Large-scale multi-agent reinforcement learning-based method for coordinated output voltage control of solid oxide fuel cell

Authors：Li, Jiawen; Li, Yaping; Yu, Tao^*; Yang, Bo

Source：Case Studies in Thermal Engineering, 2022, 30: 101752.

DOI：10.1016/j.csite.2021.101752

• Large-scale deep reinforcement learning
• Pygmalion effect-based multi-agent double delay deep deterministic policy gradient algorithm (PEB-MA4DPG)
• DC/DC converter
• Fuel reformer
• Output voltage coordinated control
• Solid oxide fuel cell (SOFC)

For a solid oxide fuel cell (SOFC) power system containing a hydrogen fuel reformer and a DC-DC converter, it is necessary to coordinate the controllers of the two devices in order to maintain effective power tracking control and also prevent constraint violations of fuel utilization. A data driven SOFC output voltage coordinated control method is proposed for maintaining a stable fuel utilization whilst satisfying load demand requirements in this paper. To that end, a Pygmalion effect-based multi-agent double delay deep deterministic policy gradient algorithm (PEB-MA4DPG) is presented in this work. This algorithm is a combination of a comprehensive exploration, imitation learning and curriculum learning policy, which altogether constitute a coordinated strategy of high robustness. By employing the controllers for the fuel reformer and DC-DC converter as the two agents, the proposed algorithm generates an optimal coordinated policy via centralized training and distributed implementation. The experimental verify the effectiveness of the proposed method.