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@inproceedings{Acha:2013:10.1049/cp.2013.1002,
author = {Acha, Izquierdo S and Van, Dam KH and Shah, N},
doi = {10.1049/cp.2013.1002},
publisher = {IET},
title = {Spatial and Temporal Electric Vehicle Demand Forecasting in Central London},
url = {http://dx.doi.org/10.1049/cp.2013.1002},
year = {2013}
}

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TY  - CPAPER
AB  - If electricity infrastructures are to make the most of electric vehicle (EV) technology it is paramount to understand how mobility can enhance the management of assets and the delivery of energy. This research builds on a proof of concept model that focuses on simulating EV movements in urban environments which serve to forecast EV loads in the networks. Having performed this analysis for a test urban environment, this paper details a case study for London using an activity-based model to make predictions of EV movements which can be validated against measured transport data. Results illustrate how optimal EV charging can impact the load profiles of two areas in central London - St. John's Wood & Marylebone/Mayfair. Transport data highlights the load flexibility a fleet of EVs can have on a daily basis in one of the most stressed networks in the world, while an optimal power flow manages the best times of the day to charge the EVs. This study presents valuable information that can help in begin addressing pressing infrastructure issues such as charging point planning and network control reinforcement.
AU  - Acha,Izquierdo S
AU  - Van,Dam KH
AU  - Shah,N
DO  - 10.1049/cp.2013.1002
PB  - IET
PY  - 2013///
TI  - Spatial and Temporal Electric Vehicle Demand Forecasting in Central London
UR  - http://dx.doi.org/10.1049/cp.2013.1002
UR  - http://hdl.handle.net/10044/1/38820
ER  -

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