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Enhancing the electrical performance of a solid oxide fuel cell using multiobjective genetic algorithms

Author

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  • Jurado, F.
  • Valverde, M.

Abstract

Several technologies are being used in distributed generation applications with variable degree of success. Among those are: wind turbines, small-scale hydropower plants, biomass, microturbines, photovoltaic arrays, and fuel cells. A fuel cell is a device that converts the chemical energy of fuel to electric energy. New improvements in the fuel cell technology significantly meliorated the technical characteristics of this technology. Environmental friendliness, practically noise free operation, and very high efficiency make fuel cells a challenger on the future electricity markets.

Suggested Citation

  • Jurado, F. & Valverde, M., 2005. "Enhancing the electrical performance of a solid oxide fuel cell using multiobjective genetic algorithms," Renewable Energy, Elsevier, vol. 30(6), pages 881-902.
  • Handle: RePEc:eee:renene:v:30:y:2005:i:6:p:881-902
    DOI: 10.1016/j.renene.2004.08.003
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    Cited by:

    1. Wei, Ya & Stanford, Russell J., 2019. "Parameter identification of solid oxide fuel cell by Chaotic Binary Shark Smell Optimization method," Energy, Elsevier, vol. 188(C).
    2. Miettinen, Kaisa & Molina, Julián & González, Mercedes & Hernández-Díaz, Alfredo & Caballero, Rafael, 2009. "Using box indices in supporting comparison in multiobjective optimization," European Journal of Operational Research, Elsevier, vol. 197(1), pages 17-24, August.
    3. Fathy, Ahmed & Rezk, Hegazy, 2022. "Political optimizer based approach for estimating SOFC optimal parameters for static and dynamic models," Energy, Elsevier, vol. 238(PC).
    4. Nasiri, Reza & Radan, Ahmad, 2011. "Adaptive decoupled control of 4-leg voltage-source inverters for standalone photovoltaic systems: Adjusting transient state response," Renewable Energy, Elsevier, vol. 36(10), pages 2733-2741.

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