论文标题
低惯性电力系统中快速频率控制的增强学习方法
A Reinforcement Learning Approach for Fast Frequency Control in Low-Inertia Power Systems
论文作者
论文摘要
电网正在从化石燃料的发电到可再生能源的主要过渡，通常通过电力电子接口到网格。因此，由于惯性和阻尼水平较低，预计未来的电力系统将面临增强的控制复杂性，并挑战与频率稳定性有关的挑战。结果，新型辅助服务的频率控制和开发变得迫在眉睫。本文提出了一个基于增强学习（RL）的数据驱动的控制方案，用于网格形成电压源转换器（VSC），目的是利用其快速响应能力来为系统提供快速频率控制。基于RL的集中式控制器收集发电机频率并根据干扰调整VSC功率输出，以防止频率阈值违规。分析了所提出的控制方案，并通过IEEE 14-BUS测试系统的详细时间域模拟进行了评估。
The electric grid is undergoing a major transition from fossil fuel-based power generation to renewable energy sources, typically interfaced to the grid via power electronics. The future power systems are thus expected to face increased control complexity and challenges pertaining to frequency stability due to lower levels of inertia and damping. As a result, the frequency control and development of novel ancillary services is becoming imperative. This paper proposes a data-driven control scheme, based on Reinforcement Learning (RL), for grid-forming Voltage Source Converters (VSCs), with the goal of exploiting their fast response capabilities to provide fast frequency control to the system. A centralized RL-based controller collects generator frequencies and adjusts the VSC power output, in response to a disturbance, to prevent frequency threshold violations. The proposed control scheme is analyzed and its performance evaluated through detailed time-domain simulations of the IEEE 14-bus test system.
