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Long-term electricity demand forecasting under low-carbon energy transition: Based on the bidirectional feedback between power demand and generation mix

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  • Jin, Haowei
  • Guo, Jue
  • Tang, Lei
  • Du, Pei

Abstract

Predicting electricity demand is crucial for ensuring energy security. However, the low-carbon energy transition has brought a new bidirectional feedback between power demand and generation mix, which may make the previous electricity demand forecasting methods no longer applicable. This study combines the system dynamics (SDs) model and power generation mix planning (PGMP) model to construct a hybrid forecasting method. It has smaller prediction error than other models, and can simultaneously predict the collaborative evolution of electricity demand, power generation mix and carbon emissions. Besides, the long-term planning of thermal power generators may not ensure power system's secure operation, the PGMP model is utilized to modify the Markov chain state transition matrix, which can predict future power generation mix and provide stable electricity supply. The prediction results for China show that under low and high emission reduction scenarios, the carbon dioxide emissions will peak in 2030 (12.228 billion tons and 11.741 billion tons), and the electricity demand will reach 12.63 trillion kWh and 12.76 trillion kWh in 2035. The installed proportion of thermal power generators will not reduce quickly, but gradually converted into backup generators. What's more, the results can also provide policy reference for accelerating low-carbon energy transition.

Suggested Citation

  • Jin, Haowei & Guo, Jue & Tang, Lei & Du, Pei, 2024. "Long-term electricity demand forecasting under low-carbon energy transition: Based on the bidirectional feedback between power demand and generation mix," Energy, Elsevier, vol. 286(C).
    • Handle: RePEc:eee:energy:v:286:y:2024:i:c:s0360544223028293
    DOI: 10.1016/j.energy.2023.129435
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    1. Li, Xuetao & Wang, Ziwei & Yang, Chengying & Bozkurt, Ayhan, 2024. "An advanced framework for net electricity consumption prediction: Incorporating novel machine learning models and optimization algorithms," Energy, Elsevier, vol. 296(C).

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