Data-Driven Affinely Adjustable Distributionally Robust Unit Commitment

@article{Duan2018DataDrivenAA,
  title={Data-Driven Affinely Adjustable Distributionally Robust Unit Commitment},
  author={Chao Duan and Lin Jiang and Wanliang Fang and Jun Liu},
  journal={IEEE Transactions on Power Systems},
  year={2018},
  volume={33},
  pages={1385-1398}
}
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… CONTINUE READING

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