MARL-iDR: Multi-Agent Reinforcement Learning for Incentive-Based Residential Demand Response

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This paper presents a decentralized Multi-Agent Reinforcement Learning (MARL) approach to an incentive-based Demand Response (DR) program, which aims to maintain the capacity limits of the electricity grid and prevent grid congestion by financially incentivizing residential consumers to reduce their energy consumption. The proposed approach addresses the key challenge of coordinating heterogeneous preferences and requirements from multiple participants while preserving their privacy and minimizing financial costs for the aggregator. The participant agents use a novel Disjunctively Constrained Knapsack Problem optimization to curtail or shift the requested household appliances based on the selected demand reduction. Through case studies with electricity data from 25 households, the proposed approach effectively reduced energy consumption's Peak-to-Average ratio (PAR) by 14.48% compared to the original PAR while fully preserving participant privacy. This approach has the potential to significantly improve the efficiency and reliability of the electricity grid, making it an important con-tribution to the management of renewable energy resources and the growing electricity demand.
Original languageEnglish
Title of host publicationProceedings of the 2023 IEEE Belgrade PowerTech
Place of PublicationPiscataway
Number of pages8
ISBN (Electronic)978-1-6654-8778-8
ISBN (Print)978-1-6654-8779-5
Publication statusPublished - 2023
Event2023 IEEE Belgrade PowerTech - Belgrade, Serbia
Duration: 25 Jun 202329 Jun 2023

Publication series

Name2023 IEEE Belgrade PowerTech, PowerTech 2023


Conference2023 IEEE Belgrade PowerTech

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Reinforcement Learning
  • Incentive-based Demand Response
  • Multi-Agent systems


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