Scale Deployment Of Electric Vehicles Research Articles

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

A large scale deployment of electric vehicles (EVs) is likely to contribute to a more sustainable transport system. However, charging EVs will increase the load on the electricity network. The maximum load may be minimized by coordinating the timing of charging activities, in order to spread electricity demand more equally over the course of a day. In this study, based on a stated-choice experiment, the effects of two different temporal price differentiation strategies on stated charging time are investigated, including socio-demographic, behavioural and socio-psychological variables.In a situation without charging time coordination, a peak in charging events is likely to occur during the early evening. Temporal price differentiation has a significant influence on charging time and in particular the level of price differentiation matters. The likelihood to change charging time differs and different alternative time slots are chosen when comparing high to low levels of price differentiation. People that have more knowledge about EVs have a higher chance to change their charging time, whereas people that have the tendency to plan their trips long time beforehand are less likely to adjust their charging time in the scenarios with temporal price differentiation.

Innovative Technologies for Smart Cities: Towards Customer Driven Infrastructure Design for Large Scale Deployment of Electric Vehicles and Vehicle-to-Grid Applications

The large scale deployment of electric vehicles in cities will play a key role over the next decades to reduce air-pollutants in densely populated areas. This will imply a shift of the energy demand from the oil sector to the electric energy utilities, and hence the development of an adequate recharge infrastructure to meet the electricity demand from electric vehicles. Moreover, the full integration of electric vehicles within future smart cities will call for the development of new Vehicle-to-Grid applications, enabling the grid operators and end-users to dynamically interact with the vehicles. The purpose of this paper is to demonstrate how driving patterns and data mining can be used for the design of a smart recharge infrastructure and for identifying Vehicle-to-Grid hot-spots over vast geographical areas. The analysis relies on a database of approximately 16,000 vehicles and 2.6 million of parking events, interfaced with an electric vehicle model and two recharge behavioural models. The results help to understand to which extent battery electric vehicles can replace conventional fuel cars under real-world constraints, quantifying their electric energy demand and their recursive parking locations, to design a customised recharge infrastructure and identify Vehicle-to-Grid hot-spots. This paper provides a description of the developed model, analysing its potential and limitations and underlining its possible future applications for smart cities, providing new insights towards the definition, assessment and development of future energy and transport policy.

Customer-driven design of the recharge infrastructure and Vehicle-to-Grid in urban areas: A large-scale application for electric vehicles deployment

The large scale deployment of electric vehicles in urban environment will play a key-role over the next decades to reduce air-pollutants in densely populated areas, but it will also require the development of an adequate recharge infrastructure. The purpose of this paper is to demonstrate how driving patterns databases and data mining can be used to appropriately design this infrastructure. This application focuses on the Italian province of Firenze, involving about 12,000 conventional fuel vehicles monitored over one month, estimating a fleet share shift from conventional fuel vehicles to battery electric vehicles ranging from 10% to 57%, and a mileage share shift from 1.6% to 36.5%. The increase of electric energy demand from electric vehicles ranges from 0.7% to 18% of the total demand in the province, with a number of charging spots three-to-six times higher than the number of circulating electric vehicles. Additionally the results show that a Vehicle-to-Grid interaction strategy can contribute to reduce from 5% to 50% the average daily electric energy demand in specific locations. This paper provides a description of the developed model and focuses on the valuable potential of the proposed methodology to support future policies for designing alternative fuel infrastructure in urban areas.

Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms

Transportation electrification has become an important issue in recent decades and the large scale deployment of electric vehicles (EVs) has yet to be achieved. The smart coordination of EV demand addresses an improvement in the flexibility of power systems and reduces the costs of power system investment. The uncertainty in EV drivers’ behaviour is one of the main problems to solve to obtain an optimal integration of EVs into power systems. In this paper, an optimisation algorithm to coordinate the charging of EVs has been developed and implemented using a Genetic Algorithm (GA), where thermal line limits, the load on transformers, voltage limits and parking availability patterns are taken into account to establish an optimal load pattern for EV charging-based reliability. This methodology has been applied to an existing residential low-voltage system. The results indicate that a smart charging schedule for EVs leads to a flattening of the load profile, peak load shaving and the prevention of the aging of power system elements.

The feasibility of long range battery electric cars in New Zealand

New Zealand transport accounts for over 40% of the carbon emissions with private cars accounting for 25%. In the Ministry of Economic Development's recently released “New Zealand Energy Strategy to 2050”, it proposed the wide scale deployment of electric vehicles as a means of reducing carbon emissions from transport. However, New Zealand's lack of public transport infrastructure and its subsequent reliance on private car use for longer journeys could mean that many existing battery electric vehicles (BEVs) will not have the performance to replace conventionally fuelled cars. As such, this paper discusses the potential for BEVs in New Zealand, with particular reference to the development of the University of Waikato's long-range UltraCommuter BEV. It is shown that to achieve a long range at higher speeds, BEVs should be designed specifically rather than retrofitting existing vehicles to electric. Furthermore, the electrical energy supply for a mixed fleet of 2 million BEVs is discussed and conservatively calculated, along with the number of wind turbines to achieve this. The results show that approximately 1350 MW of wind turbines would be needed to supply the mixed fleet of 2 million BEVs, or 54% of the energy produced from NZ's planned and installed wind farms.