# Reinforcement Learning-based Energy Trading for Microgrids

arXiv: https://arxiv.org/abs/1801.06285 · HTML: https://mlsanity.com/paper/1801.06285/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2018.

## Built on by (most cited first, 2 of 2)

- [Multi-agent Bayesian Deep Reinforcement Learning for Microgrid Energy Management under Communication Failures](https://mlsanity.com/paper/2111.11868/) (2021)
- [Optimal Scheduling in IoT-Driven Smart Isolated Microgrids Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2305.00127/) (2023)

## Abstract (arXiv)

With the time-varying renewable energy generation and power demand, microgrids (MGs) exchange energy in smart grids to reduce their dependence on power plants. In this paper, we formulate an MG energy trading game, in which each MG trades energy according to the predicted renewable energy generation and local energy demand, the current battery level, and the energy trading history. The Nash quilibrium (NE) of the game is provided, revealing the conditions under which the local energy generation satisfies the energy demand of the MG and providing the performance bound of the energy trading scheme. We propose a reinforcement learning based MG energy trading scheme that applies the deep Q-network (DQN) to improve the utility of the MG for the case with a large number of the connected MGs. Simulations are performed for the MGs with wind generation that are aware of the electricity prices and the historic energy trading, showing that this scheme significantly reduces the average power plant schedules and improves the utility of the MG compared with the benchmark strategy.