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Robust optimal model for rural integrated energy system incorporating biomass waste utilization and power-to-gas coupling unit considering deep learning-based air conditioning load personalized demand response

Author

Listed:
  • Zhu, Zhenle
  • Qu, Zhiguo
  • Gong, Jianqiang
  • Li, Jianjun
  • Xu, Hongtao

Abstract

To realize the low-carbon use of energy and reduce the pressure of energy supply. Based on the characteristics of abundant renewable resources in rural region, considering deep learning-based air conditioning load-personalized demand response (DL-AC-PDR), a robust optimal model for rural integrated energy system (RIES) is designed incorporating biomass waste utilization (BWU)- power to gas (P2G) coupling. Firstly, the study develops a biogas digester (BD) analytical model tailored to system optimization timescales. Subsequently, BD and biogas two-stage membrane purification (BTP) form BWU, and BWU-P2G coupling to alleviate the problem of economic carbon emission contradiction. After that, to alleviate the pressure on the peak power supply, based on deep learning, an air conditioning load-reducing mechanism considering personnel type is proposed. The case studies show: (1) The BD analytical model is improved to make it less complex to solve coupled with other units. (2) The RIES structure was improved by Carbon Capture (CC), Carbon Sequestration (CS) and BWU-P2G coupling units, which reduced the operating cost and carbon emission by 68.49 % and 19.37 %, respectively. (3) The implementation of DL-AC-PDR can reduce peak electrical load by 1.57 MW, and the operating cost and carbon emission of RIES are reduced by 0.36 % and 2.40 %, respectively.

Suggested Citation

  • Zhu, Zhenle & Qu, Zhiguo & Gong, Jianqiang & Li, Jianjun & Xu, Hongtao, 2025. "Robust optimal model for rural integrated energy system incorporating biomass waste utilization and power-to-gas coupling unit considering deep learning-based air conditioning load personalized demand," Energy, Elsevier, vol. 321(C).
    • Handle: RePEc:eee:energy:v:321:y:2025:i:c:s0360544225011260
    DOI: 10.1016/j.energy.2025.135484
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