# A Learning-based Power Management for Networked Microgrids Under Incomplete Information

arXiv: https://arxiv.org/abs/1810.01758 · HTML: https://mlsanity.com/paper/1810.01758/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2018.

## Built on by (most cited first, 6 of 6)

- [Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges](https://mlsanity.com/paper/2102.01168/) (2021)
- [Safe reinforcement learning for multi-energy management systems with known constraint functions](https://mlsanity.com/paper/2207.03830/) (2022)
- [A Multiobjective Reinforcement Learning Framework for Microgrid Energy Management](https://mlsanity.com/paper/2307.08692/) (2023)
- [Imitation Game: A Model-based and Imitation Learning Deep Reinforcement Learning Hybrid](https://mlsanity.com/paper/2404.01794/) (2024)
- [Resilient Control of Networked Microgrids using Vertical Federated Reinforcement Learning: Designs and Real-Time Test-Bed Validations](https://mlsanity.com/paper/2311.12264/) (2023)
- [Physical Informed-Inspired Deep Reinforcement Learning Based Bi-Level Programming for Microgrid Scheduling](https://mlsanity.com/paper/2410.11932/) (2024)

## Abstract (arXiv)

This paper presents an approximate Reinforcement Learning (RL) methodology for bi-level power management of networked Microgrids (MG) in electric distribution systems. In practice, the cooperative agent can have limited or no knowledge of the MG asset behavior and detailed models behind the Point of Common Coupling (PCC). This makes the distribution systems unobservable and impedes conventional optimization solutions for the constrained MG power management problem. To tackle this challenge, we have proposed a bi-level RL framework in a price-based environment. At the higher level, a cooperative agent performs function approximation to predict the behavior of entities under incomplete information of MG parametric models; while at the lower level, each MG provides power-flow-constrained optimal response to price signals. The function approximation scheme is then used within an adaptive RL framework to optimize the price signal as the system load and solar generation change over time. Numerical experiments have verified that, compared to previous works in the literature, the proposed privacy-preserving learning model has better adaptability and enhanced computational speed.