Cloud-AC-OPF: Model reduction technique for multi-scenario optimal power flow via chance-constrained optimization

Vladimir Frolov, Line Roald, Michael Chertkov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Many practical planning and operational applications in power systems require simultaneous consideration of a large number of operating conditions or Multi-Scenario AC-Optimal Power Flow (MS-AC-OPF) solution. However, when the number of exogenously prescribed conditions is large, solving the problem as a collection of single AC-OPFs may be time-consuming or simply intractable computationally. In this paper, we suggest a model reduction approach, coined Cloud-AC-OPF, which replaces a collection of samples by their compact representation in terms of mean and standard deviation. Instead of determining an optimal generation dispatch for each sample individually, we parametrize the generation dispatch as an affine function. The Cloud-AC-OPF is mathematically similar to a generalized Chance-Constrained AC-OPF (CC-AC-OPF) of the type recently discussed in the literature, but conceptually different as it discusses applications to long-term planning. We further propose a tractable formulation and implementation, and illustrate our construction on the example of 30-bus IEEE model.

Original languageEnglish (US)
Title of host publication2019 IEEE Milan PowerTech, PowerTech 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538647226
StatePublished - Jun 2019
Externally publishedYes
Event2019 IEEE Milan PowerTech, PowerTech 2019 - Milan, Italy
Duration: Jun 23 2019Jun 27 2019

Publication series

Name2019 IEEE Milan PowerTech, PowerTech 2019


Conference2019 IEEE Milan PowerTech, PowerTech 2019


  • Chance-constrained optimization
  • Complexity reduction
  • Non-linear optimization
  • Optimal power flow

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Safety, Risk, Reliability and Quality


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