# Online Scheduling of a Residential Microgrid via Monte-Carlo Tree Search and a Learned Model

arXiv: https://arxiv.org/abs/2005.06161 · HTML: https://mlsanity.com/paper/2005.06161/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2020.

## Built on by (most cited first, 8 of 8)

- [Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges](https://mlsanity.com/paper/2102.01168/) (2021)
- [Beyond Games: A Systematic Review of Neural Monte Carlo Tree Search Applications](https://mlsanity.com/paper/2303.08060/) (2023)
- [Optimal Scheduling in IoT-Driven Smart Isolated Microgrids Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2305.00127/) (2023)
- [Designing Decision Support Systems for Emergency Response: Challenges and Opportunities](https://mlsanity.com/paper/2202.11268/) (2022)
- [Scalable Multi-Agent Reinforcement Learning for Residential Load Scheduling under Data Governance](https://mlsanity.com/paper/2110.02784/) (2021)
- [Joint Energy Dispatch and Unit Commitment in Microgrids Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2206.01663/) (2022)
- [Efficient Learning of Voltage Control Strategies via Model-based Deep Reinforcement Learning](https://mlsanity.com/paper/2212.02715/) (2022)
- [S3TS: Stochastic Scenario-Structured Tree Search for Advanced Planning Under Uncertainty](https://mlsanity.com/paper/2606.02151/) (2026)

## Abstract (arXiv)

The uncertainty of distributed renewable energy brings significant challenges to economic operation of microgrids. Conventional online optimization approaches require a forecast model. However, accurately forecasting the renewable power generations is still a tough task. To achieve online scheduling of a residential microgrid (RM) that does not need a forecast model to predict the future PV/wind and load power sequences, this paper investigates the usage of reinforcement learning (RL) approach to tackle this challenge. Specifically, based on the recent development of Model-Based Reinforcement Learning, MuZero, we investigate its application to the RM scheduling problem. To accommodate the characteristics of the RM scheduling application, a optimization framework that combines the modelbased RL agent with the mathematical optimization technique is designed, and long short-term memory (LSTM) units are adopted to extract features from the past renewable generation and load sequences. At each time step, the optimal decision is obtained by conducting Monte-Carlo tree search (MCTS) with a learned model and solving an optimal power flow sub-problem. In this way, this approach can sequentially make operational decisions online without relying on a forecast model. The numerical simulation results demonstrate the effectiveness of the proposed algorithm.