The rising adoption of plug-in electric vehicles (PEVs) leads to the temporal alignment of their electricity and mobility demands. However, mobility demand has not yet been considered in electricity planning and management. Here, we present a method to estimate individual mobility of PEV drivers at fine temporal and spatial resolution, by integrating three unique datasets of mobile phone activity of 1.39 million Bay Area residents, census data and the PEV drivers survey data. Through coupling the uncovered patterns of PEV mobility with the charging activity of PEVs in 580,000 session profiles obtained in the same region, we recommend changes in PEV charging times of commuters at their work stations and shave the pronounced peak in power demand. Informed by the tariff of electricity, we calculate the monetary gains to incentivize the adoption of the recommendations. These results open avenues for planning for the future of coupled transportation and electricity needs using personalized data.
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We would like to thank ChargePoint for providing the electric vehicle charging data and Airsage for providing the call detail records used in this study. We also would like to thank S. Kiliccote and M. Tabone for their valuable feedback. This work was supported by the Siebel Energy Institute and MIT Energy Initiative.
The authors declare no competing interests.
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Xu, Y., Çolak, S., Kara, E.C. et al. Planning for electric vehicle needs by coupling charging profiles with urban mobility. Nat Energy 3, 484–493 (2018). https://doi.org/10.1038/s41560-018-0136-x
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