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Spatio-temporal modelling of electric vehicle charging demand and impacts on peak household electrical load

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Abstract

This paper presents a composite methodology for obtaining spatial and temporal projections of charging demand and peak-shaving potential from plug-in electric vehicles (EVs), and the associated spatio-temporal impacts on peak household electrical load. The methodology comprises a suite of models of future EV uptake, travel by households, household electricity demand and recharge/discharge of EVs at their home locations. The analysis is disaggregated to hourly time-steps over a full year, and spatially to mesh blocks comprising around 250 houses per block. The modelling suite is applied to an analysis of peak household load impacts across the state of Victoria, Australia, under nine different combinations of EV uptake and charging/discharging behaviour. The projected increase in peak household electrical loads under expected penetration rates and charging demands is less than 10 % on most high-demand days, but can be up to 15 % on a handful of days and geographic locations. Peak-load impacts under off-peak charging are mostly less than 5 %. With judicious EV discharging strategies, there is potential to shave peak loads on the highest demand days by up to 5 %.

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Acknowledgments

The authors gratefully acknowledge the Victorian State Government (led by the Department of Transport), SP AusNet and the CSIRO Energy Transformed Flagship for co-funding this research. We would also like to acknowledge our CSIRO and University of Technology, Sydney, colleagues for their contributions to various underpinning research activities—John Gardner, Chris Phyland, Tarek Elgindy, Yong-Bing Khoo, Greg Foliente, George Quezada, Dong Chen, Mingwei Zhou, Josh Usher, Chris Dunstan, Katie Ross, Leigh Christie and Conal Horgan.

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Authors and Affiliations

  1. CSIRO Ecosystem Sciences, P.O. Box 56, Highett, VIC, Australia

    Phillip Paevere, Zhengen Ren, George Grozev & Cheryl McNamara

  2. CSIRO Ecosystem Sciences, P.O. Box 2583, Brisbane, QLD, Australia

    Andrew Higgins

  3. CSIRO Computational Informatics, North Ryde, NSW, Australia

    Mark Horn

Authors
  1. Phillip Paevere
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  2. Andrew Higgins
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  3. Zhengen Ren
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  4. Mark Horn
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  5. George Grozev
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  6. Cheryl McNamara
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Correspondence to Andrew Higgins.

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Handled by Osamu Saito, UNU-Institute for Sustainability and Peace (ISP), Japan.

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Paevere, P., Higgins, A., Ren, Z. et al. Spatio-temporal modelling of electric vehicle charging demand and impacts on peak household electrical load. Sustain Sci 9, 61–76 (2014). https://doi.org/10.1007/s11625-013-0235-3

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  • DOI: https://doi.org/10.1007/s11625-013-0235-3

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