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.
Highlights
In a context of a progressively decarbonised power sector, electric vehicles (EV) can bring significant reductions in CO2 emissions from road traffic (IEA, 2009)
A myopic choice, appears consistent with the view of charging behaviour as a coping strategy resulting from range appraisal, as conceptualised and tested by Franke and Krems (2013)
These could potentially be used to model individuals’ EV use and charging planning choices over longer time horizons considering multiple charging opportunities. Such a model extension is beyond the scope of the present work, which aims at the detailed modelling of tactical charging choices, at their occurrence, and their related trade-offs
Summary
In a context of a progressively decarbonised power sector, electric vehicles (EV) can bring significant reductions in CO2 emissions from road traffic (IEA, 2009). Daina et al / Transportation Research Part C 81 (2017) 36–56 single transaction (Bessa and Matos, 2012) In this centralised framework EV load aggregators act as an intermediary between vehicle owners and grid markets and contract power demand from several EVs. In the decentralised framework, individual EVs respond to market information made available to them. The typical aggregator based approach to charging demand management implies direct control This means that control actions are imposed on electric vehicles without the involvement of the electric vehicle owners (Galus et al, 2012). The energy requirement specifies the battery level required by the end of the charging operation while the timing requirement specifies the time by which the charging operation must be completed Under this scheme, users directly affect the flexibility of the controls that can be imposed on the charging operation through their charging preferences.
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