Two‐stage minimax stochastic unit commitment

Che, Ping; Tang, Lixin; Wang, Jianhui

doi:10.1049/iet-gtd.2017.1467

Two‐stage minimax stochastic unit commitment

Journal Article · Thu Jan 18 23:00:00 EST 2018 · IET Generation, Transmission, & Distribution

DOI:https://doi.org/10.1049/iet-gtd.2017.1467· OSTI ID:1786783

Che, Ping ^[1]; Tang, Lixin ^[2]; Wang, Jianhui ^[3]

Department of Mathematics Northeastern University Shenyang People's Republic of China, State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang People's Republic of China
Institute of Industrial &, Systems Engineering Northeastern University Shenyang People's Republic of China
Argonne National Laboratory Argonne IL 60439 USA

The study proposes a stochastic optimisation approach based on discrete scenarios and the minimax criterion to deal with demand uncertainty in the two‐stage unit commitment problem. To avoid making an over‐conservative decision, the approach is designed to assign a probability‐based weight to each demand scenario and provide a unit commitment schedule that minimises the maximum possible‐weighted generation cost. Unit commitment is determined in the first stage and the economic dispatch under the scenario that corresponds to the maximum possible‐weighted generation cost is determined in the second stage. The problem is formulated as a type of min–max–min mixed integer programming model. By introducing an auxiliary variable, the model is further transformed into a minimisation problem. A Benders decomposition algorithm is developed to solve the problem. The Benders master problem determines the unit commitment decision, while the Benders subproblem determines the dispatch decision. Multiple Benders feasibility cuts are constructed when the Benders subproblem is infeasible. Valid inequalities are derived to improve the lower bound provided by the Benders master problem. Numerical results show the performance of the algorithm.

View Accepted Manuscript (Publisher)

Sponsoring Organization:: USDOE

Grant/Contract Number:: OE0000842

OSTI ID:: 1786783

Alternate ID(s):: OSTI ID: 1466391

Journal Information:: IET Generation, Transmission, & Distribution, Journal Name: IET Generation, Transmission, & Distribution Journal Issue: 4 Vol. 12; ISSN 1751-8687

Publisher:: Institution of Engineering and Technology (IET)Copyright Statement

Country of Publication:: United Kingdom

Language:: English

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