In the present paper an agent based approach, addressed to simulate the behaviour of a Plug-in Electric Vehicles (PEV) fleet into a Smart City, is presented. Considering the traffic data-set available from mobility plans, a spatial and time model, representing the evolution of travel patterns, can be developed considering each vehicle as an agent. The following statistical analysis in space and time of the agent behaviours is used to plan the PEV charging infrastructure of municipalities. The proposed planning methodology has been tested on an European city in order to evaluate the effectiveness of the proposed procedure. Such charging infrastructure, defined according to the mobility needs, has been tested and used to evaluate the customer satisfaction of PEV users in term of charging demand. The proposed charging system has been implemented to estimate the average daily energy profiles for charging the smart city PEV fleet during a typical workday. This has been finally used as one day ahead energy reference profile to develop a market-oriented EV charging strategies. The performance of the proposed smart charging strategies has been finally simulated and compared.
An Agent Based Approach for the Development of EV fleet Charging Strategies in Smart Cites
MUSIO, MAURA;DAMIANO, ALFONSO
2014-01-01
Abstract
In the present paper an agent based approach, addressed to simulate the behaviour of a Plug-in Electric Vehicles (PEV) fleet into a Smart City, is presented. Considering the traffic data-set available from mobility plans, a spatial and time model, representing the evolution of travel patterns, can be developed considering each vehicle as an agent. The following statistical analysis in space and time of the agent behaviours is used to plan the PEV charging infrastructure of municipalities. The proposed planning methodology has been tested on an European city in order to evaluate the effectiveness of the proposed procedure. Such charging infrastructure, defined according to the mobility needs, has been tested and used to evaluate the customer satisfaction of PEV users in term of charging demand. The proposed charging system has been implemented to estimate the average daily energy profiles for charging the smart city PEV fleet during a typical workday. This has been finally used as one day ahead energy reference profile to develop a market-oriented EV charging strategies. The performance of the proposed smart charging strategies has been finally simulated and compared.
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