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Optimization of energy systems based on Evolutionary and Social metaphors

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  • Dimopoulos, George G.
  • Frangopoulos, Christos A.

Abstract

Optimization problems that arise in energy systems design often have several features that hinder the use of many optimization techniques. These optimization problems have non-continuous mixed variable definition domains, are heavily constrained, are multimodal (i.e. have many local optima) and, foremost, the functions used to define the engineering optimization problem are often computationally intensive. Three methods are tested here: (a) a Struggle Genetic Algorithm (StrGA), (b) a Particle Swarm Optimization Algorithm (PSOA), and (c) a PSOA with Struggle Selection (PSOStr). The last is a hybrid of the evolutionary StrGA and the socially inspired PSOA. They are tested in four purely mathematical and three energy systems thermoeconomic optimization problems. All of the methods solved successfully all the problems. The PSOStr, however, outperformed the other methods in terms of both solution accuracy and computational cost (i.e. function evaluations).

Suggested Citation

  • Dimopoulos, George G. & Frangopoulos, Christos A., 2008. "Optimization of energy systems based on Evolutionary and Social metaphors," Energy, Elsevier, vol. 33(2), pages 171-179.
    • Handle: RePEc:eee:energy:v:33:y:2008:i:2:p:171-179
    DOI: 10.1016/j.energy.2007.09.002
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    References listed on IDEAS

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    1. Valero, Antonio & Lozano, Miguel A. & Serra, Luis & Tsatsaronis, George & Pisa, Javier & Frangopoulos, Christos & von Spakovsky, Michael R., 1994. "CGAM problem: Definition and conventional solution," Energy, Elsevier, vol. 19(3), pages 279-286.
    2. Toffolo, A. & Lazzaretto, A., 2002. "Evolutionary algorithms for multi-objective energetic and economic optimization in thermal system design," Energy, Elsevier, vol. 27(6), pages 549-567.
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    Cited by:

    1. Yong Zeng & Yanpeng Cai & Guohe Huang & Jing Dai, 2011. "A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty," Energies, MDPI, vol. 4(10), pages 1-33, October.
    2. Kotowicz, Janusz & Bartela, Łukasz, 2010. "The influence of economic parameters on the optimal values of the design variables of a combined cycle plant," Energy, Elsevier, vol. 35(2), pages 911-919.
    3. Voll, Philip & Lampe, Matthias & Wrobel, Gregor & Bardow, André, 2012. "Superstructure-free synthesis and optimization of distributed industrial energy supply systems," Energy, Elsevier, vol. 45(1), pages 424-435.
    4. Kuo, Cheng-Chien, 2009. "Reactive energy scheduling using bi-objective programming with modified particle swarm optimization," Energy, Elsevier, vol. 34(6), pages 804-815.
    5. Lin, Q.G. & Huang, G.H., 2010. "An inexact two-stage stochastic energy systems planning model for managing greenhouse gas emission at a municipal level," Energy, Elsevier, vol. 35(5), pages 2270-2280.
    6. Sadegheih, A., 2009. "Optimization of network planning by the novel hybrid algorithms of intelligent optimization techniques," Energy, Elsevier, vol. 34(10), pages 1539-1551.
    7. Wang, Jiangjiang & Zhai, Zhiqiang (John) & Jing, Youyin & Zhang, Chunfa, 2011. "Influence analysis of building types and climate zones on energetic, economic and environmental performances of BCHP systems," Applied Energy, Elsevier, vol. 88(9), pages 3097-3112.
    8. Voll, Philip & Klaffke, Carsten & Hennen, Maike & Bardow, André, 2013. "Automated superstructure-based synthesis and optimization of distributed energy supply systems," Energy, Elsevier, vol. 50(C), pages 374-388.
    9. Frangopoulos, Christos A., 2018. "Recent developments and trends in optimization of energy systems," Energy, Elsevier, vol. 164(C), pages 1011-1020.
    10. Pires, Thiago S. & Cruz, Manuel E. & Colaço, Marcelo J., 2013. "Response surface method applied to the thermoeconomic optimization of a complex cogeneration system modeled in a process simulator," Energy, Elsevier, vol. 52(C), pages 44-54.

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