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A practical multi-objective PSO algorithm for optimal operation management of distribution network with regard to fuel cell power plants

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  • Niknam, Taher
  • Meymand, Hamed Zeinoddini
  • Mojarrad, Hasan Doagou

Abstract

In this paper a novel Multi-objective fuzzy self adaptive hybrid particle swarm optimization (MFSAHPSO) evolutionary algorithm to solve the Multi-objective optimal operation management (MOOM) is presented. The purposes of the MOOM problem are to decrease the total electrical energy losses, the total electrical energy cost and the total pollutant emission produced by fuel cells and substation bus. Conventional algorithms used to solve the multi-objective optimization problems convert the multiple objectives into a single objective, using a vector of the user-predefined weights. In this conversion several deficiencies can be detected. For instance, the optimal solution of the algorithms depends greatly on the values of the weights and also some of the information may be lost in the conversion process and so this strategy is not expected to provide a robust solution. This paper presents a new MFSAHPSO algorithm for the MOOM problem. The proposed algorithm maintains a finite-sized repository of non-dominated solutions which gets iteratively updated in the presence of new solutions. Since the objective functions are not the same, a fuzzy clustering technique is used to control the size of the repository, within the limits. The proposed algorithm is tested on a distribution test feeder and the results demonstrate the capabilities of the proposed approach, to generate true and well-distributed Pareto-optimal non-dominated solutions of the MOOM problem.

Suggested Citation

  • Niknam, Taher & Meymand, Hamed Zeinoddini & Mojarrad, Hasan Doagou, 2011. "A practical multi-objective PSO algorithm for optimal operation management of distribution network with regard to fuel cell power plants," Renewable Energy, Elsevier, vol. 36(5), pages 1529-1544.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:5:p:1529-1544
    DOI: 10.1016/j.renene.2010.11.027
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    References listed on IDEAS

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    Cited by:

    1. Haddadian, Hossein & Noroozian, Reza, 2017. "Optimal operation of active distribution systems based on microgrid structure," Renewable Energy, Elsevier, vol. 104(C), pages 197-210.
    2. Ebrahim Farjah & Mosayeb Bornapour & Taher Niknam & Bahman Bahmanifirouzi, 2012. "Placement of Combined Heat, Power and Hydrogen Production Fuel Cell Power Plants in a Distribution Network," Energies, MDPI, vol. 5(3), pages 1-25, March.
    3. Gabrielli, Paolo & Gazzani, Matteo & Mazzotti, Marco, 2018. "Electrochemical conversion technologies for optimal design of decentralized multi-energy systems: Modeling framework and technology assessment," Applied Energy, Elsevier, vol. 221(C), pages 557-575.
    4. Zhang, Houcheng & Chen, Liwei & Zhang, Jinjie & Chen, Jincan, 2014. "Performance analysis of a direct carbon fuel cell with molten carbonate electrolyte," Energy, Elsevier, vol. 68(C), pages 292-300.
    5. Khorramdel, Benyamin & Raoofat, Mahdi, 2012. "Optimal stochastic reactive power scheduling in a microgrid considering voltage droop scheme of DGs and uncertainty of wind farms," Energy, Elsevier, vol. 45(1), pages 994-1006.
    6. Roldán-Blay, Carlos & Escrivá-Escrivá, Guillermo & Roldán-Porta, Carlos & Álvarez-Bel, Carlos, 2017. "An optimisation algorithm for distributed energy resources management in micro-scale energy hubs," Energy, Elsevier, vol. 132(C), pages 126-135.
    7. Haddadian, Hossein & Noroozian, Reza, 2017. "Multi-microgrids approach for design and operation of future distribution networks based on novel technical indices," Applied Energy, Elsevier, vol. 185(P1), pages 650-663.
    8. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Multi-objective self-scheduling of CHP (combined heat and power)-based microgrids considering demand response programs and ESSs (energy storage systems)," Energy, Elsevier, vol. 55(C), pages 1044-1054.

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