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A decomposition approach to the two-stage stochastic unit commitment problem

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  • Qipeng Zheng
  • Jianhui Wang
  • Panos Pardalos
  • Yongpei Guan

Abstract

The unit commitment problem has been a very important problem in the power system operations, because it is aimed at reducing the power production cost by optimally scheduling the commitments of generation units. Meanwhile, it is a challenging problem because it involves a large amount of integer variables. With the increasing penetration of renewable energy sources in power systems, power system operations and control have been more affected by uncertainties than before. This paper discusses a stochastic unit commitment model which takes into account various uncertainties affecting thermal energy demand and two types of power generators, i.e., quick-start and non-quick-start generators. This problem is a stochastic mixed integer program with discrete decision variables in both first and second stages. In order to solve this difficult problem, a method based on Benders decomposition is applied. Numerical experiments show that the proposed algorithm can solve the stochastic unit commitment problem efficiently, especially those with large numbers of scenarios. Copyright Springer Science+Business Media, LLC 2013

Suggested Citation

  • Qipeng Zheng & Jianhui Wang & Panos Pardalos & Yongpei Guan, 2013. "A decomposition approach to the two-stage stochastic unit commitment problem," Annals of Operations Research, Springer, vol. 210(1), pages 387-410, November.
    • Handle: RePEc:spr:annopr:v:210:y:2013:i:1:p:387-410:10.1007/s10479-012-1092-7
    DOI: 10.1007/s10479-012-1092-7
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    2. Chao Li & Muhong Zhang & Kory Hedman, 2021. "Extreme Ray Feasibility Cuts for Unit Commitment with Uncertainty," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 1037-1055, July.
    3. Zhouchun Huang & Qipeng P. Zheng & Andrew L. Liu, 2022. "A Nested Cross Decomposition Algorithm for Power System Capacity Expansion with Multiscale Uncertainties," INFORMS Journal on Computing, INFORMS, vol. 34(4), pages 1919-1939, July.
    4. Wim Ackooij & Jérôme Malick, 2016. "Decomposition algorithm for large-scale two-stage unit-commitment," Annals of Operations Research, Springer, vol. 238(1), pages 587-613, March.
    5. Shin, Joohyun & Lee, Jay H. & Realff, Matthew J., 2017. "Operational planning and optimal sizing of microgrid considering multi-scale wind uncertainty," Applied Energy, Elsevier, vol. 195(C), pages 616-633.
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    7. Schulze, Tim & McKinnon, Ken, 2016. "The value of stochastic programming in day-ahead and intra-day generation unit commitment," Energy, Elsevier, vol. 101(C), pages 592-605.
    8. Jianqiu Huang & Kai Pan & Yongpei Guan, 2021. "Multistage Stochastic Power Generation Scheduling Co-Optimizing Energy and Ancillary Services," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 352-369, January.
    9. Yin, S. & Wang, J. & Li, Z. & Fang, X., 2021. "State-of-the-art short-term electricity market operation with solar generation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    10. Skolfield, J. Kyle & Escobedo, Adolfo R., 2022. "Operations research in optimal power flow: A guide to recent and emerging methodologies and applications," European Journal of Operational Research, Elsevier, vol. 300(2), pages 387-404.
    11. Richard Li-Yang Chen & Neng Fan & Ali Pinar & Jean-Paul Watson, 2017. "Contingency-constrained unit commitment with post-contingency corrective recourse," Annals of Operations Research, Springer, vol. 249(1), pages 381-407, February.
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