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

TL;DR

This paper introduces a decentralized multi-agent reinforcement learning method for incentive-based residential demand response, effectively reducing peak energy demand while preserving participant privacy and minimizing costs.

Contribution

It proposes a novel MARL approach with a disjunctively constrained knapsack optimization for demand management, addressing privacy and heterogeneity challenges.

Findings

Reduced Peak-to-Average Ratio by 14.48% in case studies

Effectively coordinated heterogeneous household preferences

Preserved participant privacy during demand response

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 contribution to the management of renewable energy resources and the growing electricity demand.