Smart charging of electric vehicles using reinforcement learning

Konstantina Valogianni, Wolfgang Ketter, John Collins

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

19 Citations (Scopus)

Abstract

The introduction of Electric Vehicles (EVs) in the existing Energy Grid raises many issues regarding Grid stability and charging behavior. Uncontrolled charging on the customer's side may increase the already high peaks in the energy demand that lead to respective increase in the energy prices. We propose a novel smart charging algorithm that maximizes individual welfare and reduces the individual energy expenses. We use Reinforcement Learning trained on real world data to learn the individual household consumption behavior and propose a charging algorithm with respect to individual welfare maximization objective. Furthermore, we use statistical customer models to simulate the EV customer behavior. We show that the individual customers, represented by intelligent agents, using the proposed charging algorithm reduce their energy expenses. Additionally, we show that the average energy prices, on an aggregated level, are reduced as a result of smarter use of the energy available. Finally we prove that the presented algorithm achieves significant peak reduction and reshaping of the energy demand curve.

Original languageEnglish
Title of host publicationTrading Agent Design and Analysis
Subtitle of host publicationPapers from the 2013 AAAI Workshop, Technical Report
PublisherAI Access Foundation
Pages41-48
Number of pages8
ISBN (Print)9781577356264
Publication statusPublished - 15 Jul 2013
Event2013 AAAI Workshop - Bellevue, WA, United States
Duration: 15 Jul 201315 Jul 2013

Publication series

SeriesAAAI Workshop - Technical Report
VolumeWS-13-15

Conference

Conference2013 AAAI Workshop
Country/TerritoryUnited States
CityBellevue, WA
Period15/07/1315/07/13

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