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Electric vehicle charging optimization to minimize marginal greenhouse gas emissions from power generation

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  • Tu, Ran
  • Gai, Yijun (Jessie)
  • Farooq, Bilal
  • Posen, Daniel
  • Hatzopoulou, Marianne

Abstract

Electrification of urban transportation systems is an important path towards achieving low carbon transportation and meeting climate commitments. Despite zero on-road greenhouse gas emissions, the upstream emissions from electricity generation cannot be ignored. In this study, a heuristic algorithm was designed to optimize regional electric vehicle charging schedules with the objective of minimizing greenhouse gas emissions from electricity generation. Our study is set in the Greater Toronto and Hamilton Area. Emissions from the charging demand are estimated by a marginal emission model calibrated with historical data for Ontario electricity generation. The results illustrate that the optimized plan can reduce greenhouse gas emissions by around 97% compared to a base case, where vehicles are powered by gasoline. Four other charging scenarios (home, out of home, after trip, and after 3am) that do not entail optimization were also investigated and compared with the optimized plan. The scenario where charging is only allowed after 3am generates the lowest emissions among all four scenarios but its emissions are 50% higher than the optimized scenario. Charging at the end of each trip was observed to generate the highest emissions. Locations serving the most charging events, and the number of non-residential charging plugs were also evaluated. The home location serves most charging events, followed by workplace. The optimized plan requires the second highest number of public charging plugs . This implies a trade-off between greenhouse gas emissions associated with vehicle charging and investments in electric vehicle charging infrastructure.

Suggested Citation

  • Tu, Ran & Gai, Yijun (Jessie) & Farooq, Bilal & Posen, Daniel & Hatzopoulou, Marianne, 2020. "Electric vehicle charging optimization to minimize marginal greenhouse gas emissions from power generation," Applied Energy, Elsevier, vol. 277(C).
    • Handle: RePEc:eee:appene:v:277:y:2020:i:c:s0306261920310291
    DOI: 10.1016/j.apenergy.2020.115517
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    18. Jasmine Ramsebner & Albert Hiesl & Reinhard Haas, 2020. "Efficient Load Management for BEV Charging Infrastructure in Multi-Apartment Buildings," Energies, MDPI, vol. 13(22), pages 1-23, November.
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    21. Shaheer Ansari & Afida Ayob & Molla Shahadat Hossain Lipu & Aini Hussain & Mohamad Hanif Md Saad, 2021. "Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries," Energies, MDPI, vol. 14(22), pages 1-22, November.
    22. Mohammadzadeh, Narges & Zegordi, Seyed Hessameddin & Nikbakhsh, Ehsan, 2021. "Pricing and free periodic maintenance service decisions for an electric-and-fuel automotive supply chain using the total cost of ownership," Applied Energy, Elsevier, vol. 288(C).
    23. 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).

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