论文标题
使用Modelica和Python的恒温控制负载控制的增强学习
Reinforcement Learning for Thermostatically Controlled Loads Control using Modelica and Python
论文作者
论文摘要
该项目的目的是调查和评估应用强化学习（RL）进行电力系统控制的机会。作为概念证明（POC），使用基于Modelica的管道开发了用于恒温控制载荷（TCLS）的电压控制（TCLS）。 Q学习RL算法已经过验证，用于确定性和随机初始化TCLS。后者的建模更接近真实的网格行为，考虑到负载开关的随机性，这挑战了控制开发。此外，本文显示了Q学习参数的影响，包括国家行动空间的离散化对控制器性能。
The aim of the project is to investigate and assess opportunities for applying reinforcement learning (RL) for power system control. As a proof of concept (PoC), voltage control of thermostatically controlled loads (TCLs) for power consumption regulation was developed using Modelica-based pipeline. The Q-learning RL algorithm has been validated for deterministic and stochastic initialization of TCLs. The latter modelling is closer to real grid behaviour, which challenges the control development, considering the stochastic nature of load switching. In addition, the paper shows the influence of Q-learning parameters, including discretization of state-action space, on the controller performance.
