Leveraging Predictions in Power System Frequency Control: An Adaptive Approach
Leveraging Predictions in Power System Frequency Control: An Adaptive Approach
复制标题
DOI：
10.1109/cdc49753.2023.10383969
复制
发表时间：
2023-05
期刊：
影响因子：
--
通讯作者：
Wenqi Cui;Guanya Shi;Yuanyuan Shi;Baosen Zhang
中科院分区：
文献类型：
--
作者：
Wenqi Cui;Guanya Shi;Yuanyuan Shi;Baosen Zhang
随着可再生能源的不断增加，确保电网的频率稳定是电力系统运行的关键问题。近年来，许多先进的控制器被设计用于优化频率控制。然而，这些控制器几乎总是假设系统中的净负载在足够长的时间内保持不变。考虑到可再生资源的间歇性和不确定性，明确考虑随时间变化的净负荷变得非常重要。本文提出了一种具有显著时变净负荷的电力系统的自适应频率控制方法。我们利用短期负荷预测方面的进步，利用天气和其他特征可以准确预测系统中的净负荷。我们将这些预测集成到自适应控制器的设计中，该控制器可以与大多数现有控制器无缝结合，包括传统的下垂控制和新兴的基于神经网络的控制器。证明了整个控制体系结构实现了分散的频率恢复。实例研究表明，与现有方法相比，该方法提高了暂态和频率恢复性能。
Ensuring the frequency stability of electric grids with increasing renewable resources is a key problem in power system operations. In recent years, a number of advanced controllers have been designed to optimize frequency control. These controllers, however, almost always assume that the net load in the system remains constant over a sufficiently long time. Given the intermittent and uncertain nature of renewable resources, it is becoming important to explicitly consider net load that is time-varying. This paper proposes an adaptive approach to frequency control in power systems with significant time-varying net load. We leverage the advances in short-term load forecasting, where the net load in the system can be accurately predicted using weather and other features. We integrate these predictions into the design of adaptive controllers, which can be seamlessly combined with most existing controllers including conventional droop control and emerging neural network-based controllers. We prove that the overall control architecture achieves frequency restoration decentralizedly. Case studies verify that the proposed method improves both transient and frequency-restoration performances compared to existing approaches.