# Joint Energy Dispatch and Unit Commitment in Microgrids Based on Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2206.01663 · HTML: https://mlsanity.com/paper/2206.01663/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 3 Jun 2022.

## Builds on (most cited first, 9 of 9)

- [The Uncertainty Bellman Equation and Exploration](https://mlsanity.com/paper/1709.05380/) (2017)
- [Deep Reinforcement Learning in Parameterized Action Space](https://mlsanity.com/paper/1511.04143/) (2015)
- [Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space](https://mlsanity.com/paper/1810.06394/) (2018)
- [A Review of Deep Reinforcement Learning for Smart Building Energy Management](https://mlsanity.com/paper/2008.05074/) (2020)
- [Deep Reinforcement Learning for Smart Home Energy Management](https://mlsanity.com/paper/1909.10165/) (2019)
- [On-line Building Energy Optimization using Deep Reinforcement Learning](https://mlsanity.com/paper/1707.05878/) (2017)
- [Dynamic Energy Dispatch Based on Deep Reinforcement Learning in IoT-Driven Smart Isolated Microgrids](https://mlsanity.com/paper/2002.02581/) (2020)
- [Online Scheduling of a Residential Microgrid via Monte-Carlo Tree Search and a Learned Model](https://mlsanity.com/paper/2005.06161/) (2020)
- [Branching Dueling Q-Network Based Online Scheduling of a Microgrid With Distributed Energy Storage Systems](https://mlsanity.com/paper/2105.13497/) (2021)

## Abstract (arXiv)

Nowadays, the application of microgrids (MG) with renewable energy is becoming more and more extensive, which creates a strong need for dynamic energy management. In this paper, deep reinforcement learning (DRL) is applied to learn an optimal policy for making joint energy dispatch (ED) and unit commitment (UC) decisions in an isolated MG, with the aim for reducing the total power generation cost on the premise of ensuring the supply-demand balance. In order to overcome the challenge of discrete-continuous hybrid action space due to joint ED and UC, we propose a DRL algorithm, i.e., the hybrid action finite-horizon DDPG (HAFH-DDPG), that seamlessly integrates two classical DRL algorithms, i.e., deep Q-network (DQN) and deep deterministic policy gradient (DDPG), based on a finite-horizon dynamic programming (DP) framework. Moreover, a diesel generator (DG) selection strategy is presented to support a simplified action space for reducing the computation complexity of this algorithm. Finally, the effectiveness of our proposed algorithm is verified through comparison with several baseline algorithms by experiments with real-world data set.