Demand Response through Price-setting Multi-agent Reinforcement Learning

Morten Herget Christensen, Cédric Ernewein, Pierre Pinson

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Price based demand response is a cost-effective way of obtaining flexibility needed in power systems with high penetration of intermittent renewable energy sources. Model-free deep reinforcement learning is proposed as a way to train autonomous agents for enabling buildings to participate in demand response programs as well as coordinating such programs though price setting in a multiagent setup. First, we show price responsive control of buildings with electric heat pumps using deep deterministic policy gradient. Then a coordinating agent is trained to manage a population of buildings by adjusting the price in order to keep the total load from exceeding the available capacity considering also the non-flexible base load.
Original languageEnglish
Title of host publicationRLEM'20: Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities
Number of pages5
PublisherAssociation for Computing Machinery
Publication date2020
ISBN (Print)9781450381932
DOIs
Publication statusPublished - 2020
Event1st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities - Virtual event
Duration: 17 Nov 202017 Nov 2020
https://rlem-workshop.net/

Workshop

Workshop1st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities
LocationVirtual event
Period17/11/202017/11/2020
Internet address
SeriesRlem 2020 - Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings and Cities

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