Optimizing electric vehicle charging patterns and infrastructure for grid decarbonization

Electric vehicles hold decarbonization potential. However, their uncoordinated charging patterns and suboptimal station deployment risk destabilizing power grids. Here we investigate how to enhance grid stability and reduce emissions from mobility-charging-grid perspectives by optimizing charging demand and infrastructure. We present a flexible scheduling strategy to redistribute peak load demand based on spatiotemporal charging and mobility behavior extracted from real-world data spanning 2018 through 2024 in Shanghai, China. Considering projected electric vehicles adoption and population growth, we propose a comprehensive charging station deployment strategy to balance demand and supply, and predict the extra power dispatching emissions caused by charging demand. Results show that by 2035, the strategy could reduce 46.06 thousand tons of cumulative carbon dioxide emissions citywide in household travel. Our findings highlight the importance of integrating charging demand, charging stations, and grid for achieving decarbonization, providing actionable insights for sustainable urban mobility and electrification strategies in large cities. Optimized electric vehicle charging strategy and infrastructure planning can enhance grid stability and reduce emissions from mobility-charging-grid perspectives based on large scale practical driving data obtained from the Megacity of China- Shanghai.

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Publication Details

Journal
Communications Sustainability
Published
2026-03-07
DOI
https://doi.org/10.1038/s44458-026-00037-7
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
0.00

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article

Optimizing electric vehicle charging patterns and infrastructure for grid decarbonization

Jihao Deng, Xi Chen, C. Liao, Quan Yuan
Communications Sustainability
Electric Vehicles and Infrastructure
article

Optimizing electric vehicle charging patterns and infrastructure for grid decarbonization

Jihao Deng, Xi Chen, C. Liao, Quan Yuan
article en

Abstract

Electric vehicles hold decarbonization potential. However, their uncoordinated charging patterns and suboptimal station deployment risk destabilizing power grids. Here we investigate how to enhance grid stability and reduce emissions from mobility-charging-grid perspectives by optimizing charging demand and infrastructure. We present a flexible scheduling strategy to redistribute peak load demand based on spatiotemporal charging and mobility behavior extracted from real-world data spanning 2018 through 2024 in Shanghai, China. Considering projected electric vehicles adoption and population growth, we propose a comprehensive charging station deployment strategy to balance demand and supply, and predict the extra power dispatching emissions caused by charging demand. Results show that by 2035, the strategy could reduce 46.06 thousand tons of cumulative carbon dioxide emissions citywide in household travel. Our findings highlight the importance of integrating charging demand, charging stations, and grid for achieving decarbonization, providing actionable insights for sustainable urban mobility and electrification strategies in large cities. Optimized electric vehicle charging strategy and infrastructure planning can enhance grid stability and reduce emissions from mobility-charging-grid perspectives based on large scale practical driving data obtained from the Megacity of China- Shanghai.

Communications SustainabilityVol. 1(1)
Tongji University (CN), University of Toronto (CA), Zhejiang Provincial Institute of Communications Planning,Design & Research (CN), Zhejiang University (CN)
National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities, Natural Science Foundation of Zhejiang Province
Sustainable cities and communities
Openalex Percentile: Top 30%
Electric Vehicles and Infrastructure
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Optimizing electric vehicle charging patterns and infrastructure for grid decarbonization — Jihao Deng, Xi Chen, et al. · Communications Sustainability (2026) | TGRS Research Map | TGRS