Journal
IEEE TRANSACTIONS ON POWER SYSTEMS
Volume 33, Issue 2, Pages 1385-1398Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2017.2741506
Keywords
Ambiguity; chance constraints; distributionally robust optimization; uncertainty; unit commitment
Categories
Funding
- National Key Research and Development Program of China [2016YFB0901903]
- National Natural Science Foundation of China [51428702]
- Engineering and Physical Sciences Research Council [EP/L014351/1]
- Engineering and Physical Sciences Research Council [EP/L014351/1] Funding Source: researchfish
- EPSRC [EP/L014351/1] Funding Source: UKRI
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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.
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