  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.
