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

arXiv: https://arxiv.org/abs/2602.16525 · HTML: https://mlsanity.com/paper/2602.16525/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 5 of its 53 references that are in the map.
- First posted on arXiv 18 Feb 2026.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 5 of 5)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Distributed Deep Reinforcement Learning for Intelligent Load Scheduling in Residential Smart Grids](https://mlsanity.com/paper/2006.16100/) (2020)
- [Experimental analysis of data-driven control for a building heating system](https://mlsanity.com/paper/1507.03638/) (2015)
- [MARL-iDR: Multi-Agent Reinforcement Learning for Incentive-based Residential Demand Response](https://mlsanity.com/paper/2304.04086/) (2023)
- [Limiting gaming opportunities on incentive-based demand response programs](https://mlsanity.com/paper/1802.08604/) (2018)

## Abstract (arXiv)

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.