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Digital twin driven life-cycle operation optimization for combined cooling heating and power-cold energy recovery (CCHP-CER) system

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  • Huang, Z.F.
  • Soh, K.Y.
  • Islam, M.R.
  • Chua, K.J.

Abstract

Natural gas is expected to be the dominant fossil fuel in the coming decades. Improving the sustainability of natural gas usage is imperative to achieving a low-carbon society. This study proposes a combined cooling, heating, and power incorporating cold energy recovery (CCHP-CER) system to utilize both heat and cold energies of liquified natural gas (LNG) in a cascade way. The system is comprised of four subsystems, namely, gas turbine, water-lithium bromide absorption chiller, hot water heat exchanger, and cold energy recovery unit. A digital twin approach is applied to this system for real-time and life-cycle operational optimization. The cascade forward neural network (CFNN) is employed to construct the virtual representation while a parameter-free intelligent algorithm is adopted to seek the optimal operating parameters. Key results from this study revealed that incorporating the cold energy recovery (CER) unit produces additional electricity and cooling effect, bringing a 0.72 % improvement in the average daily primary energy saving rate (PESR). The digital twin-based optimization process updates the optimal operation parameters in time when the system suffers degradation. Consequently, the degradation performance is alleviated by the living parameters. Compared to static model-based optimization, the digital twin-based optimization improves the daily PESR by 2.23 %, 0.35 %, and 1.53 % during respective winter, summer, and transition days, particularly when the compressor and turbine of the gas turbine suffer degraded efficiency of −2 %.

Suggested Citation

  • Huang, Z.F. & Soh, K.Y. & Islam, M.R. & Chua, K.J., 2022. "Digital twin driven life-cycle operation optimization for combined cooling heating and power-cold energy recovery (CCHP-CER) system," Applied Energy, Elsevier, vol. 324(C).
    • Handle: RePEc:eee:appene:v:324:y:2022:i:c:s0306261922010546
    DOI: 10.1016/j.apenergy.2022.119774
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    References listed on IDEAS

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    1. Huang, Z.F. & Wan, Y.D. & Soh, K.Y. & Islam, M.R. & Chua, K.J., 2022. "Off-design and flexibility analyses of combined cooling and power based liquified natural gas (LNG) cold energy utilization system under fluctuating regasification rates," Applied Energy, Elsevier, vol. 310(C).
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    7. Li, Yongyi & Liu, Yujia & Zhang, Guoqiang & Yang, Yongping, 2020. "Thermodynamic analysis of a novel combined cooling and power system utilizing liquefied natural gas (LNG) cryogenic energy and low-temperature waste heat," Energy, Elsevier, vol. 199(C).
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    Cited by:

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    2. Hua, Weiqi & Stephen, Bruce & Wallom, David C.H., 2023. "Digital twin based reinforcement learning for extracting network structures and load patterns in planning and operation of distribution systems," Applied Energy, Elsevier, vol. 342(C).

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