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The potential and economics of EV smart charging: A case study in Shanghai

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  • Jian, Liu
  • Yongqiang, Zhao
  • Hyoungmi, Kim

Abstract

The development of electric vehicles (EVs) is not only essential to improving energy efficiency and reducing emissions in transportation sector, but also increases power system flexibility through demand response. However, the quantitative research on the potential and economics of smart charging for different types of EVs in China remains insufficient, which has been increasingly regarded as a critical barrier for associated policy decision marking. To address this issue, this paper conducts a survey on the charging behaviors of four types of EVs in Shanghai and evaluates the capacity potential and cost of EV smart charging. A Bass Diffusion model is adopted to forecast the stock growth of EVs and the potential of smart charging is investigated by simulating EV charging behavior under two scenarios. It finds that private and commercial EVs could deliver greater smart charging than that of electric bus and taxis. Business models and charging policies that could cost-effectively explore the potential of EV smart charging are further discussed. The paper concludes that charging service providers with time of use charging tariffs could realize the potential of smart charging in a cost-efficient way.

Suggested Citation

  • Jian, Liu & Yongqiang, Zhao & Hyoungmi, Kim, 2018. "The potential and economics of EV smart charging: A case study in Shanghai," Energy Policy, Elsevier, vol. 119(C), pages 206-214.
    • Handle: RePEc:eee:enepol:v:119:y:2018:i:c:p:206-214
    DOI: 10.1016/j.enpol.2018.04.037
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    Citations

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    Cited by:

    1. Chao-Tsung Ma, 2019. "System Planning of Grid-Connected Electric Vehicle Charging Stations and Key Technologies: A Review," Energies, MDPI, vol. 12(21), pages 1-22, November.
    2. Sousa, Joana & Soares, Isabel, 2023. "Benefits and barriers concerning demand response stakeholder value chain: A systematic literature review," Energy, Elsevier, vol. 280(C).
    3. Kobashi, Takuro & Choi, Younghun & Hirano, Yujiro & Yamagata, Yoshiki & Say, Kelvin, 2022. "Rapid rise of decarbonization potentials of photovoltaics plus electric vehicles in residential houses over commercial districts," Applied Energy, Elsevier, vol. 306(PB).
    4. Jinah Yang & Daiki Min & Jeenyoung Kim, 2020. "The Use of Big Data and Its Effects in a Diffusion Forecasting Model for Korean Reverse Mortgage Subscribers," Sustainability, MDPI, vol. 12(3), pages 1-17, January.
    5. Lagomarsino, Maria & van der Kam, Mart & Parra, David & Hahnel, Ulf J.J., 2022. "Do I need to charge right now? Tailored choice architecture design can increase preferences for electric vehicle smart charging," Energy Policy, Elsevier, vol. 162(C).
    6. Tarroja, Brian & Hittinger, Eric, 2021. "The value of consumer acceptance of controlled electric vehicle charging in a decarbonizing grid: The case of California," Energy, Elsevier, vol. 229(C).
    7. Jean-Michel Clairand & Paulo Guerra-Terán & Xavier Serrano-Guerrero & Mario González-Rodríguez & Guillermo Escrivá-Escrivá, 2019. "Electric Vehicles for Public Transportation in Power Systems: A Review of Methodologies," Energies, MDPI, vol. 12(16), pages 1-22, August.
    8. Purnell, K. & Bruce, A.G. & MacGill, I., 2022. "Impacts of electrifying public transit on the electricity grid, from regional to state level analysis," Applied Energy, Elsevier, vol. 307(C).
    9. Li, Bo & Ma, Ziming & Hidalgo-Gonzalez, Patricia & Lathem, Alex & Fedorova, Natalie & He, Gang & Zhong, Haiwang & Chen, Minyou & Kammen, Daniel M., 2021. "Modeling the impact of EVs in the Chinese power system: Pathways for implementing emissions reduction commitments in the power and transportation sectors," Energy Policy, Elsevier, vol. 149(C).

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