# Decentralized Microgrid Energy Management: A Multi-agent Correlated Q-learning Approach

arXiv: https://arxiv.org/abs/2103.04154 · HTML: https://mlsanity.com/paper/2103.04154/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2021.

## Built on by (most cited first, 5 of 5)

- [Smart Home Energy Management: VAE-GAN synthetic dataset generator and Q-learning](https://mlsanity.com/paper/2305.08885/) (2023)
- [Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation in Smart Home](https://mlsanity.com/paper/2201.07387/) (2022)
- [Multi-agent Bayesian Deep Reinforcement Learning for Microgrid Energy Management under Communication Failures](https://mlsanity.com/paper/2111.11868/) (2021)
- [Reinforcement Learning Enabled Peer-to-Peer Energy Trading for Dairy Farms](https://mlsanity.com/paper/2405.12716/) (2024)
- [Peer-to-Peer Energy Trading in Dairy Farms using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2511.23148/) (2025)

## Abstract (arXiv)

Microgrids (MG) are anticipated to be important players in the future smart grid. For proper operation of MGs an Energy Management System (EMS) is essential. The EMS of an MG could be rather complicated when renewable energy resources (RER), energy storage system (ESS) and demand side management (DSM) need to be orchestrated. Furthermore, these systems may belong to different entities and competition may exist between them. Nash equilibrium is most commonly used for coordination of such entities however the convergence and existence of Nash equilibrium can not always be guaranteed. To this end, we use the correlated equilibrium to coordinate agents, whose convergence can be guaranteed. In this paper, we build an energy trading model based on mid-market rate, and propose a correlated Q-learning (CEQ) algorithm to maximize the revenue of each agent. Our results show that CEQ is able to balance the revenue of agents without harming total benefit. In addition, compared with Q-learning without correlation, CEQ could save 19.3% cost for the DSM agent and 44.2% more benefits for the ESS agent.