Capacity-constrained demand response in smart grids using deep reinforcement learning

2026-02-18

AI Summary

This study addresses capacity overloads and congestion in residential smart grids caused by peak electricity demand by proposing a hierarchical incentive mechanism that balances the economic interests of both service providers and users. The approach integrates explicit capacity constraints into an incentive-based demand response framework and models heterogeneous user preferences and discomfort costs through an appliance-level home energy management system. Leveraging deep reinforcement learning, the method dynamically optimizes constrained incentive prices in real time to steer user consumption behavior. Experimental results on real-world household data demonstrate effective peak-shaving and valley-filling, achieving a 22.82% reduction in the peak-to-average ratio. To the best of our knowledge, this work is the first to realize a win-win dynamic pricing strategy under hard capacity constraints for both grid operators and consumers.

Technology Category

• Multiagent Systems: Mechanism Design
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

This paper presents a capacity-constrained incentive-based demand response approach for residential smart grids. It aims to maintain electricity grid capacity limits and prevent congestion by financially incentivising end users to reduce or shift their energy consumption. The proposed framework adopts a hierarchical architecture in which a service provider adjusts hourly incentive rates based on wholesale electricity prices and aggregated residential load. The financial interests of both the service provider and end users are explicitly considered. A deep reinforcement learning approach is employed to learn optimal real-time incentive rates under explicit capacity constraints. Heterogeneous user preferences are modelled through appliance-level home energy management systems and dissatisfaction costs. Using real-world residential electricity consumption and price data from three households, simulation results show that the proposed approach effectively reduces peak demand and smooths the aggregated load profile. This leads to an approximately 22.82% reduction in the peak-to-average ratio compared to the no-demand-response case.

Problem

• demand response
• capacity constraint
• smart grid
• incentive mechanism
• peak load reduction

Innovation

• deep reinforcement learning
• capacity-constrained demand response
• hierarchical architecture
• incentive-based DR
• residential smart grid