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Efficient Operation of the Hybrid Power System Using an Optimal Fueling Strategy and Control of the Fuel Cell Power Based on the Required Power Tracking Algorithm

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  • Nicu Bizon

    (Faculty of Electronics, Communication and Computers, University of Pitesti, 110040 Pitesti, Romania
    ICSI Energy, National Research and Development Institute for Cryogenic and Isotopic Technologies, 240050 Ramnicu Valcea, Romania
    Doctoral School, Polytehnic University of Bucharest, 313 Splaiul Independentei, 060042 Bucharest, Romania)

  • Phatiphat Thounthong

    (Renewable Energy Research Centre (RERC), Department of Teacher Training in Electrical Engineering, Faculty of Technical Education, King Mongkut’s University of Technology North Bangkok, 1518, Pracharat 1 Road, Bangsue, Bangkok 10800, Thailand
    Groupe de Recherche en Energie Electrique de Nancy (GREEN), Université de Lorraine, GREEN, F-54000 Nancy, France)

  • Damien Guilbert

    (Groupe de Recherche en Energie Electrique de Nancy (GREEN), Université de Lorraine, GREEN, F-54000 Nancy, France)

Abstract

In this paper, four fuel economy strategies using power tracking control of the fuel cell boost converter and fuel cell optimization through the control of the fueling regulators were analyzed. The performance and safe operation in conditions of load disturbances and variations of renewable energy were considered. A benchmark strategy was used as a well-known strategy, which was based on the static feed-forward control of the fueling regulators. One of the four strategies is new and was based on switching the optimization reference to air and fuel regulators based on a threshold of the required power from the fuel cell system. The advantages of using the power tracking control and the optimization based on two variables instead of one are highlighted in sizing the battery capacity and its lifetime, and obtaining fuel economy respectively. The percentages of fuel economy for the analyzed strategies compared to the reference strategy are between 2.83% and 4.36%, and between 7.69% and 12.94%, in the case of a dynamic load cycle with an average of 5 kW and 2.5 kW, respectively.

Suggested Citation

  • Nicu Bizon & Phatiphat Thounthong & Damien Guilbert, 2020. "Efficient Operation of the Hybrid Power System Using an Optimal Fueling Strategy and Control of the Fuel Cell Power Based on the Required Power Tracking Algorithm," Sustainability, MDPI, vol. 12(22), pages 1-26, November.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:22:p:9690-:d:448283
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    References listed on IDEAS

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    1. Ramos-Paja, Carlos Andrés & Spagnuolo, Giovanni & Petrone, Giovanni & Mamarelis, Emilio, 2014. "A perturbation strategy for fuel consumption minimization in polymer electrolyte membrane fuel cells: Analysis, Design and FPGA implementation," Applied Energy, Elsevier, vol. 119(C), pages 21-32.
    2. Yu, Dongmin & Zhu, Haoming & Han, Wenqi & Holburn, Daniel, 2019. "Dynamic multi agent-based management and load frequency control of PV/Fuel cell/ wind turbine/ CHP in autonomous microgrid system," Energy, Elsevier, vol. 173(C), pages 554-568.
    3. Burmester, Daniel & Rayudu, Ramesh & Seah, Winston & Akinyele, Daniel, 2017. "A review of nanogrid topologies and technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 760-775.
    4. San, Guocheng & Zhang, Wenlin & Guo, Xiaoqiang & Hua, Changchun & Xin, Huanhai & Blaabjerg, Frede, 2020. "Large-disturbance stability for power-converter-dominated microgrid: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    5. Zia, Muhammad Fahad & Elbouchikhi, Elhoussin & Benbouzid, Mohamed, 2018. "Microgrids energy management systems: A critical review on methods, solutions, and prospects," Applied Energy, Elsevier, vol. 222(C), pages 1033-1055.
    6. Yamashita, Daniela Yassuda & Vechiu, Ionel & Gaubert, Jean-Paul, 2020. "A review of hierarchical control for building microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
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    1. Yuansheng Li & Bo Cheng & Kaifu Zhang & Xiao Li & Shengzhao Pang & Zhaoyong Mao, 2023. "Adaptive Hamiltonian-Based Energy Control with Built-In Integrator for PEMFC Hybrid Power Conversion Architecture," Energies, MDPI, vol. 16(23), pages 1-11, November.

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