# Dynamic Energy Dispatch Based on Deep Reinforcement Learning in IoT-Driven Smart Isolated Microgrids

arXiv: https://arxiv.org/abs/2002.02581 · HTML: https://mlsanity.com/paper/2002.02581/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2020.

## Built on by (most cited first, 13 of 13)

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- [Optimal Scheduling in IoT-Driven Smart Isolated Microgrids Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2305.00127/) (2023)
- [Reinforcement Learning for Sustainable Energy: A Survey](https://mlsanity.com/paper/2407.18597/) (2024)
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- [Combating Uncertainties in Wind and Distributed PV Energy Sources Using Integrated Reinforcement Learning and Time-Series Forecasting](https://mlsanity.com/paper/2302.14094/) (2023)
- [Energy Optimization for HVAC Systems in Multi-VAV Open Offices: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2306.13333/) (2023)
- [Electric Bus Charging Schedules Relying on Real Data-Driven Targets Based on Hierarchical Deep Reinforcement Learning](https://mlsanity.com/paper/2505.10262/) (2025)
- [Applications of Reinforcement Learning in Deregulated Power Market: A Comprehensive Review](https://mlsanity.com/paper/2205.08369/) (2022)
- [Joint Energy Dispatch and Unit Commitment in Microgrids Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2206.01663/) (2022)
- [Collaborative Optimization of Multi-microgrids System with Shared Energy Storage Based on Multi-agent Stochastic Game and Reinforcement Learning](https://mlsanity.com/paper/2306.10754/) (2023)

## Abstract (arXiv)

Microgrids (MGs) are small, local power grids that can operate independently from the larger utility grid. Combined with the Internet of Things (IoT), a smart MG can leverage the sensory data and machine learning techniques for intelligent energy management. This paper focuses on deep reinforcement learning (DRL)-based energy dispatch for IoT-driven smart isolated MGs with diesel generators (DGs), photovoltaic (PV) panels, and a battery. A finite-horizon Partial Observable Markov Decision Process (POMDP) model is formulated and solved by learning from historical data to capture the uncertainty in future electricity consumption and renewable power generation. In order to deal with the instability problem of DRL algorithms and unique characteristics of finite-horizon models, two novel DRL algorithms, namely, finite-horizon deep deterministic policy gradient (FH-DDPG) and finite-horizon recurrent deterministic policy gradient (FH-RDPG), are proposed to derive energy dispatch policies with and without fully observable state information. A case study using real isolated MG data is performed, where the performance of the proposed algorithms are compared with the other baseline DRL and non-DRL algorithms. Moreover, the impact of uncertainties on MG performance is decoupled into two levels and evaluated respectively.