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A priority list based approach for solving thermal unit commitment problem with novel hybrid genetic-imperialist competitive algorithm

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  • Saber, Navid Abdolhoseyni
  • Salimi, Mahdi
  • Mirabbasi, Davar

Abstract

This paper has proposed a novel Hybrid modified Genetic – Imperialist Competitive Algorithm (HGICA) for solving thermal Unit Commitment Problem (UCP). The UCP is a mixed integer problem with many equality and inequality constraints like the minimum down and minimum up time, spinning reserve, and ramp rate so need to a complex optimization process. In this paper the constraint handling of the problem is realized without any penalizing of solutions so a wide range of feasible solutions will be available for final optimum response. The proposed modified genetic is a novel method which has better performance than its original version and helps ICA to find more optimized responses and escape for local minimum areas easily. The main advantages of HGICA are good quality of the solution and high computational speed, which make it a suitable method for solving optimization problems. This method is carried out for three case studies including 10 and 20 units systems to efficiency of it be proved. Also the obtained results is compared to other optimization methods represented in literature for different scenarios.

Suggested Citation

  • Saber, Navid Abdolhoseyni & Salimi, Mahdi & Mirabbasi, Davar, 2016. "A priority list based approach for solving thermal unit commitment problem with novel hybrid genetic-imperialist competitive algorithm," Energy, Elsevier, vol. 117(P1), pages 272-280.
    • Handle: RePEc:eee:energy:v:117:y:2016:i:p1:p:272-280
    DOI: 10.1016/j.energy.2016.10.082
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    Citations

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    Cited by:

    1. 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.
    2. Anand, Himanshu & Narang, Nitin & Dhillon, J.S., 2018. "Profit based unit commitment using hybrid optimization technique," Energy, Elsevier, vol. 148(C), pages 701-715.
    3. Shahbazitabar, Maryam & Abdi, Hamdi, 2018. "A novel priority-based stochastic unit commitment considering renewable energy sources and parking lot cooperation," Energy, Elsevier, vol. 161(C), pages 308-324.
    4. Wang, Jinwen & Guo, Min & Liu, Yong, 2018. "Hydropower unit commitment with nonlinearity decoupled from mixed integer nonlinear problem," Energy, Elsevier, vol. 150(C), pages 839-846.
    5. Erica Ocampo & Yen-Chih Huang & Cheng-Chien Kuo, 2020. "Feasible Reserve in Day-Ahead Unit Commitment Using Scenario-Based Optimization," Energies, MDPI, vol. 13(20), pages 1-17, October.
    6. 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.

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