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A Distributed In-Transit Processing Infrastructure for Forecasting Electric Vehicle Charging Demand

Rafael Tolosana-Calasanz José Ángel Bañares Liana Cipcigan Omer F. Rana 1 Panagiotis Papadopoulos 2 Cong-Duc Pham 3
1 School of Computer Sciences & Informatics [Cardiff]
2 FORTH - Foundation for Research and Technology - Hellas
3 LIUPPA - Laboratoire Informatique de l'Université de Pau et des Pays de l'Adour
Abstract : With an increasing interest in Electric Vehicles (EVs), it is essential to understand how EV charging could impact demand on the Electricity Grid. Existing approaches used to achieve this make use of a centralised data collection mechanism - which often is agnostic of demand variation in a given geographical area. We present an in-transit data processing architecture that is more efficient and can aggregate a variety of different types of data. A model using Reference nets has been developed and evaluated. Our focus in this paper is primarily to introduce requirements for such an architecture. \textcopyright 2013 IEEE.
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Conference papers
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https://hal-univ-pau.archives-ouvertes.fr/hal-01912937
Contributor : Gaelle Chancerel-Lannuzel <>
Submitted on : Monday, November 5, 2018 - 5:50:21 PM
Last modification on : Thursday, March 5, 2020 - 7:11:17 PM

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Rafael Tolosana-Calasanz, José Ángel Bañares, Liana Cipcigan, Omer F. Rana, Panagiotis Papadopoulos, et al.. A Distributed In-Transit Processing Infrastructure for Forecasting Electric Vehicle Charging Demand. 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2013, Delft, Netherlands, May 13-16, 2013, 2013, Unknown, Unknown Region. pp.538--545, ⟨10.1109/CCGrid.2013.103⟩. ⟨hal-01912937⟩

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