# Optimal Scheduling of Isolated Microgrids Using Automated Reinforcement Learning-based Multi-period Forecasting

arXiv: https://arxiv.org/abs/2108.06764 · HTML: https://mlsanity.com/paper/2108.06764/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 31 references that are in the map.
- First posted on arXiv 15 Aug 2021.

## Builds on (most cited first, 2 of 2)

- [Optimal Scheduling of an Isolated Microgrid with Battery Storage Considering Load and Renewable Generation Uncertainties](https://mlsanity.com/paper/1808.05691/) (2018)
- [Optimal Scheduling of Integrated Demand Response-Enabled Integrated Energy Systems with Uncertain Renewable Generations: A Stackelberg Game Approach](https://mlsanity.com/paper/2103.04723/) (2021)

## Built on by (most cited first, 14 of 14)

- [Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2211.02674/) (2022)
- [A Deep-Learning Intelligent System Incorporating Data Augmentation for Short-Term Voltage Stability Assessment of Power Systems](https://mlsanity.com/paper/2112.03265/) (2021)
- [Bitcoin Transaction Strategy Construction Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2109.14789/) (2021)
- [Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach](https://mlsanity.com/paper/2212.13472/) (2022)
- [Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm](https://mlsanity.com/paper/2304.01223/) (2023)
- [Optimal Scheduling in IoT-Driven Smart Isolated Microgrids Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2305.00127/) (2023)
- [Enhancing Cyber-Resilience in Integrated Energy System Scheduling with Demand Response Using Deep Reinforcement Learning](https://mlsanity.com/paper/2311.17941/) (2023)
- [Review on Monitoring, Operation and Maintenance of Smart Offshore Wind Farms](https://mlsanity.com/paper/2211.00221/) (2022)
- [CONSTRUCTA: Automating Commercial Construction Schedules in Fabrication Facilities with Large Language Models](https://mlsanity.com/paper/2502.12066/) (2025)
- [Privacy-preserving household load forecasting based on non-intrusive load monitoring: A federated deep learning approach](https://mlsanity.com/paper/2206.15192/) (2022)
- [Predicting Short Term Energy Demand in Smart Grid: A Deep Learning Approach for Integrating Renewable Energy Sources in Line with SDGs 7, 9, and 13](https://mlsanity.com/paper/2304.03997/) (2023)
- [The State of the Art in transformer fault diagnosis with artificial intelligence and Dissolved Gas Analysis: A Review of the Literature](https://mlsanity.com/paper/2304.11880/) (2023)
- [Short-term power load forecasting method based on CNN-SAEDN-Res](https://mlsanity.com/paper/2309.07140/) (2023)
- [An End-to-End Approach for Microgrid Probabilistic Forecasting and Robust Operation via Decision-focused Learning](https://mlsanity.com/paper/2512.12755/) (2025)

## Abstract (arXiv)

In order to reduce the negative impact of the uncertainty of load and renewable energies outputs on microgrid operation, an optimal scheduling model is proposed for isolated microgrids by using automated reinforcement learning-based multi-period forecasting of renewable power generations and loads. Firstly, a prioritized experience replay automated reinforcement learning (PER-AutoRL) is designed to simplify the deployment of deep reinforcement learning (DRL)-based forecasting model in a customized manner, the single-step multi-period forecasting method based on PER-AutoRL is proposed for the first time to address the error accumulation issue suffered by existing multi-step forecasting methods, then the prediction values obtained by the proposed forecasting method are revised via the error distribution to improve the prediction accuracy; secondly, a scheduling model considering demand response is constructed to minimize the total microgrid operating costs, where the revised forecasting values are used as the dispatch basis, and a spinning reserve chance constraint is set according to the error distribution; finally, by transforming the original scheduling model into a readily solvable mixed integer linear programming via the sequence operation theory (SOT), the transformed model is solved by using CPLEX solver. The simulation results show that compared with the traditional scheduling model without forecasting, this approach manages to significantly reduce the system operating costs by improving the prediction accuracy.