Online energy management of electric vehicle parking-lots

Arman Alahyari, David Pozo, Mohammad Ali Sadri

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Electric vehicles (EV) charging scheduling in parking lots has been a hot topic in recent years. Instead of simply starting the charging process with the entrance of the EVs, a parking lot operator can decrease the cost of buying electricity in real-time, when prices are low. However, this decision-making process involves randomness in both price and EVs behavior (arrival and departure times). In this study, we introduce a supervised machine learning framework using a multi-layer perceptron regression that can train an online estimator to help the operator with the aforementioned process. This online estimator uses a small set of historical data and provides values of the amount of energy that should be bought by the operator. We use this method in the online management of EVs within parking-lots and evaluate the performance with a real-world EVs' charging data.

Original languageEnglish
Title of host publicationSEST 2020 - 3rd International Conference on Smart Energy Systems and Technologies
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728147017
DOIs
Publication statusPublished - Sep 2020
Event3rd International Conference on Smart Energy Systems and Technologies, SEST 2020 - Virtual, Istanbul, Turkey
Duration: 7 Sep 20209 Sep 2020

Publication series

NameSEST 2020 - 3rd International Conference on Smart Energy Systems and Technologies

Conference

Conference3rd International Conference on Smart Energy Systems and Technologies, SEST 2020
Country/TerritoryTurkey
CityVirtual, Istanbul
Period7/09/209/09/20

Keywords

  • Electric Vehicles
  • Machine Learning
  • Online Management
  • Smart Charging
  • Uncertainty

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