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A data-driven output voltage control of solid oxide fuel cell using multi-agent deep reinforcement learning

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  • Li, Jiawen
  • Yu, Tao
  • Yang, Bo

Abstract

To effectively control the output voltage of solid oxide fuel cells (SOFCs) and improve the operating efficiency of SOFC systems, an SOFC output voltage data-driven controller based on multi-agent large-scale deep reinforcement learning is proposed, whereby a discrete–continuous hybrid action space large-scale multi-agent twin delayed deep deterministic policy gradient (DHASL-MATD3) is used as the control algorithm for this controller. To solve the low robustness problem of deep reinforcement learning-based conventional controllers, this algorithm adopts a hybrid action space multi-agent policy that achieves parallel exploration by using double deep Q-learning (DDQN) agents with discrete space and deep deterministic policy gradient (DDPG) agents with continuous action space, thus improving exploration efficiency and realizing excellent robustness. In addition, many techniques are adopted by this algorithm to solve the problem of Q-value overestimation. Ultimately, an SOFC output voltage controller with stronger robustness is obtained. Simulation results show that this controller can effectively control the output voltage of a SOFC by regulating the fuel flux and maintaining its fuel utilization within a reasonable range.

Suggested Citation

  • Li, Jiawen & Yu, Tao & Yang, Bo, 2021. "A data-driven output voltage control of solid oxide fuel cell using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 304(C).
    • Handle: RePEc:eee:appene:v:304:y:2021:i:c:s0306261921009193
    DOI: 10.1016/j.apenergy.2021.117541
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    References listed on IDEAS

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    Cited by:

    1. Li, Jiawen & Yu, Tao & Zhang, Xiaoshun, 2022. "Coordinated load frequency control of multi-area integrated energy system using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 306(PA).
    2. Jie, Hao & Liao, Jiawei & Zhu, Guozhu & Hong, Weirong, 2024. "Nonlinear model predictive control of direct internal reforming solid oxide fuel cells via PDAE-constrained dynamic optimization," Applied Energy, Elsevier, vol. 360(C).
    3. Mazare, Mahmood, 2024. "Adaptive optimal secure wind power generation control for variable speed wind turbine systems via reinforcement learning," Applied Energy, Elsevier, vol. 353(PA).
    4. Zhang, Bin & Hu, Weihao & Ghias, Amer M.Y.M. & Xu, Xiao & Chen, Zhe, 2022. "Multi-agent deep reinforcement learning-based coordination control for grid-aware multi-buildings," Applied Energy, Elsevier, vol. 328(C).
    5. Zhu, Dafeng & Yang, Bo & Liu, Yuxiang & Wang, Zhaojian & Ma, Kai & Guan, Xinping, 2022. "Energy management based on multi-agent deep reinforcement learning for a multi-energy industrial park," Applied Energy, Elsevier, vol. 311(C).
    6. Yin, Linfei & Li, Yu, 2022. "Hybrid multi-agent emotional deep Q network for generation control of multi-area integrated energy systems," Applied Energy, Elsevier, vol. 324(C).
    7. Homod, Raad Z. & Togun, Hussein & Kadhim Hussein, Ahmed & Noraldeen Al-Mousawi, Fadhel & Yaseen, Zaher Mundher & Al-Kouz, Wael & Abd, Haider J. & Alawi, Omer A. & Goodarzi, Marjan & Hussein, Omar A., 2022. "Dynamics analysis of a novel hybrid deep clustering for unsupervised learning by reinforcement of multi-agent to energy saving in intelligent buildings," Applied Energy, Elsevier, vol. 313(C).

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