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Power Generation Optimization of the Combined Cycle Power-Plant System Comprising Turbo Expander Generator and Trigen in Conjunction with the Reinforcement Learning Technique

Author

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  • Hyoung Tae Kim

    (Department of Innovation Laboratory, Korea Gas Corporation Research Institute, Gyeonggi-do 15328, Korea)

  • Gen Soo Song

    (Department of R&D Center, Kum Young ENG, Daejeon 34051, Korea)

  • Sangwook Han

    (Department of Electrical Information and Control, Dong Seoul University, Gyeonggi-do 13117, Korea)

Abstract

In this paper, a method that utilizes the reinforcement learning (RL) technique is proposed to establish an optimal operation plan to obtain maximum power output from a trigen generator. Trigen is a type of combined heat and power system (CHP) that provides chilling, heating, and power generation, and the turbo expander generator (TEG) is a generator that uses the decompression energy of gas to generate electricity. If the two are combined to form a power source, a power generation system with higher efficiency can be created. However, it is very difficult to control the heat and power generation amount of TEG and trigen according to the flow rate of natural gas that changes every moment. Accordingly, a method is proposed to utilize the RL technique to determine the operation process to attain an even higher efficiency. When the TEG and trigen are configured using the RL technique, the power output can be maximized, and the power output variability can be reduced to obtain high-quality power. When using the RL technique, it was confirmed that the overall efficiency was improved by an additional 3%.

Suggested Citation

  • Hyoung Tae Kim & Gen Soo Song & Sangwook Han, 2020. "Power Generation Optimization of the Combined Cycle Power-Plant System Comprising Turbo Expander Generator and Trigen in Conjunction with the Reinforcement Learning Technique," Sustainability, MDPI, vol. 12(20), pages 1-14, October.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:20:p:8379-:d:426630
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    References listed on IDEAS

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    4. A.G. Olabi & Tabbi Wilberforce & Enas Taha Sayed & Khaled Elsaid & Mohammad Ali Abdelkareem, 2020. "Prospects of Fuel Cell Combined Heat and Power Systems," Energies, MDPI, vol. 13(16), pages 1-20, August.
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    Cited by:

    1. Sangwook Han, 2020. "Control Method of Buses and Lines Using Reinforcement Learning for Short Circuit Current Reduction," Sustainability, MDPI, vol. 12(22), pages 1-18, November.
    2. Il-oh Kang & Hyunseok You & Kyungshik Choi & Sung-kook Jeon & Jaehee Lee & Dongho Lee, 2022. "Modeling and Economic Operation of Energy Hub Considering Energy Market Price and Demand," Sustainability, MDPI, vol. 14(4), pages 1-18, February.

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