A planning model for coal power exit scales based on minimizing idle and shortage losses: A case study of China

Delu Wang, Jinqi Mao, Xunpeng Shi, Chunxiao Li and Fan Chen

Energy Economics, 2024, vol. 138, issue C

Abstract: Owing to the rising energy demand and coal dependency in developing countries, the losses incurred from coal power exit are substantial, leading to a low willingness to exit and a high risk of tragedy of the commons. Concurrently, the normalization of peak loads and increasing penetration of renewable energy have exacerbated the contradiction between the overall electricity surplus and peak power shortages in these countries. In response to these challenges, we propose an optimal planning model for a coal power exit scale based on the minimization of losses from idle coal power and industrial power shortages. The main findings are presented as follows: First, compared with the coal power exit models guided by the energy trilemma, our model can reduce losses (the primary demand in developing countries) by at least 22 %, and, in some scenarios, it can increase the exit scale by up to 147 GW. Second, unlike existing methods that measure the overall impact of power shortages, our approach is better at capturing industries' production loss characteristics, which is important for developing countries (positioned in the production segment of the industrial chain), including the evolution patterns of resource and time losses, recovery trajectories of power interruptions, and main influencing factors. Third, China's optimal coal power exit scale for 2030 fluctuates between −7 and 282 GW, with total losses between 48.34 and 188.86 billion yuan. This study provides quantitative tools and decision-making references for optimizing coal power exit and power use.

Keywords: Coal power exit; Power shortage losses; Idle coal power losses; Optimal scales (search for similar items in EconPapers)
JEL-codes: Q41 Q43 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:138:y:2024:i:c:s0140988324005723

DOI: 10.1016/j.eneco.2024.107864

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