The introduction of electric vehicles into the transportation sector helps reduce global warming and carbon emissions. The interaction between electric vehicles and the power grid has spurred the emergence of a smart grid technology, denoted as vehicle-to grid-technology. Vehicle-to-grid technology manages the energy exchange between a large fleet of electric vehicles and the power grid to accomplish shared advantages for the vehicle owners and the power utility. This paper presents an optimal scheduling of vehicle-to-grid using the genetic algorithm to minimize the power grid load variance. This is achieved by allowing electric vehicles charging (grid-to-vehicle) whenever the actual power grid loading is lower than the target loading, while conducting electric vehicle discharging (vehicle-to-grid) whenever the actual power grid loading is higher than the target loading. The vehicle-to-grid optimization algorithm is implemented and tested in MATLAB software (R2013a, MathWorks, Natick, MA, USA). The performance of the optimization algorithm depends heavily on the setting of the target load, power grid load and capability of the grid-connected electric vehicles. Hence, the performance of the proposed algorithm under various target load and electric vehicles’ state of charge selections were analysed. The effectiveness of the vehicle-to-grid scheduling to implement the appropriate peak load shaving and load levelling services for the grid load variance minimization is verified under various simulation investigations. This research proposal also recommends an appropriate setting for the power utility in terms of the selection of the target load based on the electric vehicle historical data.

Electric vehicles (EVs) are drawing increasing attention, given the volatile fuel prices and impending reduction in fuel supplies. EVs are also preferred in the automotive industry as they reduce the environmental impact and fuel consumption while achieving a higher efficiency when compared to internal combustion engine vehicles (ICEVs). Although most of energy and environmental policies worldwide have set targets with the goal to shift from classic fossil fuel driven vehicles to electrified transport, the share of EVs is still rather low. The complexity of changing the human perception of transportation goes beyond technical and economic aspects and very few research activities managed to capture the additional factors. This paper presents a comprehensive review of studies on consumer preferences for EV, aiming to better inform policy-makers and give direction to further research. In addition, this paper discusses the main obstacles that limit the social acceptance in adopting EVs such as driving obstacles, charging obstacles and willingness-to-pay from consumers. Furthermore, mitigation strategies are outlined as the recommendation to promote a better EV deployment.