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

Author

Tolosana-Calasanz, Rafael ; Banares, J.A. ; Cipcigan, L. ; Rana, Omer ; Papadopoulos, Panagiotis ; Congduc Pham

Author_Institution

Aragon Inst. of Eng. Res. (I3A), Univ. of Zaragoza, Zaragoza, Spain

fYear

2013

fDate

13-16 May 2013

Firstpage

538

Lastpage

545

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.

Keywords

battery powered vehicles; data handling; distributed processing; load forecasting; power engineering computing; power grids; EV; centralised data collection mechanism; demand variation agnostic; distributed in-transit processing infrastructure; electricity grid; forecasting electric vehicle charging demand; geographical area; reference nets; Batteries; Companies; Computer architecture; Demand forecasting; Meteorology; Quality of service; Vehicles; Distributed Data Stream Processing; Electric Vehicle Demand Forecasting;

fLanguage

English

Publisher

ieee

Conference_Titel

Cluster, Cloud and Grid Computing (CCGrid), 2013 13th IEEE/ACM International Symposium on

Conference_Location

Delft

Print_ISBN

978-1-4673-6465-2

Type

conf

DOI

10.1109/CCGrid.2013.103

Filename

6546136