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Optimization of Solar/Fuel Cell Hybrid Energy System Using the Combinatorial Dynamic Encoding Algorithm for Searches (cDEAS)

Author

Listed:
  • Jong-Wook Kim

    (Department of Electronic Engineering, Dong-A University, Busan 60471, Korea)

  • Heungju Ahn

    (School of Undergraduate Studies, Daegu Gyeongbuk Institute of Science and Technology, Daegu 42988, Korea)

  • Hyeon Cheol Seo

    (Division of Intelligent Robot, Convergence Research Institute, Daegu Gyeongbuk Institute of Science and Technology, Daegu 42988, Korea)

  • Sang Cheol Lee

    (Division of Intelligent Robot, Convergence Research Institute, Daegu Gyeongbuk Institute of Science and Technology, Daegu 42988, Korea)

Abstract

This study proposes a computational design method for determining a hybrid power system’s sizing and ratio values that combines the national electric, solar cell, and fuel cell power sources. The inequality constraints associated with the ranges of power storage exchange and the stored energy are reflected as penalty functions in the overall cost function to be minimized. Using the energy hub model and the actual data for the solar cell power and the load of the residential sector in one Korean city for one hundred days, we optimize the ratio of fuel cell energy and solar cell energy to 0.46:0.54 through our proposed approach. We achieve an average cost-reduction effect of 19.35% compared to the cases in which the fuel-cell energy ratio is set from 0.1 to 0.9 in 0.1 steps. To optimize the sizing and the ratio of fuel-cell energy in the hybrid power system, we propose the modified version of the univariate dynamic encoding algorithm for searches (uDEAS) as a novel optimization method. The proposed novel approaches can be applied directly to any place to optimize an energy hub system model comprising three power sources, i.e., solar power, fuel cell, and power utility.

Suggested Citation

  • Jong-Wook Kim & Heungju Ahn & Hyeon Cheol Seo & Sang Cheol Lee, 2022. "Optimization of Solar/Fuel Cell Hybrid Energy System Using the Combinatorial Dynamic Encoding Algorithm for Searches (cDEAS)," Energies, MDPI, vol. 15(8), pages 1-15, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2779-:d:790826
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    References listed on IDEAS

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    4. Zeng, Tao & Zhang, Caizhi & Zhang, Yanyi & Deng, Chenghao & Hao, Dong & Zhu, Zhongwen & Ran, Hongxu & Cao, Dongpu, 2021. "Optimization-oriented adaptive equivalent consumption minimization strategy based on short-term demand power prediction for fuel cell hybrid vehicle," Energy, Elsevier, vol. 227(C).
    5. Lee, Sang C. & Kwon, Osung & Thomas, Sobi & Park, Sam & Choi, Gyeung-Ho, 2014. "Graphical and mathematical analysis of fuel cell/battery passive hybridization with K factors," Applied Energy, Elsevier, vol. 114(C), pages 135-145.
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    Cited by:

    1. Asmita Ajay Rathod & Balaji Subramanian, 2022. "Scrutiny of Hybrid Renewable Energy Systems for Control, Power Management, Optimization and Sizing: Challenges and Future Possibilities," Sustainability, MDPI, vol. 14(24), pages 1-35, December.

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