Chance-Constrained AC Optimal Power Flow for Distribution Systems with Renewables

Emiliano Dall-Anese, Kyri Baker, Tyler Summers

Research output: Contribution to journalArticle

123 Scopus Citations


This paper focuses on distribution systems featuring renewable energy sources (RESs) and energy storage systems, and presents an AC optimal power flow (OPF) approach to optimize system-level performance objectives while coping with uncertainty in both RES generation and loads. The proposed method hinges on a chance-constrained AC OPF formulation where probabilistic constraints are utilized to enforce voltage regulation with prescribed probability. A computationally more affordable convex reformulation is developed by resorting to suitable linear approximations of the AC power-flow equations as well as convex approximations of the chance constraints. The approximate chance constraints provide conservative bounds that hold for arbitrary distributions of the forecasting errors. An adaptive strategy is then obtained by embedding the proposed AC OPF task into a model predictive control framework. Finally, a distributed solver is developed to strategically distribute the solution of the optimization problems across utility and customers.
Original languageAmerican English
Pages (from-to)3427-3438
Number of pages12
JournalIEEE Transactions on Power Systems
Issue number5
StatePublished - 2017

NREL Publication Number

  • NREL/JA-5D00-67689


  • distribution systems
  • model predictive control
  • optimal power flow
  • renewable integration
  • voltage regulation


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