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

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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.

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Authors and Affiliations

  1. Department of Industrial & Management Systems Engineering, West Virginia University, P.O. Box 6070, Morgantown, WV, 26506, USA

    Qipeng P. Zheng

  2. Decision and Information Sciences Division, Argonne National Laboratory, Argonne, IL, 60439, USA

    Jianhui Wang

  3. Department of Industrial & Systems Engineering, University of Florida, Weil 303, P.O. Box 6595, Gainesville, FL, 32611, USA

    Panos M. Pardalos & Yongpei Guan

  4. Laboratory of Algorithms and Technologies for Networks Analysis (LATNA), Higher School of Economics, National Research University, 20, Myasnitskaya st., Moscow, 101000, Russia

    Panos M. Pardalos

Authors
  1. Qipeng P. Zheng
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  2. Jianhui Wang
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  3. Panos M. Pardalos
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  4. Yongpei Guan
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Correspondence to Qipeng P. Zheng.

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Zheng, Q.P., Wang, J., Pardalos, P.M. et al. A decomposition approach to the two-stage stochastic unit commitment problem. Ann Oper Res 210, 387–410 (2013). https://doi.org/10.1007/s10479-012-1092-7

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  • DOI: https://doi.org/10.1007/s10479-012-1092-7

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