Data-Driven Affinely Adjustable Distributionally Robust Unit Commitment

Chao Duan, Lin Jiang, Wanliang Fang*, Jun Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

63 Scopus citations


This paper proposes a data-driven affinely adjustable distributionally robust method for unit commitment considering uncertain load and renewable generation forecasting errors. The proposed formulation minimizes expected total operation costs, including the costs of generation, reserve, wind curtailment, and load shedding, while guaranteeing the system security. Without any presumption about the probability distribution of the uncertainties, the proposed method constructs an ambiguity set of distributions using historical data and immunizes the operation strategies against the worst case distribution in the ambiguity set. The more historical data is available, the smaller the ambiguity set is and the less conservative the solution is. The formulation is finally cast into a mixed integer linear programming whose scale remains unchanged as the amount of historical data increases. Numerical results and Monte Carlo simulations on the 118- A nd 1888-bus systems demonstrate the favorable features of the proposed method.

Original languageEnglish (US)
Pages (from-to)1385-1398
Number of pages14
JournalIEEE Transactions on Power Systems
Issue number2
StatePublished - Mar 2018


  • Ambiguity
  • chance constraints
  • distributionally robust optimization
  • uncertainty
  • unit commitment

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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