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A semi-analytical non-iterative primary approach based on priority list to solve unit commitment problem

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  • Moradi, Saeed
  • Khanmohammadi, Sohrab
  • Hagh, Mehrdad Tarafdar
  • Mohammadi-ivatloo, Behnam

Abstract

For many years, the UC (unit commitment) problem has been solved by complex numerical techniques or intelligent search algorithms, due to nonlinear and complex constraints. Many of the applied algorithms employ random searches, which leads to production of different solutions in different program runs. Priority list-based methods are a way out to this, as they produce robust results during a non-iterative procedure, and without help of trial and error efforts. Nevertheless, they have all proven inefficient. This paper introduces a new approach that generates the solutions using algorithm-specific constraint handling techniques, based on the priority list concept. The solution-making stages include: 1. Minimum up/down time establishment using a probabilistic priority list-oriented selection mechanism, 2. Spinning reserve constraint handling through a deterministic priority list-based process, 3. Power balance handling and a ramp rate modification procedure for generating efficient ramp-constrained solutions. The different steps are designed such that efficient modifications are applied in each step without violating the previously established constraints. Simulation results on different test systems reveal that the approach obtains robust and competitive results. A new 140-unit large-scale test system based on Korean power system is also presented for verifying applicability of the proposed approach on real world power systems.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:88:y:2015:i:c:p:244-259
    DOI: 10.1016/j.energy.2015.04.102
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    Cited by:

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    2. Venter, Philip van Zyl & Terblanche, Stephanus Esias & van Eldik, Martin, 2018. "Turbine investment optimisation for energy recovery plants by utilising historic steam flow profiles," Energy, Elsevier, vol. 155(C), pages 668-677.
    3. Alexia Marchand & Michel Gendreau & Marko Blais & Jonathan Guidi, 2019. "Optimized operating rules for short-term hydropower planning in a stochastic environment," Computational Management Science, Springer, vol. 16(3), pages 501-519, July.
    4. Yang, Linfeng & Li, Wei & Xu, Yan & Zhang, Cuo & Chen, Shifei, 2021. "Two novel locally ideal three-period unit commitment formulations in power systems," Applied Energy, Elsevier, vol. 284(C).
    5. Biéron, M. & Le Dréau, J. & Haas, B., 2023. "Assessment of the marginal technologies reacting to demand response events: A French case-study," Energy, Elsevier, vol. 275(C).
    6. Yuan, Guanghui & Yang, Weixin, 2019. "Study on optimization of economic dispatching of electric power system based on Hybrid Intelligent Algorithms (PSO and AFSA)," Energy, Elsevier, vol. 183(C), pages 926-935.
    7. Ali, E.S. & Elazim, S.M. Abd & Balobaid, A.S., 2023. "Implementation of coyote optimization algorithm for solving unit commitment problem in power systems," Energy, Elsevier, vol. 263(PA).

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