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 - Online
    Duration: 17 Nov 202017 Nov 2020
    Conference number: 1
    https://rlem-workshop.net/

    Workshop

    Workshop1st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities
    Number1
    LocationOnline
    Period17/11/202017/11/2020
    Internet address

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