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Considering Maintenance Cost in Unit Commitment Problems

Author

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  • Hyeongon Park

    (Department of Statistics, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea)

  • Joonhyung Park

    (School of Information Technology & Electrical Engineering, The University of Queensland, St. Lucia, QLD 4072, Australia)

  • Jong-Young Park

    (Korea Railroad Research Institute, 176 Cheoldobangmulgwan-ro, Uiwang-si, Gyeonggi-do 16105, Korea)

  • Jae-Haeng Heo

    (Raon Friends, 267 Simin-daero, Dongan-gu, Anyang-si, Gyeonggi-do 14054, Korea)

Abstract

Electric power systems worldwide are receiving an increasing volume of wind power generation (WPG) because of environmental concerns and cost declines associated with technological innovation. To manage the uncertainty of WPG, a system operator must commit sufficient conventional generators to provide an appropriate reserve. At times, frequent start and stop operations are applied to certain generators, which incurs maintenance costs associated with thermal-mechanical fatigue. In this paper, we suggest a comprehensive approach to unit commitment (UC) that considers maintenance cost: the parameters of equivalent start (ES) and equivalent base load hours (EBHs) are adopted in the UC problem to determine optimal generation scheduling. A new formulation for the maintenance cost that can be readily combined with an existing mixed integer linear programming algorithm is presented. The effectiveness of the proposed UC method is verified through simulations based on an IEEE 118-bus test system. The simulation results show that considering maintenance cost in the UC problem effectively restricts frequent start and stop operation scheduling. Furthermore, the operating cost is reduced, the required reserve level is maintained, and the computational time is comparable with that of the conventional UC method.

Suggested Citation

  • Hyeongon Park & Joonhyung Park & Jong-Young Park & Jae-Haeng Heo, 2017. "Considering Maintenance Cost in Unit Commitment Problems," Energies, MDPI, vol. 10(11), pages 1-12, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1917-:d:119733
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    References listed on IDEAS

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    1. Wang, J. & Botterud, A. & Bessa, R. & Keko, H. & Carvalho, L. & Issicaba, D. & Sumaili, J. & Miranda, V., 2011. "Wind power forecasting uncertainty and unit commitment," Applied Energy, Elsevier, vol. 88(11), pages 4014-4023.
    2. Bird, Lori & Bolinger, Mark & Gagliano, Troy & Wiser, Ryan & Brown, Matthew & Parsons, Brian, 2005. "Policies and market factors driving wind power development in the United States," Energy Policy, Elsevier, vol. 33(11), pages 1397-1407, July.
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    Cited by:

    1. Pavel Y. Gubin & Vladislav P. Oboskalov & Anatolijs Mahnitko & Roman Petrichenko, 2020. "Simulated Annealing, Differential Evolution and Directed Search Methods for Generator Maintenance Scheduling," Energies, MDPI, vol. 13(20), pages 1-26, October.
    2. Khalid Alqunun & Tawfik Guesmi & Abdullah F. Albaker & Mansoor T. Alturki, 2020. "Stochastic Unit Commitment Problem, Incorporating Wind Power and an Energy Storage System," Sustainability, MDPI, vol. 12(23), pages 1-17, December.

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