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A new three-stage method for solving unit commitment problem

Author

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  • Khanmohammadi, S.
  • Amiri, M.
  • Haque, M. Tarafdar

Abstract

This paper presents a new Three-Stage (THS) approach for solving Unit Commitment (UC) problem. The proposed method has a simple procedure to get at favorite solutions in a feasible duration of time by producing a primal schedule of status of units at the first step. In the second step the operating units take hourly values by doing Economic Dispatch (ED) on them via a hybrid serial algorithm of Artificial Intelligence (AI) including Particle Swarm Optimization (PSO) and Nelder–Mead (NM) algorithms. In spite of the acceptable solutions obtained by these two stages, the presented method takes another step called the solution modification process (SMP) to reach a more suitable solution. The simulation results over some standard cases of UC problem confirm that this method produces robust solutions and generally gets appropriate near-optimal solutions.

Suggested Citation

  • Khanmohammadi, S. & Amiri, M. & Haque, M. Tarafdar, 2010. "A new three-stage method for solving unit commitment problem," Energy, Elsevier, vol. 35(7), pages 3072-3080.
    • Handle: RePEc:eee:energy:v:35:y:2010:i:7:p:3072-3080
    DOI: 10.1016/j.energy.2010.03.049
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    Citations

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    Cited by:

    1. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
    2. Moradi, Saeed & Khanmohammadi, Sohrab & Hagh, Mehrdad Tarafdar & Mohammadi-ivatloo, Behnam, 2015. "A semi-analytical non-iterative primary approach based on priority list to solve unit commitment problem," Energy, Elsevier, vol. 88(C), pages 244-259.
    3. Ji, Bin & Yuan, Xiaohui & Chen, Zhihuan & Tian, Hao, 2014. "Improved gravitational search algorithm for unit commitment considering uncertainty of wind power," Energy, Elsevier, vol. 67(C), pages 52-62.
    4. Yang, Zhile & Li, Kang & Guo, Yuanjun & Feng, Shengzhong & Niu, Qun & Xue, Yusheng & Foley, Aoife, 2019. "A binary symmetric based hybrid meta-heuristic method for solving mixed integer unit commitment problem integrating with significant plug-in electric vehicles," Energy, Elsevier, vol. 170(C), pages 889-905.
    5. Vasilios A. Tsalavoutis & Constantinos G. Vrionis & Athanasios I. Tolis, 2021. "Optimizing a unit commitment problem using an evolutionary algorithm and a plurality of priority lists," Operational Research, Springer, vol. 21(1), pages 1-54, March.
    6. Glotić, Arnel & Glotić, Adnan & Kitak, Peter & Pihler, Jože & Tičar, Igor, 2014. "Optimization of hydro energy storage plants by using differential evolution algorithm," Energy, Elsevier, vol. 77(C), pages 97-107.
    7. Layon Mescolin de Oliveira & Ivo Chaves da Silva Junior & Ramon Abritta, 2022. "Search Space Reduction for the Thermal Unit Commitment Problem through a Relevance Matrix," Energies, MDPI, vol. 15(19), pages 1-16, September.
    8. Wang, Yongqiang & Zhou, Jianzhong & Mo, Li & Zhang, Rui & Zhang, Yongchuan, 2012. "Short-term hydrothermal generation scheduling using differential real-coded quantum-inspired evolutionary algorithm," Energy, Elsevier, vol. 44(1), pages 657-671.
    9. Nazari, M.E. & Ardehali, M.M. & Jafari, S., 2010. "Pumped-storage unit commitment with considerations for energy demand, economics, and environmental constraints," Energy, Elsevier, vol. 35(10), pages 4092-4101.
    10. Shukla, Anup & Singh, S.N., 2016. "Advanced three-stage pseudo-inspired weight-improved crazy particle swarm optimization for unit commitment problem," Energy, Elsevier, vol. 96(C), pages 23-36.
    11. Amiri, M. & Khanmohammadi, S. & Badamchizadeh, M.A., 2018. "Floating search space: A new idea for efficient solving the Economic and emission dispatch problem," Energy, Elsevier, vol. 158(C), pages 564-579.

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