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Investigation and optimization of PEMFC-CHP systems based on Chinese residential thermal and electrical consumption data

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  • Lyu, Xingbao
  • Yuan, Yi
  • Ning, Wenjing
  • Chen, Li
  • Tao, Wen-Quan

Abstract

The proton exchange membrane fuel cell based combined heat and power (PEMFC-CHP) system can recover the waste heat and obtain high energy efficiency. In the present study, a residential PEMFC-CHP system is modeled and optimized. To obtain reliable thermal and electrical consumption data of Chinese residence a questionnaire is designed based on the bottom-up approach which considers the effects of family size, seasons as well as weekends and weekdays. With the data as the input, effects of key parameters including tank setting temperature and volume, PEMFC unit number and operating modes on the CHP efficiency and matching degree of the PEMFC-CHP system are investigated for different family sizes in different seasons. The results show that big family has higher energy consumption, especially thermal consumption, leading to higher CHP efficiency but lower matching degree. Increasing the tank setting temperature and volume results in higher CHP efficiency, but a too big tank also generates low matching degree. Increasing the PEMFC unit number can get higher electric efficiency but lower matching degree. Compared to electrical-following mode, constant load operating mode can achieve higher CHP efficiency and matching degree. Besides, a genetic algorithm is coupled with ensemble learning model to optimize the PEMFC-CHP system, leading to improvement of system performance, with 96.91% (96.01%) of CHP efficiency and 99.49% (99.60%) of matching degree for big (small) family. The present study provides reliable thermal and electrical consumption data of Chinese residence and is helpful for the design of residential PEMFC-CHP system.

Suggested Citation

  • Lyu, Xingbao & Yuan, Yi & Ning, Wenjing & Chen, Li & Tao, Wen-Quan, 2024. "Investigation and optimization of PEMFC-CHP systems based on Chinese residential thermal and electrical consumption data," Applied Energy, Elsevier, vol. 356(C).
    • Handle: RePEc:eee:appene:v:356:y:2024:i:c:s0306261923017014
    DOI: 10.1016/j.apenergy.2023.122337
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    References listed on IDEAS

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    1. Zhao, Junjie & Luo, Xiaobing & Tu, Zhengkai & Hwa Chan, Siew, 2023. "A novel CCHP system based on a closed PEMEC-PEMFC loop with water self-supply," Applied Energy, Elsevier, vol. 338(C).
    2. Bentzen, Jan & Engsted, Tom, 2001. "A revival of the autoregressive distributed lag model in estimating energy demand relationships," Energy, Elsevier, vol. 26(1), pages 45-55.
    3. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    4. Andersen, F.M. & Gunkel, P.A. & Jacobsen, H.K. & Kitzing, L., 2021. "Residential electricity consumption and household characteristics: An econometric analysis of Danish smart-meter data," Energy Economics, Elsevier, vol. 100(C).
    5. Bai, Fan & Quan, Hong-Bing & Yin, Ren-Jie & Zhang, Zhuo & Jin, Shu-Qi & He, Pu & Mu, Yu-Tong & Gong, Xiao-Ming & Tao, Wen-Quan, 2022. "Three-dimensional multi-field digital twin technology for proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 324(C).
    6. Haghighat Mamaghani, Alireza & Najafi, Behzad & Casalegno, Andrea & Rinaldi, Fabio, 2017. "Predictive modelling and adaptive long-term performance optimization of an HT-PEM fuel cell based micro combined heat and power (CHP) plant," Applied Energy, Elsevier, vol. 192(C), pages 519-529.
    7. Oh, Si-Doek & Kim, Ki-Young & Oh, Shuk-Bum & Kwak, Ho-Young, 2012. "Optimal operation of a 1-kW PEMFC-based CHP system for residential applications," Applied Energy, Elsevier, vol. 95(C), pages 93-101.
    8. Cao, Jing & Ho, Mun Sing & Li, Yating & Newell, Richard G. & Pizer, William A., 2019. "Chinese residential electricity consumption: Estimation and forecast using micro-data," Resource and Energy Economics, Elsevier, vol. 56(C), pages 6-27.
    9. Yuan, Yi & Chen, Li & Lyu, Xingbao & Ning, Wenjing & Liu, Wenqi & Tao, Wen-Quan, 2024. "Modeling and optimization of a residential PEMFC-based CHP system under different operating modes," Applied Energy, Elsevier, vol. 353(PA).
    10. Li, Dacheng & Guo, Songshan & He, Wei & King, Marcus & Wang, Jihong, 2021. "Combined capacity and operation optimisation of lithium-ion battery energy storage working with a combined heat and power system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
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