Electric vehicle charging choices: Modelling and implications for smart charging services. (August 2017)

• Record Type: Journal Article
• Authors: Daina, Nicolò; Sivakumar, Aruna; Polak, John W.
• Is Part Of: Transportation research. Volume 81(2017)
• Page Start: 36
• Page End: 56
• Publication Date: 2017-08
• Subjects: Charging choices -- Electric vehicles -- Smart charging -- Charging service provider; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2017.05.006
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: We provide a model of charging choice to appraise smart charging services. Our model integrates with activity based models for transport and energy analyses. Our model captures empirically the behavioural nuances of charging choices. Results show insights on trade-offs among EV range, charging time and charging cost. We draw implications for the development of smart charging services. Abstract: The rollout of electric vehicles (EV) occurring in parallel with the decarbonisation of the power sector can bring uncontested environmental benefits, in terms of CO2 emission reduction and air quality. This roll out, however, poses challenges to power systems, as additional power demand is injected in context of increasingly volatile supply from renewable energy sources. Smart EV charging services can provide a solution to such challenges. The development of effective smart charging services requires evaluating pre-emptively EV drivers' response. The current practice in the appraisal of smart charging strategies largely relies on simplistic or theoretical representation of drivers' charging and travel behaviour. We propose a random utility model for joint EV drivers' activity-travel scheduling and charging choices. Our model easily integrates in activity-based demand modelling systems for the analyses of integrated transport and energy systems. However, unlike previous charging behaviour models used in integrated transport and energy system analyses, our model empirically captures the behavioural nuances of tactical charging choices in smart grid context, using empirically estimated charging preferences. We present model estimation results that provide insights into the value placed by individuals on the main attributes of the charging choice and draw implications charging service providers.