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.

中文翻译：

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信息不完全的网络微电网基于学习的电源管理方法

本文提出了一种用于配电系统中的联网微电网（MG）的双层电源管理的近似强化学习（RL）方法。在实践中，合作代理人可能对MG资产行为以及通用耦合点（PCC）背后的详细模型了解有限或不了解。这使得配电系统变得不可观察，并阻碍了针对受限MG电源管理问题的常规优化解决方案。为了应对这一挑战，我们提出了一个基于价格的环境中的双层RL框架。在较高的层次上，合作代理执行功能逼近来预测在MG参数模型的不完全信息下的实体的行为。而在较低级别，每个MG都提供了受潮流限制的对价格信号的最佳响应。然后，在自适应RL框架内使用函数逼近方案来优化价格信号，因为系统负载和太阳能发电量会随时间变化。数值实验证明，与文献中的先前工作相比，提出的隐私保护学习模型具有更好的适应性和更高的计算速度。