This is the autho s e sio of a o k that was published in the following source: Hardman, S.; Jenn, A.; Beard, G.; Daina, N.;Figenbaum, E.; Jochem, P. E. P.; Kinnear, N. A. D.; Pontes, J. P.;Refa, N.;Turrentine, T. S.; Witkamp, B. (2018). AbstractThis paper presents a literature review of studies that investigate infrastructure needs to support the market introduction of plug-in electric vehicles (PEVs). It focuses on literature relating to consumer preferences for charging infrastructure, and how consumers interact with and use this infrastructure. This includes studies that use questionnaire surveys, interviews, modelling, GPS data from vehicles, and data from electric vehicle charging equipment. These studies indicate that the most important location for PEV charging is at home, followed by work, and then public locations. Studies have found that more effort is needed to ensure consumers have easy access to PEV charging and that charging at home, work, or public locations should not be free of cost. Research indicates that PEV charging will not impact electricity grids on the short term, however charging may need to be managed when the vehicles are deployed in greater numbers. In some areas of study the literature is not sufficiently mature to draw any conclusions from. More research is especially needed to determine how much infrastructure is needed to support the roll out of PEVs. This paper ends with policy implications and suggests avenues of future research.Next, we provide background information on charging modes and levels and then introduce the approach to the literature review. Section 2 then summarises the literature, whilst Section 3 concludes with insights for policymakers and literature gaps.

The electric vehicle (EV) flexibility, indicates to what extent the charging load can be coordinated (i.e., to flatten the load curve or to utilize renewable energy resources). However, such flexibility is neither well analyzed nor effectively quantified in literature. In this paper we fill this gap and offer an extensive

Abstract-The increasing adoption of electric vehicles (EVs) presents both challenges and opportunities for the power grid, especially for distribution system operators (DSOs). The demand represented by EVs can be significant, but on the other hand, sojourn times of EVs could be longer than the time required to charge their batteries to the desired level (e.g., to cover the next trip). The latter observation means that the electrical load from EVs is characterized by a certain level of flexibility, which could be exploited for example in demand response (DR) approaches (e.g., to balance generation from renewable energy sources).This paper analyzes two data sets, one from a charging-athome field trial in Flanders (about 8.5k charging sessions) and another from a large-scale EV public charging pole deployment in The Netherlands (more than 1M sessions). We rigorously analyze the collected data and quantify aforementioned flexibility:(1) we characterize the EV charging behavior by clustering the arrival and departure time combinations, identifying three behaviors (charging near home, charging near work, and park to charge), (2) we fit statistical models for the sojourn time, and flexibility (i.e., non-charging idle time) for each type of observed behavior, and (3) quantify the the potential of DR exploitation as the maximal load that could be achieved by coordinating EV charging for a given time of day t, continuously until t + ∆.

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The adoption of electric vehicles (EV) has to be complemented with the right charging infrastructure roll-out. This infrastructure is already in place in many cities throughout the main markets of China, EU and USA. Public policies are both taken at regional and/or at a city level targeting both EV adoption, but also charging infrastructure management. A growing trend is the increasing idle time over the years (time an EV is connected without charging), which directly impacts on the sizing of the infrastructure, hence its cost or availability. Such a phenomenon can be regarded as an opportunity but may very well undermine the same initiatives being taken to promote adoption; in any case it must be measured, studied, and managed. The time an EV takes to charge depends on its initial/final state of charge (SOC) and the power being supplied to it. The problem however is to estimate the time the EV remains parked after charging (idle time), as it depends on many factors which simple statistical analysis cannot tackle. In this study we apply supervised machine learning to a dataset from the Netherlands and analyze three regression algorithms, Random Forest, Gradient Boosting and XGBoost, identifying the most accurate one and main influencing parameters. The model can provide useful information for EV users, policy maker and network owners to better manage the network, targeting specific variables. The best performing model is XGBoost with an R2 score of 60.32% and mean absolute error of 1.11. The parameters influencing the model the most are: The time of day in which the charging sessions start and the total energy supplied with 22.35%, 15.57% contribution respectively. Partial dependencies of variables and model performances are presented and implications on public policies discussed.

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This study aims to quantify the impact of uncontrolled charging of Electric Vehicles (EVs) on the low voltage distribution networks with increasing EV penetration levels. For this objective, key indicators are developed to show the magnitude, scale and duration of the impact on the distribution network. The disseminated results are based on the case study with actual data from the existing distribution networks. The findings of this paper can serve as a benchmark for determining the potential of smart EV charging algorithms and/or the extent of necessary infrastructural reinforcement that the grid operators must incorporate.