# Optimal Scheduling in IoT-Driven Smart Isolated Microgrids Based on Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2305.00127 · HTML: https://mlsanity.com/paper/2305.00127/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 48 references that are in the map.
- First posted on arXiv 28 Apr 2023.

## Builds on (most cited first, 13 of 13)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Deep Recurrent Q-Learning for Partially Observable MDPs](https://mlsanity.com/paper/1507.06527/) (2015)
- [The Uncertainty Bellman Equation and Exploration](https://mlsanity.com/paper/1709.05380/) (2017)
- [Memory-based control with recurrent neural networks](https://mlsanity.com/paper/1512.04455/) (2015)
- [Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space](https://mlsanity.com/paper/1810.06394/) (2018)
- [Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges](https://mlsanity.com/paper/1907.09059/) (2019)
- [On-line Building Energy Optimization using Deep Reinforcement Learning](https://mlsanity.com/paper/1707.05878/) (2017)
- [Optimal Scheduling of Isolated Microgrids Using Automated Reinforcement Learning-based Multi-period Forecasting](https://mlsanity.com/paper/2108.06764/) (2021)
- [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)
- [Optimal Scheduling of an Isolated Microgrid with Battery Storage Considering Load and Renewable Generation Uncertainties](https://mlsanity.com/paper/1808.05691/) (2018)
- [Multi-agent Bayesian Deep Reinforcement Learning for Microgrid Energy Management under Communication Failures](https://mlsanity.com/paper/2111.11868/) (2021)
- [Reinforcement Learning-based Energy Trading for Microgrids](https://mlsanity.com/paper/1801.06285/) (2018)

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

- [Optimizing Electric Bus Charging Scheduling with Uncertainties Using Hierarchical Deep Reinforcement Learning](https://mlsanity.com/paper/2505.10296/) (2025)
- [Safe and Sustainable Electric Bus Charging Scheduling with Constrained Hierarchical DRL](https://mlsanity.com/paper/2512.03059/) (2025)

## Abstract (arXiv)

In this paper, we investigate the scheduling issue of diesel generators (DGs) in an Internet of Things (IoT)-Driven isolated microgrid (MG) by deep reinforcement learning (DRL). The renewable energy is fully exploited under the uncertainty of renewable generation and load demand. The DRL agent learns an optimal policy from history renewable and load data of previous days, where the policy can generate real-time decisions based on observations of past renewable and load data of previous hours collected by connected sensors. The goal is to reduce operating cost on the premise of ensuring supply-demand balance. In specific, a novel finite-horizon partial observable Markov decision process (POMDP) model is conceived considering the spinning reserve. In order to overcome the challenge of discrete-continuous hybrid action space due to the binary DG switching decision and continuous energy dispatch (ED) decision, a DRL algorithm, namely the hybrid action finite-horizon RDPG (HAFH-RDPG), is proposed. HAFH-RDPG seamlessly integrates two classical DRL algorithms, i.e., deep Q-network (DQN) and recurrent deterministic policy gradient (RDPG), based on a finite-horizon dynamic programming (DP) framework. Extensive experiments are performed with real-world data in an IoT-driven MG to evaluate the capability of the proposed algorithm in handling the uncertainty due to inter-hour and inter-day power fluctuation and to compare its performance with those of the benchmark algorithms.