MARL-iDR

Multi-Agent Reinforcement Learning for Incentive-Based Residential Demand Response

Conference paper (2023)

Authors

L. Cavalcante Siebert Interactive Intelligence - EEMCS
Jochen Cremer Intelligent Electrical Power Grids - EEMCS
Research Group
Interactive Intelligence (EEMCS) (TU Delft)
Copyright: © 2023 Jasper van Tilburg, L. Cavalcante Siebert, Jochen Cremer
DOI
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https://doi.org/10.1109/PowerTech55446.2023.10202941
Reinforcement Learning Multi-Agent systems Incentive-based Demand Response
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Published Date

2023

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Interactive Intelligence

Abstract

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.