Recently, due to the increment of electric vehicles (EVs), the investigation of vehicle-to-grid paradigm strategies has become a key concern in both the electric mobility and distribution grid research areas. Indeed each EV can be seen as a distributed energy storage, thus giving to each customer a potential active role in the energy distribution scenario. However, there is still a lack of large scale data (often location dependant) to test and deploy energy management strategies for vehicle-to grid services. In this paper, a scalable EVs population simulator is presented as an attempt to fill this gap. The proposed tool provides individual and aggregated charge, discharge and plugin/-out events data of a custom geographically defined population of EVs, considering both home and public charging stations. The population is generated on the basis of statistical data (which can be obtained by data driven approaches or a priori assumptions) including commuting distances, vehicles models, traffic and social behavior of the owners.

A New Hybrid Software Tool for the Simulation of Energy Usage in a Population of Electric Vehicles

Di Somma M.;Graditi G.;
2020

Abstract

Recently, due to the increment of electric vehicles (EVs), the investigation of vehicle-to-grid paradigm strategies has become a key concern in both the electric mobility and distribution grid research areas. Indeed each EV can be seen as a distributed energy storage, thus giving to each customer a potential active role in the energy distribution scenario. However, there is still a lack of large scale data (often location dependant) to test and deploy energy management strategies for vehicle-to grid services. In this paper, a scalable EVs population simulator is presented as an attempt to fill this gap. The proposed tool provides individual and aggregated charge, discharge and plugin/-out events data of a custom geographically defined population of EVs, considering both home and public charging stations. The population is generated on the basis of statistical data (which can be obtained by data driven approaches or a priori assumptions) including commuting distances, vehicles models, traffic and social behavior of the owners.
978-1-7281-7455-6
Aggregator
Electric Vehicles
Simulator
Vehicle to Grid
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12079/59065
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