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Multi-objective intelligent control strategy for SOEC steam inlet system based on deep reinforcement learning
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Xin WU1, Minghui ZHENG1, Xingyu XIONG2, 3, Zhiyong MA1, Ruiyun ZHANG4
Thermal Power Generation | 2026, 55(5) : 13 - 20
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Thermal Power Generation | 2026, 55(5): 13-20
• Energy storage and renewable energy technology •
Multi-objective intelligent control strategy for SOEC steam inlet system based on deep reinforcement learning
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Xin WU1, Minghui ZHENG1, Xingyu XIONG2, 3, Zhiyong MA1, Ruiyun ZHANG4
Affiliations
  • 1.School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China
  • 2.School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
  • 3.Shanxi Research Institute of Huairou Laboratory, Taiyuan 030032, China
  • 4.Huaneng Clean Energy Research Institute Co., Ltd., Beijing 102209, China
Published: 2026-05-25 doi: 10.19666/j.rlfd.202505095
Outline
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[Objective]

The stability of the steam inlet system in solid oxide electrolysis cell (SOEC) systems is crucial for enhancing the electrolysis efficiency of the electrolytic stack and prolonging its service life. However, the nonlinear coupling between steam pressure and flow rate imposes high demands on the control strategy.

[Methods]

An experimental platform for the steam inlet system tailored for the 50 kW-class SOEC system was established to investigate the control of steam flow rate error and pressure fluctuation. Based on the collected operation data of the experimental platform, a double-delay deep deterministic policy gradient agent was trained. A multi-objective intelligent control (IC) strategy based on deep reinforcement learning was proposed, aiming to achieve the control goals of the system output flow error not exceeding 3% and pressure fluctuation not exceeding 1 kPa.

[Results]

The experimental results show that the maximum error of the output steam flow rate under the IC method is 1.4%, and the maximum pressure fluctuation is ±0.67 kPa. While under the PID control method, the maximum error of the steady-state output flow rate of the system is 3.8%, and the maximum fluctuation of the pressure is ±1.25 kPa. Compared with PID control, the IC method reduces the maximum flow rate error by 63.2% and the pressure fluctuation by 46.4%.

[Conclusion]

The proposed IC method demonstrates significantly superior control performance compared to the PID method.

solid oxide electrolysis cell  /  steam inlet system  /  deep reinforcement learning  /  multi-objective control
Xin WU, Minghui ZHENG, Xingyu XIONG, Zhiyong MA, Ruiyun ZHANG. Multi-objective intelligent control strategy for SOEC steam inlet system based on deep reinforcement learning[J]. Thermal Power Generation, 2026 , 55 (5) : 13 -20 . DOI: 10.19666/j.rlfd.202505095
Funding
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  • Young Scholars Development Program(2023SY3006)
  • National Key Research and Development Program(2017YFB0601900)
Appendix
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Year 2026 volume 55 Issue 5
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Article Info
doi: 10.19666/j.rlfd.202505095
  • Receive Date：2025-05-11
  • Online Date：2026-08-14
  • Published：2026-05-25
Article Data
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History
  • Received：2025-05-11
  • Revised：2025-06-27
  • Accepted：2025-07-01
Funding
Young Scholars Development Program(2023SY3006)
National Key Research and Development Program(2017YFB0601900)
Affiliations
    1.School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China
    2.School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
    3.Shanxi Research Institute of Huairou Laboratory, Taiyuan 030032, China
    4.Huaneng Clean Energy Research Institute Co., Ltd., Beijing 102209, China
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https://castjournals.cast.org.cn/joweb/rlfd/EN/10.19666/j.rlfd.202505095
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多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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