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A multi-objective energy coordinative and management policy for solid oxide fuel cell using triune brain large-scale multi-agent deep deterministic policy gradient

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  • Li, Jiawen

Abstract

In this paper, a data-driven multi-objective energy coordinative management policy is proposed in order to enhance the net output power and efficiency of a solid oxide fuel cell (SOFC) and prevent constraint violations. This study focuses on the optimization agent and controller design for a SOFC power system to maintain stable oxygen excess ratio (OER) and fuel utilization (FU) ratio as well as meet the load demand simultaneously. The optimization agent is responsible for output the reference OER and FU, aiming to achieve maximum net output power and operational efficiency as well as dynamic constraint satisfaction times in terms of OER and FU in real time. By applying reference OER and FU settings, the air and hydrogen flow within the SOFC can be effectively controlled by coordination of the air control agent and hydrogen control agent, respectively. In addition, a triune brain large-scale multi-agent deep deterministic policy gradient algorithm (TBL-MADDPG) is proposed. In order to improve the robustness of the proposed policy, the design of TBL-MADDPG entails curriculum learning, imitation learning and a large-scale multi-agent training framework. The performance of this proposed method is verified by the experiment.

Suggested Citation

  • Li, Jiawen, 2022. "A multi-objective energy coordinative and management policy for solid oxide fuel cell using triune brain large-scale multi-agent deep deterministic policy gradient," Applied Energy, Elsevier, vol. 324(C).
    • Handle: RePEc:eee:appene:v:324:y:2022:i:c:s0306261922006675
    DOI: 10.1016/j.apenergy.2022.119313
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    References listed on IDEAS

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    1. Li, Jiawen & Yu, Tao & Zhang, Xiaoshun & Li, Fusheng & Lin, Dan & Zhu, Hanxin, 2021. "Efficient experience replay based deep deterministic policy gradient for AGC dispatch in integrated energy system," Applied Energy, Elsevier, vol. 285(C).
    2. Nayeripour, Majid & Hoseintabar, Mohammad, 2013. "A new control strategy of solid oxide fuel cell based on coordination between hydrogen fuel flow rate and utilization factor," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 505-514.
    3. Shuxian Li & Minghui Hu & Changchao Gong & Sen Zhan & Datong Qin, 2018. "Energy Management Strategy for Hybrid Electric Vehicle Based on Driving Condition Identification Using KGA-Means," Energies, MDPI, vol. 11(6), pages 1-16, June.
    4. Shen, Peihong & Zhao, Zhiguo & Zhan, Xiaowen & Li, Jingwei & Guo, Qiuyi, 2018. "Optimal energy management strategy for a plug-in hybrid electric commercial vehicle based on velocity prediction," Energy, Elsevier, vol. 155(C), pages 838-852.
    5. Prodromidis, George N. & Coutelieris, Frank A., 2017. "Thermodynamic analysis of biogas fed solid oxide fuel cell power plants," Renewable Energy, Elsevier, vol. 108(C), pages 1-10.
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    Cited by:

    1. Abid, Md. Shadman & Apon, Hasan Jamil & Hossain, Salman & Ahmed, Ashik & Ahshan, Razzaqul & Lipu, M.S. Hossain, 2024. "A novel multi-objective optimization based multi-agent deep reinforcement learning approach for microgrid resources planning," Applied Energy, Elsevier, vol. 353(PA).
    2. Zhang, Gang & Zhou, Su & Gao, Jianhua & Fan, Lei & Lu, Yanda, 2023. "Stacks multi-objective allocation optimization for multi-stack fuel cell systems," Applied Energy, Elsevier, vol. 331(C).

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