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

Haowei Jin, Jue Guo, Lei Tang and Pei Du

Energy, 2024, vol. 286, issue C

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feedback between power demand and generation mix, which may make the previous electricity demand forecasting methods no longer applicable.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.

Keywords: Electricity demand forecasting; Low-carbon energy transition; Bidirectional feedback; System dynamics; Power generation mix planning (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544223028293
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:286:y:2024:i:c:s0360544223028293

DOI: 10.1016/j.energy.2023.129435

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().