Back to Search
Start Over
A Distributionally Robust Optimization Model for Unit Commitment Based on Kullback–Leibler Divergence.
- Source :
-
IEEE Transactions on Power Systems . Sep2018, Vol. 33 Issue 5, p5147-5160. 14p. - Publication Year :
- 2018
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08858950
- Volume :
- 33
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Power Systems
- Publication Type :
- Academic Journal
- Accession number :
- 131487732
- Full Text :
- https://doi.org/10.1109/TPWRS.2018.2797069