Planning charging stations for 2050 to support flexible electric vehicle demand considering individual mobility patterns

Jiaman Wu; Siobhan Powell; Yanyan Xu; Ram Rajagopal; Marta C. Gonzalez

doi:10.1016/j.crsus.2023.100006

Planning charging stations for 2050 to support flexible electric vehicle demand considering individual mobility patterns

Jiaman Wu
, Siobhan Powell
, Yanyan Xu
, Ram Rajagopal
, Marta C. Gonzalez^*

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

36 Citations (Scopus)

51 Downloads (CityUHK Scholars)

Abstract

With the widespread adoption of electric vehicles (EVs), it is crucial to plan for charging in a way that considers both EV driver behavior and the electricity grid's demand. Here, we integrate detailed mobility data with empirical charging preferences to estimate charging demand and demonstrate the power of personalized shifting recommendations to move individual EV drivers’ demand on the grid out of peak hours. We find an unbalanced geographical distribution of charging demand in the San Francisco Bay Area, with temporal peaks in both grid off-peak hours in the morning and on-peak hours in the evening. Aligning with mobility patterns, our strategy effectively shifts demand to off-peak times. With the 2050 target of 90% EVs, this shifting reduces total on-peak charging demand by 61%, which could require over ∼18,000 additional level 3 chargers. We recommend building more charging stations and implementing shifting recommendations for EV grid integration. © 2023

Original language	English
Article number	100006
Journal	Cell Reports Sustainability
Volume	1
Issue number	1
Online published	8 Jan 2024
DOIs	https://doi.org/10.1016/j.crsus.2023.100006
Publication status	Published - 26 Jan 2024
Externally published	Yes

Funding

This work was supported by Engie SA, the ITS-SB1 Berkeley Statewide Transportation Research Program and the California Air Resources Board. Y.X. was supported by the National Natural Science Foundation of China (62102258), the Shanghai Municipal Science and Technology Major Project (2021SHZDZX0102), and Shanghai Pujiang Program (21PJ1407300). The authors would like to thank Dr. Gustavo Cezar for his support. The authors would like to thank ChargePoint for the use of their data in this project under grant EPC-16-057 funded by the California Energy Commission. S.P. acknowledges funding from the Bits & Watts Initiative of Stanford University. R.R. acknowledges the National Science Foundation CAREER award #1554178. J.W. S.P. Y.X. R.R. and M.C.G conceived the research. J.W. S.P. Y.X. R.R. and M.C.G. developed the methodology. S.P. and Y.X. provided data. J.W. and S.P. processed the data. J.W. implemented the methodology. J.W. S.P. Y.X. R.R. and M.C.G analyzed the results. J.W. S.P. and Y.X. performed visualization. J.W. and M.C.G. prepared the original draft. J.W. S.P. Y.X. R.R. and M.C.G. edited and revised the manuscript. R.R. and M.C.G. supervised the research. The authors declare no competing interests. We support inclusive, diverse, and equitable conduct of research.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Research Keywords

charging demand
charging demand management
electric vehicle charging station
electric vehicles
mobility pattern

Publisher's Copyright Statement

This full text is made available under CC-BY-NC-ND 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/

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abstract = "With the widespread adoption of electric vehicles (EVs), it is crucial to plan for charging in a way that considers both EV driver behavior and the electricity grid's demand. Here, we integrate detailed mobility data with empirical charging preferences to estimate charging demand and demonstrate the power of personalized shifting recommendations to move individual EV drivers{\textquoteright} demand on the grid out of peak hours. We find an unbalanced geographical distribution of charging demand in the San Francisco Bay Area, with temporal peaks in both grid off-peak hours in the morning and on-peak hours in the evening. Aligning with mobility patterns, our strategy effectively shifts demand to off-peak times. With the 2050 target of 90\% EVs, this shifting reduces total on-peak charging demand by 61\%, which could require over ∼18,000 additional level 3 chargers. We recommend building more charging stations and implementing shifting recommendations for EV grid integration. {\textcopyright} 2023",

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N2 - With the widespread adoption of electric vehicles (EVs), it is crucial to plan for charging in a way that considers both EV driver behavior and the electricity grid's demand. Here, we integrate detailed mobility data with empirical charging preferences to estimate charging demand and demonstrate the power of personalized shifting recommendations to move individual EV drivers’ demand on the grid out of peak hours. We find an unbalanced geographical distribution of charging demand in the San Francisco Bay Area, with temporal peaks in both grid off-peak hours in the morning and on-peak hours in the evening. Aligning with mobility patterns, our strategy effectively shifts demand to off-peak times. With the 2050 target of 90% EVs, this shifting reduces total on-peak charging demand by 61%, which could require over ∼18,000 additional level 3 chargers. We recommend building more charging stations and implementing shifting recommendations for EV grid integration. © 2023

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Planning charging stations for 2050 to support flexible electric vehicle demand considering individual mobility patterns

Abstract

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UN SDGs

Research Keywords

Publisher's Copyright Statement

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