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

TL;DR

This paper introduces a deep reinforcement learning-based demand response method for smart grids that optimizes incentive rates to reduce peak demand while respecting capacity constraints, improving grid stability.

Contribution

It proposes a hierarchical framework with a deep RL approach to learn real-time incentives considering capacity limits and user preferences, a novel integration for demand response.

Findings

Reduces peak-to-average ratio by approximately 22.82%

Effectively smooths aggregated load profile

Demonstrates success with real-world data

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.