Planning under Uncertainty for Aggregated Electric Vehicle Charging using Markov Decision Processes

Erwin Walraven, Matthijs T. J. Spaan

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

The increasing penetration of renewable energy sources and electric vehicles raises important challenges related to the operation of electricity grids. For instance, the amount of power generated by wind turbines is time-varying and dependent on the weather, which makes it hard to match flexible electric vehicle demand and uncertain wind power supply. In this paper we propose a vehicle aggregation framework which uses Markov Decision Processes to control charging of multiple electric vehicles and deals with uncertainty in renewable supply. We present a grouping technique to address the scalability aspects of our framework. In experiments we show that the aggregation framework maximizes the profit of the aggregator while reducing usage of conventionally-generated power and cost of customers.
Original languageEnglish
Title of host publicationAAAI 2016 International Workshop on Artificial Intelligence for Smart Grids and Smart Buildings
PublisherAmerican Association for Artificial Intelligence (AAAI)
Pages1-7
Number of pages7
Publication statusPublished - 12 Feb 2016
EventAAAI 2016 International Workshop On Artificial Intelligence For Smart Grids And Smart Buildings - Phoenix, Arizona, United States
Duration: 12 Feb 201617 Feb 2016
https://www.cs.nmsu.edu/aisgsb16/

Conference

ConferenceAAAI 2016 International Workshop On Artificial Intelligence For Smart Grids And Smart Buildings
Country/TerritoryUnited States
CityPhoenix, Arizona
Period12/02/1617/02/16
Internet address

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