A Distributionally Robust Optimization Model for Unit Commitment Based on Kullback-Leibler Divergence

doi:10.1109/TPWRS.2018.2797069

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A Distributionally Robust Optimization Model for Unit Commitment Based on Kullback-Leibler Divergence

2018-09-01

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DOI：10.1109/TPWRS.2018.2797069

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Abstract

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This paper proposes a new distance-based distributionally robust unit commitment (DB-DRUC) model via Kullback-Leibler (KL) divergence, considering volatile wind power generation. The objective function of the DB-DRUC model is to minimize the expected cost under the worst case wind distributions restricted in an ambiguity set. The ambiguity set is a family of distributions within a fixed distance from a nominal distribution. The distance between two distributions is measured by KL divergence. The DB-DRUC model is a min-max-min programming model; thus, it is intractable to solve. Applying reformulation methods and stochastic programming technologies, we reformulate this min-max-min DB-DRUC model into a one-level model, referred to as the reformulated DB-DRUC (RDB-DRUC) model. Using the generalized Benders decomposition, we then propose a two-level decomposition method and an iterative algorithm to address the RDB-DRUC model. The iterative algorithm for the RDB-DRUC model guarantees global convergence within finite iterations. Case studies are carried out to demonstrate the effectiveness, global optimality, and finite convergence of a proposed solution strategy.

Keywords:

Distributionally robust

generalized Benders decomposition

unit commitment

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