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Solving the combined heat and power economic dispatch problems by an improved genetic algorithm and a new constraint handling strategy

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  • Zou, Dexuan
  • Li, Steven
  • Kong, Xiangyong
  • Ouyang, Haibin
  • Li, Zongyan

Abstract

This paper presents an improved genetic algorithm using novel crossover and mutation (IGA-NCM) to solve the combined heat and power economic dispatch (CHPED) problems. The basic genetic algorithm (GA) has been augmented in three aspects. First, the selection operation is excluded from GA in order to avoid excessive losses of population diversity. Second, two kinds of adaptive crossover operations are used to sufficiently excavate the information of parents and yield potential offsprings. Third, a novel mutation operation is used to replace a few genes of each crossed offspring by those of the other crossed offsprings’ parents, which can further improve their quality. Furthermore, a new constraint handling method is proposed to repair the mutated offsprings and enable them to enter feasible regions easily. Experimental results show that our proposed IGA-NCM algorithm outperforms the other ones according to computation accuracy and runtime. Therefore, it is a potential alternative for the CHPED problems with or without prohibited operating zones.

Suggested Citation

  • Zou, Dexuan & Li, Steven & Kong, Xiangyong & Ouyang, Haibin & Li, Zongyan, 2019. "Solving the combined heat and power economic dispatch problems by an improved genetic algorithm and a new constraint handling strategy," Applied Energy, Elsevier, vol. 237(C), pages 646-670.
  • Handle: RePEc:eee:appene:v:237:y:2019:i:c:p:646-670
    DOI: 10.1016/j.apenergy.2019.01.056
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    References listed on IDEAS

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    1. Shi, Bin & Yan, Lie-Xiang & Wu, Wei, 2013. "Multi-objective optimization for combined heat and power economic dispatch with power transmission loss and emission reduction," Energy, Elsevier, vol. 56(C), pages 135-143.
    2. Makkonen, Simo & Lahdelma, Risto, 2006. "Non-convex power plant modelling in energy optimisation," European Journal of Operational Research, Elsevier, vol. 171(3), pages 1113-1126, June.
    3. Zou, Dexuan & Li, Steven & Wang, Gai-Ge & Li, Zongyan & Ouyang, Haibin, 2016. "An improved differential evolution algorithm for the economic load dispatch problems with or without valve-point effects," Applied Energy, Elsevier, vol. 181(C), pages 375-390.
    4. Arias-Rosales, Andrés & Mejía-Gutiérrez, Ricardo, 2018. "Optimization of V-Trough photovoltaic concentrators through genetic algorithms with heuristics based on Weibull distributions," Applied Energy, Elsevier, vol. 212(C), pages 122-140.
    5. Chen, Wei-Hsin & Wu, Po-Hua & Lin, Yu-Li, 2018. "Performance optimization of thermoelectric generators designed by multi-objective genetic algorithm," Applied Energy, Elsevier, vol. 209(C), pages 211-223.
    6. Subbaraj, P. & Rengaraj, R. & Salivahanan, S., 2009. "Enhancement of combined heat and power economic dispatch using self adaptive real-coded genetic algorithm," Applied Energy, Elsevier, vol. 86(6), pages 915-921, June.
    7. Giasemidis, Georgios & Haben, Stephen & Lee, Tamsin & Singleton, Colin & Grindrod, Peter, 2017. "A genetic algorithm approach for modelling low voltage network demands," Applied Energy, Elsevier, vol. 203(C), pages 463-473.
    8. Chan, C.M. & Bai, H.L. & He, D.Q., 2018. "Blade shape optimization of the Savonius wind turbine using a genetic algorithm," Applied Energy, Elsevier, vol. 213(C), pages 148-157.
    9. Beigvand, Soheil Derafshi & Abdi, Hamdi & La Scala, Massimo, 2017. "Hybrid Gravitational Search Algorithm-Particle Swarm Optimization with Time Varying Acceleration Coefficients for large scale CHPED problem," Energy, Elsevier, vol. 126(C), pages 841-853.
    10. Rong, Aiying & Lahdelma, Risto, 2007. "An efficient envelope-based Branch and Bound algorithm for non-convex combined heat and power production planning," European Journal of Operational Research, Elsevier, vol. 183(1), pages 412-431, November.
    11. Kaplan, P. Ozge & Witt, Jonathan W., 2019. "What is the role of distributed energy resources under scenarios of greenhouse gas reductions? A specific focus on combined heat and power systems in the industrial and commercial sectors," Applied Energy, Elsevier, vol. 235(C), pages 83-94.
    12. Mellal, Mohamed Arezki & Williams, Edward J., 2015. "Cuckoo optimization algorithm with penalty function for combined heat and power economic dispatch problem," Energy, Elsevier, vol. 93(P2), pages 1711-1718.
    13. Alipour, Manijeh & Mohammadi-Ivatloo, Behnam & Zare, Kazem, 2014. "Stochastic risk-constrained short-term scheduling of industrial cogeneration systems in the presence of demand response programs," Applied Energy, Elsevier, vol. 136(C), pages 393-404.
    14. Kaelo, P. & Ali, M.M., 2007. "Integrated crossover rules in real coded genetic algorithms," European Journal of Operational Research, Elsevier, vol. 176(1), pages 60-76, January.
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