When do you charge your electric vehicle? A stated adaptation approach

Langbroek, Joram Hendrik Maarten; Franklin, Joel P.; Susilo, Yusak O.

doi:10.1016/j.enpol.2017.06.023

Cited by 52 publications

(23 citation statements)

References 20 publications

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“…We assumed that the EV and V2G FC will provide better results according to the RES utilization when compared to SC, but results gained within this study showed the opposite. Previous study [45] showed that drivers' behavior can be regulated by electricity prices in a way that drivers will chose to charge their EVs during a period of lower electricity prices. Future energy planning and development of such systems will have to be supported using ICT tools [17,36] and smart grids [33] in order to provide communication in between all the participants of the energy system [31].…”

Section: Res Integration Through Ev Fast Chargingmentioning

confidence: 99%

Vehicle-to-Grid in Standard and Fast Electric Vehicle Charging: Comparison of Renewable Energy Source Utilization and Charging Costs

2020

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Croatia aims to achieve 10% of its energy production from the renewable energy sources in the total energy consumption in the transport sector. One of the ways to achieve this goal is by the use of electric vehicles. This work comparatively analyses the financial and social aspects of vehicle-to-grid charging in standard and fast charging mode, their impact on the renewable electricity production and the total electricity consumption regulated through variable electricity prices. Data were taken for the wider urban area of the Dubrovnik region. The assumption is that the Dubrovnik region will be self-sufficient by the year 2050 with 100% renewable electricity production and that all conventional vehicles will be replaced by electric vehicles. This work aims to show that the fast charging based on 10 min time steps offers more opportunities for flexibility and utilization of renewable generation in the energy system than the standard charging based on hourly time step. The results of this work showed the opposite, where in most of the scenarios standard charging provided better results. Replacement of the existing two tariff model in electricity prices with variable electricity prices contributes to the stability of the energy system, providing better regulation of charging and higher opportunities for renewable electricity utilization in standard and fast charging and reduction of charging costs. According to the financial aspects, fast charging is shown to be more expensive, but for the social aspects, it provides electric vehicles with more opportunities for better competition in the market.

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Section: Res Integration Through Ev Fast Chargingmentioning

confidence: 99%

Vehicle-to-Grid in Standard and Fast Electric Vehicle Charging: Comparison of Renewable Energy Source Utilization and Charging Costs

2020

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“…We identified a set of individual-and household-level attributes (including sex, income, age, education level, number of driving licenses and number of vehicles owned) for the MNL models to be developed, based on the previous studies of parking and charging behaviours, which suggested that socio-demographic attributes were associated with both parking [50,51] and charging [52,53] behaviours. Some specific empirical findings are as follows: [48] examined the possible influences of sex, income and age on drivers' choice of car parks, and found that people with higher incomes tended not to choose those parks which take a long walking time to research; Bonsall and Palmer observed different parking behaviours associated with education level and gender [54].…”

Section: Statistical Analysis Of Heterogenous Parking Behaviour: Multmentioning

confidence: 99%

Empirical Analysis of Parking Behaviour of Conventional and Electric Vehicles for Parking Modelling: A Case Study of Beijing, China

Zhuge

Shao

2019

Energies

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An empirical study of the parking behaviour of Conventional Vehicles (CVs), Battery Electric Vehicles (BEVs), and Plug-in Hybrid Electric Vehicles (PHEVs) was carried out with the data collected in a paper-based questionnaire survey in Beijing, China. The study investigated the factors that might influence the parking behaviour, with a focus on the maximum acceptable time of walking from parking lot to trip destination, parking fee, the availability of charging posts, the state of charge of EVs and the range anxiety of BEVs. Several Multinomial Logit (MNL) models were developed to explore the relationships between individual attributes and parking choices. The results suggest that (1) the maximum acceptable walking time generally increases with the rise in the amount of saving for parking fee; (2) the availability of charging posts does not influence the maximum acceptable walking time when PHEVs and BEVs have sufficient charge, but the percentage of people willing to walk longer than eight minutes increases from around 35% to 46% when PHEVs are in a low stage of charge; (3) more than half of BEV drivers want the driving range of their vehicles to be one and a half times the driving distance before they depart, given the distance is 50 km. Based on the empirical findings above, a conceptual framework was proposed to explicitly simulate the parking behaviour of both CVs and EVs using agent-based modelling.

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“…This may give rise to some security problems and overload in the power grid. Some of these inconveniences may be avoided to some extent by imposing some price incentives that stimulate EV owners to charge their EVs in off-peak hours (see, for example, [15,16]). However, it is generally difficult to achieve an optimal charging strategy [17].…”

Section: Literature Reviewmentioning

confidence: 99%

Electric Vehicle Charging Scheduling by an Enhanced Artificial Bee Colony Algorithm

et al. 2018

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Scheduling the charging times of a large fleet of Electric Vehicles (EVs) may be a hard problem due to the physical structure and conditions of the charging station. In this paper, we tackle an EV’s charging scheduling problem derived from a charging station designed to be installed in community parking where each EV has its own parking lot. The main goals are to satisfy the user demands and at the same time to make the best use of the available power. To solve the problem, we propose an artificial bee colony (ABC) algorithm enhanced with local search and some mating strategies borrowed from genetic algorithms. The proposal is analyzed experimentally by simulation and compared with other methods previously proposed for the same problem. The results of the experimental study provided interesting insights about the problem and showed that the proposed algorithm is quite competitive with previous methods.

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When do you charge your electric vehicle? A stated adaptation approach

Cited by 52 publications

References 20 publications

Vehicle-to-Grid in Standard and Fast Electric Vehicle Charging: Comparison of Renewable Energy Source Utilization and Charging Costs

Vehicle-to-Grid in Standard and Fast Electric Vehicle Charging: Comparison of Renewable Energy Source Utilization and Charging Costs

Empirical Analysis of Parking Behaviour of Conventional and Electric Vehicles for Parking Modelling: A Case Study of Beijing, China

Electric Vehicle Charging Scheduling by an Enhanced Artificial Bee Colony Algorithm

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