Data-Driven Affinely Adjustable Distributionally Robust Unit Commitment

Duan, C ORCID: 0000-0001-7358-3524, Jiang, L ORCID: 0000-0001-6531-2791, Fang, W and Liu, J (2017) Data-Driven Affinely Adjustable Distributionally Robust Unit Commitment. IEEE Transactions on Power Systems, 33 (2). pp. 1385-1398.

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Ambiguity, chance constraints, distributionally robust optimization, uncertainty, unit commitment
Depositing User:	Symplectic Admin
Date Deposited:	10 Oct 2017 06:45
Last Modified:	15 Mar 2024 04:54
DOI:	10.1109/TPWRS.2017.2741506
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3009900