Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/21991
Title: Peer to peer energy trading with electric vehicles
Authors: Alvaro-Hermana, R.
Fraile-Ardanuy, J.
Zufiria, P.
KNAPEN, Luk 
JANSSENS, Davy 
Issue Date: 2016
Source: IEEE Intelligent Transportation Systems Magazine, 8(3), p. 33-44
Abstract: This paper presents a novel peer-to-peer energy trading system between two sets of electric vehicles, which significantly reduces the impact of the charging process on the power system during business hours. This trading system is also economically beneficial for all the users involved in the trading process. An activity-based model is used to predict the daily agenda and trips of a synthetic population for Flanders (Belgium). These drivers can be initially classified into three sets; after discarding the set of drivers who will be short of energy without charging chances due to their tight schedule, we focus on the two remaining relevant sets: those who complete all their daily trips with an excess of energy in their batteries and those who need to (and can) charge their vehicle during some daily stops within their scheduled trips. These last drivers have the chance to individually optimize their energy cost in the time-space dimensions, taking into account the grid electricity price and their mobility constraints. Then, collecting all the available offer/demand information among vehicles parked in the same area at the same time, an aggregator determines an optimal peer-to-peer price per area and per time slot, allowing customers with excess of energy in their batteries to share with benefits this good with other users who need to charge their vehicles during their daily trips. Results show that, when applying the proposed trading system, the energy cost paid by these drivers at a specific time slot and in a specific area can be reduced up to 71%.
Keywords: predictive models; sociology; statistics; peer-to-peer computing; electric vehicles
Document URI: http://hdl.handle.net/1942/21991
ISSN: 1939-1390
e-ISSN: 1941-1197
DOI: 10.1109/MITS.2016.2573178
ISI #: 000384889800004
Category: A1
Type: Journal Contribution
Validations: ecoom 2018
Appears in Collections:Research publications

Files in This Item:
File Description SizeFormat 
P2PEnergyTradingWithEV.PDF
  Restricted Access
Published version1.76 MBAdobe PDFView/Open    Request a copy
Show full item record

SCOPUSTM   
Citations

67
checked on Sep 2, 2020

WEB OF SCIENCETM
Citations

99
checked on May 22, 2022

Page view(s)

46
checked on May 26, 2022

Download(s)

42
checked on May 26, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.