# Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework

arXiv: https://arxiv.org/abs/2211.15858 · HTML: https://mlsanity.com/paper/2211.15858/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 37 references that are in the map.
- First posted on arXiv 29 Nov 2022.

## Builds on (most cited first, 4 of 4)

- [Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks](https://mlsanity.com/paper/2110.14300/) (2021)
- [Distributed Deep Reinforcement Learning for Intelligent Load Scheduling in Residential Smart Grids](https://mlsanity.com/paper/2006.16100/) (2020)
- [Continuous Multiagent Control using Collective Behavior Entropy for Large-Scale Home Energy Management](https://mlsanity.com/paper/2005.10000/) (2020)
- [Dynamic Pricing and Distributed Energy Management for Demand Response](https://mlsanity.com/paper/1601.02319/) (2016)

## Built on by (most cited first, 1 of 1)

- [EcoNet: Multiagent Planning and Control Of Household Energy Resources Using Active Inference](https://mlsanity.com/paper/2512.21343/) (2025)

## Abstract (arXiv)

This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users. DR has a widely recognized potential for improving power grid stability and reliability, while at the same time reducing end-users energy bills. However, the conventional DR techniques come with several shortcomings, such as the inability to handle operational uncertainties while incurring end-user disutility, which prevents widespread adoption in real-world applications. The proposed framework addresses these shortcomings by implementing DR and DEM based on real-time pricing strategy that is achieved using deep reinforcement learning. Furthermore, this framework enables the power grid service provider to leverage distributed energy resources (i.e., PV rooftop panels and battery storage) as dispatchable assets to support the smart grid during peak hours, thus achieving management of distributed energy resources. Simulation results based on the Deep Q-Network (DQN) demonstrate significant improvements of the 24-hour accumulative profit for both prosumers and the power grid service provider, as well as major reductions in the utilization of the power grid reserve generators.