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Evaluating the carbon-emission reduction potential of employing low-carbon demand response to guide electric-vehicle charging: A Chinese case study

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  • Du, Bojun
  • Jia, Hongyang
  • Zhang, Bochao
  • Li, Yaowang
  • Wang, Han
  • Yan, Jie
  • Du, Ershun
  • Zhang, Ning

Abstract

The rapid growth of electric vehicles (EVs) has resulted in considerable indirect carbon emissions from power systems. To reduce charging emissions, EVs should be charged strategically to consume generated renewable energy electricity. To achieve this goal, a low-carbon demand response model is proposed to stimulate multi-type EV users to consume more low-carbon electricity by guiding users to adjust their charging behaviour temporally and spatially. First, the charging flexibility of EVs is modelled considering different user behaviours and charging modes. To accurately guide the EV demand response, a method is proposed to calculate future carbon emission factors with high temporal and spatial resolutions. Considering the carbon emission factors, charging flexibility is integrated into a model for a low-carbon demand response. This model holistically optimizes fast and slow charging of EVs from the user’s perspective. Case studies are carried out using real-world data and future planning results for Changzhou and China. The results indicate that EVs produce 73 % less carbon emissions during driving than traditional gasoline vehicles. Using low-carbon demand response to guide EV charging could reduce carbon emissions by up to 15 % by 2035.

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  • Du, Bojun & Jia, Hongyang & Zhang, Bochao & Li, Yaowang & Wang, Han & Yan, Jie & Du, Ershun & Zhang, Ning, 2025. "Evaluating the carbon-emission reduction potential of employing low-carbon demand response to guide electric-vehicle charging: A Chinese case study," Applied Energy, Elsevier, vol. 397(C).
    • Handle: RePEc:eee:appene:v:397:y:2025:i:c:s0306261925009262
    DOI: 10.1016/j.apenergy.2025.126196
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    References listed on IDEAS

    as
    1. Liu, Ke & Liu, Yanli, 2023. "Stochastic user equilibrium based spatial-temporal distribution prediction of electric vehicle charging load," Applied Energy, Elsevier, vol. 339(C).
    2. Chen, Jiahui & Wang, Fang & He, Xiaoyi & Liang, Xinyu & Huang, Junling & Zhang, Shaojun & Wu, Ye, 2022. "Emission mitigation potential from coordinated charging schemes for future private electric vehicles," Applied Energy, Elsevier, vol. 308(C).
    3. Lin, Haiyang & Fu, Kun & Wang, Yu & Sun, Qie & Li, Hailong & Hu, Yukun & Sun, Bo & Wennersten, Ronald, 2019. "Characteristics of electric vehicle charging demand at multiple types of location - Application of an agent-based trip chain model," Energy, Elsevier, vol. 188(C).
    4. Yao, Zhaosheng & Wang, Zhiyuan & Ran, Lun, 2023. "Smart charging and discharging of electric vehicles based on multi-objective robust optimization in smart cities," Applied Energy, Elsevier, vol. 343(C).
    5. Zhang, Jing & Yan, Jie & Liu, Yongqian & Zhang, Haoran & Lv, Guoliang, 2020. "Daily electric vehicle charging load profiles considering demographics of vehicle users," Applied Energy, Elsevier, vol. 274(C).
    6. Ma, Siyu & Liu, Hui & Wang, Ni & Huang, Lidong & Su, Jinshuo & Zhao, Teyang, 2024. "Incentive-based integrated demand response with multi-energy time-varying carbon emission factors," Applied Energy, Elsevier, vol. 359(C).
    7. Li, Xiaohui & Wang, Zhenpo & Zhang, Lei & Sun, Fengchun & Cui, Dingsong & Hecht, Christopher & Figgener, Jan & Sauer, Dirk Uwe, 2023. "Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview," Energy, Elsevier, vol. 268(C).
    8. Shao, Changzheng & Ding, Yi & Wang, Jianhui, 2019. "A low-carbon economic dispatch model incorporated with consumption-side emission penalty scheme," Applied Energy, Elsevier, vol. 238(C), pages 1084-1092.
    9. Zhang, Xizheng & Wang, Zeyu & Lu, Zhangyu, 2022. "Multi-objective load dispatch for microgrid with electric vehicles using modified gravitational search and particle swarm optimization algorithm," Applied Energy, Elsevier, vol. 306(PA).
    10. Zhenyu Zhuo & Ershun Du & Ning Zhang & Chris P. Nielsen & Xi Lu & Jinyu Xiao & Jiawei Wu & Chongqing Kang, 2022. "Cost increase in the electricity supply to achieve carbon neutrality in China," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    11. Kang, Zixuan & Ye, Zhongnan & Lam, Chor-Man & Hsu, Shu-Chien, 2023. "Sustainable electric vehicle charging coordination: Balancing CO2 emission reduction and peak power demand shaving," Applied Energy, Elsevier, vol. 349(C).
    12. Wang, Meng & Yu, Hang & Yang, Yikun & Lin, Xiaoyu & Guo, Haijin & Li, Chaoen & Zhou, Yue & Jing, Rui, 2021. "Unlocking emerging impacts of carbon tax on integrated energy systems through supply and demand co-optimization," Applied Energy, Elsevier, vol. 302(C).
    13. Dixon, James & Bukhsh, Waqquas & Edmunds, Calum & Bell, Keith, 2020. "Scheduling electric vehicle charging to minimise carbon emissions and wind curtailment," Renewable Energy, Elsevier, vol. 161(C), pages 1072-1091.
    14. Aghajan-Eshkevari, Saleh & Ameli, Mohammad Taghi & Azad, Sasan, 2023. "Optimal routing and power management of electric vehicles in coupled power distribution and transportation systems," Applied Energy, Elsevier, vol. 341(C).
    15. Shaukat, N. & Khan, B. & Ali, S.M. & Mehmood, C.A. & Khan, J. & Farid, U. & Majid, M. & Anwar, S.M. & Jawad, M. & Ullah, Z., 2018. "A survey on electric vehicle transportation within smart grid system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1329-1349.
    16. Xiang, Yue & Jiang, Zhuozhen & Gu, Chenghong & Teng, Fei & Wei, Xiangyu & Wang, Yang, 2019. "Electric vehicle charging in smart grid: A spatial-temporal simulation method," Energy, Elsevier, vol. 189(C).
    17. Zhong, Xiaoqing & Zhong, Weifeng & Liu, Yi & Yang, Chao & Xie, Shengli, 2022. "Cooperative operation of battery swapping stations and charging stations with electricity and carbon trading," Energy, Elsevier, vol. 254(PA).
    18. Das, H.S. & Rahman, M.M. & Li, S. & Tan, C.W., 2020. "Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    19. Nie, Qingyun & Zhang, Lihui & Tong, Zihao & Dai, Guyu & Chai, Jianxue, 2022. "Cost compensation method for PEVs participating in dynamic economic dispatch based on carbon trading mechanism," Energy, Elsevier, vol. 239(PA).
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