Optimal Power Flow for Distribution Systems under Uncertain Forecasts: Preprint

Emiliano Dall-Anese, Kyri Baker, Tyler Summers

Research output: Contribution to conferencePaper


The paper focuses on distribution systems featuring renewable energy sources and energy storage devices, and develops an optimal power flow (OPF) approach to optimize the system operation in spite of forecasting errors. The proposed method builds on a chance-constrained multi-period AC OPF formulation, where probabilistic constraints are utilized to enforce voltage regulation with a prescribed probability. To enable a computationally affordable solution approach, a convex reformulation of the OPF task is obtained by resorting to i) pertinent linear approximations of the power flow equations, and ii) convex approximations of the chance constraints. Particularly, the approximate chance constraints provide conservative bounds that hold for arbitrary distributions of the forecasting errors. An adaptive optimization strategy is then obtained by embedding the proposed OPF task into a model predictive control framework.
Original languageAmerican English
Number of pages8
StatePublished - 2016
Event55th IEEE Conference on Decision and Control - Las Vegas, Nevada
Duration: 12 Dec 201614 Dec 2016


Conference55th IEEE Conference on Decision and Control
CityLas Vegas, Nevada

NREL Publication Number

  • NREL/CP-5D00-66856


  • convex programming
  • optimal power flow
  • renewable sources of energy
  • uncertainty


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