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|Title:||Peer to peer energy trading with electric vehicles||Authors:||Alvaro-Hermana, R.
|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|
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