Abstract
Recently, due to the growth of the electric vehicle (EV) market, the investigation of grid-to-vehicle and vehicle-to-grid strategies has become a priority in both the electric mobility and distribution grid research areas. However, there is still a lack of large-scale data sets to test and deploy energy management strategies. In this paper, a fully customizable EV population simulator is presented as an attempt to fill this gap. The proposed tool is designed as a web simulator as well as a Matlab/Simulink block, in order to facilitate its integration in different projects and applications. It provides individual and aggregated charge, discharge and plugin/out event data for a population of EVs, considering both home and public charging stations. The population is generated on the basis of statistical data (which can be fully customized) including commuting distances, vehicle models, traffic and social behavior of the owners. A peak-shaving case study is finally proposed to show the potential of the simulator.
Highlights
Recent forecasts of the penetration rate of electric vehicles (EVs) into the market [1] and their potential impact on the electricity grid will enable a new realm of applications
For limit values equal to or below 10% the mean buffer results are negative, implying requirements are not met for a certain number of EVs at the time of plugout
The buff-perc column shows the percentage of plugout events where the EV requirements are completely fulfilled
Summary
Recent forecasts of the penetration rate of electric vehicles (EVs) into the market [1] and their potential impact on the electricity grid will enable a new realm of applications. One way to achieve this goal is to adjust (i.e., reduce, increase or shift) the electricity demand by using implicit or explicit demand-side flexibility approaches [2]. An increased flexibility level can be achieved by sector coupling, for example in the form of electrification of the mobility (electric vehicles) and the heating sectors (power to heat), or via smart appliances. The integration of electric vehicles in the electricity grid can provide flexibility due to the daily cycles of charging of their batteries. Another viable technique is the coupling of the heating and electricity sectors, which can provide flexible short-term demand by using heat pumps, heat storage and electric cooling loads. Demand-side flexibility (DSF) can be obtained through aggregated residential demand-side management in smart homes [3]
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