Demand Response through Price-setting Multi-agent Reinforcement Learning

Morten Herget Christensen; Cédric Ernewein; Pierre Pinson

doi:10.1145/3427773.3427862

Demand Response through Price-setting Multi-agent Reinforcement Learning

Morten Herget Christensen, Cédric Ernewein, Pierre Pinson

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-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 language	English
Title of host publication	RLEM'20: Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities
Number of pages	5
Publisher	Association for Computing Machinery
Publication date	2020
ISBN (Print)	9781450381932
DOIs	https://doi.org/10.1145/3427773.3427862
Publication status	Published - 2020
Event	1^st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities - Virtual event Duration: 17 Nov 2020 → 17 Nov 2020 https://rlem-workshop.net/

Workshop

Workshop	1^st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities
Location	Virtual event
Period	17/11/2020 → 17/11/2020
Internet address	https://rlem-workshop.net/

Series	Rlem 2020 - Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings and Cities

Access to Document

10.1145/3427773.3427862

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Christensen, M. H., Ernewein, C., & Pinson, P. (2020). Demand Response through Price-setting Multi-agent Reinforcement Learning. In RLEM'20: Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities Association for Computing Machinery. Rlem 2020 - Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings and Cities https://doi.org/10.1145/3427773.3427862

Christensen, Morten Herget ; Ernewein, Cédric ; Pinson, Pierre. / Demand Response through Price-setting Multi-agent Reinforcement Learning. RLEM'20: Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities. Association for Computing Machinery, 2020. (Rlem 2020 - Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings and Cities).

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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.",

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year = "2020",

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booktitle = "RLEM'20: Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities",

publisher = "Association for Computing Machinery",

address = "United States",

note = "1<sup>st</sup> International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities, RLEM'20 ; Conference date: 17-11-2020 Through 17-11-2020",

url = "https://rlem-workshop.net/",

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Christensen, MH, Ernewein, C & Pinson, P 2020, Demand Response through Price-setting Multi-agent Reinforcement Learning. in RLEM'20: Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities. Association for Computing Machinery, Rlem 2020 - Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings and Cities, 1^st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities, 17/11/2020. https://doi.org/10.1145/3427773.3427862

Demand Response through Price-setting Multi-agent Reinforcement Learning. / Christensen, Morten Herget; Ernewein, Cédric; Pinson, Pierre.

RLEM'20: Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities. Association for Computing Machinery, 2020. (Rlem 2020 - Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings and Cities).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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AB - 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.

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DO - 10.1145/3427773.3427862

M3 - Article in proceedings

SN - 9781450381932

T3 - Rlem 2020 - Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings and Cities

BT - RLEM'20: Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities

PB - Association for Computing Machinery

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Christensen MH, Ernewein C, Pinson P. Demand Response through Price-setting Multi-agent Reinforcement Learning. In RLEM'20: Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities. Association for Computing Machinery. 2020. (Rlem 2020 - Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings and Cities). https://doi.org/10.1145/3427773.3427862