Data-Driven Affinely Adjustable Distributionally Robust Unit Commitment

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 curtail...

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Bibliographic Details
Published in:IEEE transactions on power systems 2018-03, Vol.33 (2), p.1385-1398
Main Authors: Duan, Chao, Jiang, Lin, Fang, Wanliang, Liu, Jun
Format: Article
Language:English
Subjects:
Ambiguity
chance constraints
distributionally robust optimization
Electrical engineering
Optimization
Probabilistic logic
Probability distribution
Random variables
Robustness
Uncertainty
unit commitment
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Summary: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- and 1888-bus systems demonstrate the favorable features of the proposed method.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2017.2741506