium (NAPS)
CityDenver, Colorado

Bibliographical note

See NREL/CP-5D00-66844 for preprint

NREL Publication Number

  • NREL/CP-5D00-67824


  • chance constraints
  • distribution systems
  • optimal power flow
  • renewable integration
  • voltage regulation


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