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Implementation of coyote optimization algorithm for solving unit commitment problem in power systems

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  • Ali, E.S.
  • Elazim, S.M. Abd
  • Balobaid, A.S.

Abstract

The aim of the Unit Commitment(UC) problem is to find the optimum scheduling of the total generating units at lower operating costs while achieving the constraints of system and units. The decision variables contain the binary UC variables that characterize the 1/0 cases through the total time intervals in the study era. The scale of this problem grows speedily with great size of the electric power system and longer planning time. Fixing this large scale problem is a challenging process and computationally expensive. It is the most complicated optimization process in the operation and planning of the power system. Meta heuristics methods are capable to outlast the demerits of traditional deterministic methods in solving UC problem. One of the most recent meta heuristics methods is known as Coyote Optimization Algorithm (COA). It is depended on the adaptation attitude of the coyote by the surroundings and the coyote's experiences exchanging. It has a motivating mechanisms to gain a balance between exploitation and exploration. Also, it is very easy in implementation as it has only two control variables. Moreover, its capability to keep larger diversity helps it to get the optimal cost so it is proposed to handle the UC problem in this paper. The election of the schedule and production size are performed by COA. Achievement of COA is examined for two IEEE systems. Outcomes establish that the elected algorithm is supreme to the recorded literature methods in terms of total cost, CPU, percentage reduction, and statistical analysis.

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  • 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).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pa:s036054422202583x
    DOI: 10.1016/j.energy.2022.125697
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    References listed on IDEAS

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    1. Wei Han & Hong-hua Wang & Xin-song Zhang & Ling Chen, 2013. "A Unit Commitment Model with Implicit Reserve Constraint Based on an Improved Artificial Fish Swarm Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-11, December.
    2. Anand, Himanshu & Narang, Nitin & Dhillon, J.S., 2019. "Multi-objective combined heat and power unit commitment using particle swarm optimization," Energy, Elsevier, vol. 172(C), pages 794-807.
    3. Georgopoulou, Chariklia A. & Giannakoglou, Kyriakos C., 2010. "Metamodel-assisted evolutionary algorithms for the unit commitment problem with probabilistic outages," Applied Energy, Elsevier, vol. 87(5), pages 1782-1792, May.
    4. Lokesh Kumar Panwar & Srikanth Reddy K & Rajesh Kumar, 2015. "Binary Fireworks Algorithm Based Thermal Unit Commitment," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 6(2), pages 87-101, April.
    5. Jouda Arfaoui & Hegazy Rezk & Mujahed Al-Dhaifallah & Mohamed N. Ibrahim & Mami Abdelkader, 2020. "Simulation-Based Coyote Optimization Algorithm to Determine Gains of PI Controller for Enhancing the Performance of Solar PV Water-Pumping System," Energies, MDPI, vol. 13(17), pages 1-17, August.
    6. Qais, Mohammed H. & Hasanien, Hany M. & Alghuwainem, Saad & Nouh, Adnan S., 2019. "Coyote optimization algorithm for parameters extraction of three-diode photovoltaic models of photovoltaic modules," Energy, Elsevier, vol. 187(C).
    7. Tawfik Guesmi & Badr M. Alshammari & Yasser Almalaq & Ayoob Alateeq & Khalid Alqunun, 2021. "New Coordinated Tuning of SVC and PSSs in Multimachine Power System Using Coyote Optimization Algorithm," Sustainability, MDPI, vol. 13(6), pages 1-18, March.
    8. Abdelaziz, A.Y. & Ali, E.S. & Abd Elazim, S.M., 2016. "Implementation of flower pollination algorithm for solving economic load dispatch and combined economic emission dispatch problems in power systems," Energy, Elsevier, vol. 101(C), pages 506-518.
    9. 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.
    10. Luis Montero & Antonio Bello & Javier Reneses, 2022. "A Review on the Unit Commitment Problem: Approaches, Techniques, and Resolution Methods," Energies, MDPI, vol. 15(4), pages 1-40, February.
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    Cited by:

    1. Devine, Mel & Lynch, Muireann Á, 2023. "Cournot competition in an integerconstrained electricity market model," Papers WP766, Economic and Social Research Institute (ESRI).
    2. Dong, Jizhe & Li, Yuanhan & Zuo, Shi & Wu, Xiaomei & Zhang, Zuyao & Du, Jiang, 2023. "An intraperiod arbitrary ramping-rate changing model in unit commitment," Energy, Elsevier, vol. 284(C).
    3. Hossein Lotfi & Mohammad Hasan Nikkhah, 2024. "Multi-Objective Profit-Based Unit Commitment with Renewable Energy and Energy Storage Units Using a Modified Optimization Method," Sustainability, MDPI, vol. 16(4), pages 1-29, February.
    4. Li, Ningning & Gao, Yan, 2023. "Real-time pricing based on convex hull method for smart grid with multiple generating units," Energy, Elsevier, vol. 285(C).

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