Stochastic Optimization For Unit Commitment - A Review

Keywords

Electricity market operations; Mixed integer programming; Pricing; Risk constraints; Robust optimization; Stochastic programming; Uncertainty; Unit commitment

Abstract

Optimization models have been widely used in the power industry to aid the decision-making process of scheduling and dispatching electric power generation resources, a process known as unit commitment (UC). Since UC's birth, there have been two major waves of revolution on UC research and real life practice. The first wave has made mixed integer programming stand out from the early solution and modeling approaches for deterministic UC, such as priority list, dynamic programming, and Lagrangian relaxation. With the high penetration of renewable energy, increasing deregulation of the electricity industry, and growing demands on system reliability, the next wave is focused on transitioning from traditional deterministic approaches to stochastic optimization for unit commitment. Since the literature has grown rapidly in the past several years, this paper is to review the works that have contributed to the modeling and computational aspects of stochastic optimization (SO) based UC. Relevant lines of future research are also discussed to help transform research advances into real-world applications.

Publication Date

7-1-2015

Publication Title

IEEE Transactions on Power Systems

Volume

30

Issue

4

Number of Pages

1913-1924

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TPWRS.2014.2355204

Socpus ID

85028193123 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85028193123

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