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Control strategy for a CO2-based combined cooling and power generation system based on heat source and cold sink fluctuations

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  • He, Jintao
  • Shi, Lingfeng
  • Tian, Hua
  • Wang, Xuan
  • Zhang, Yonghao
  • Zhang, Meiyan
  • Yao, Yu
  • Cai, Jinwen
  • Shu, Gequn

Abstract

The CO2-based combined cooling and power system (CCP) is considered a promising solution for waste heat recovery due to its environmental protection and excellent performance. When it comes to refrigerated trucks' application, there are still critical challenges arising from the transient fluctuations of cold and heat sources. According to the poor correlation between the power sub-cycle and the refrigeration sub-cycle of the CCP system, this paper proposes various controls such as constant control and optimal control to control each cycle separately and achieve stable and optimized operation. The results show that the optimal pump speed and the split valve to limit exhaust control is an effective strategy for engine heat source fluctuations. Under the engine heat source slow step condition, the net work under this control is improved by 5%, and under the engine heat source transient condition, the net work under this control is only improved by 1.37%. In the case of cold sink fluctuations, the strategy of controlling the condensing pressure can reduce the maximum fluctuation of the cooling capacity from 5% under open-loop conditions to 2.5% under control. The above control methods can make the operation of the CCP system more stable and effective.

Suggested Citation

  • He, Jintao & Shi, Lingfeng & Tian, Hua & Wang, Xuan & Zhang, Yonghao & Zhang, Meiyan & Yao, Yu & Cai, Jinwen & Shu, Gequn, 2022. "Control strategy for a CO2-based combined cooling and power generation system based on heat source and cold sink fluctuations," Energy, Elsevier, vol. 257(C).
    • Handle: RePEc:eee:energy:v:257:y:2022:i:c:s036054422201619x
    DOI: 10.1016/j.energy.2022.124716
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    References listed on IDEAS

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    1. Lu, Bowen & Zhang, Zhifu & Cai, Jinwen & Wang, Wei & Ju, Xueming & Xu, Yao & Lu, Xun & Tian, Hua & Shi, Lingfeng & Shu, Gequn, 2023. "Integrating engine thermal management into waste heat recovery under steady-state design and dynamic off-design conditions," Energy, Elsevier, vol. 272(C).
    2. Hu, Yuankang & Deng, Zeyu & Yang, Jiaming & Hu, Yilun & Zhong, Kaifeng & Xie, Yubao & Ou, Zhihua & Guo, Shuting & Li, Xiaoning, 2024. "Performance analysis of a novel multimode electricity-cooling cogeneration system (ECCS) driven by exhaust from a marine engine," Energy, Elsevier, vol. 300(C).
    3. He, Jintao & Shi, Lingfeng & Tian, Hua & Wang, Xuan & Sun, Xiaocun & Zhang, Meiyan & Yao, Yu & Shu, Gequn, 2023. "Applying artificial neural network to approximate and predict the transient dynamic behavior of CO2 combined cooling and power cycle," Energy, Elsevier, vol. 285(C).
    4. Czyzewski, Pawel & Slefarski, Rafal & Golebiewski, Michal & Alnajideen, Mohammad & Valera-Medina, Agustin, 2024. "Experimental study of CO2/H2/NH3 influence on CH4 flameless combustion process in semi-industrial furnace," Energy, Elsevier, vol. 296(C).

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