论文标题
自动生成控制信号的统计建模和预测
Statistical Modeling and Forecasting of Automatic Generation Control Signals
论文作者
论文摘要
电力系统自动生成控制（AGC）的频率调节单元的性能取决于它们准确跟踪AGC信号的能力。此外，代表性模型以及高级分析和分析可以产生有助于控制器设计的AGC信号的预测。在本文中，时间序列分析是在AGC信号（特别是PJM Reg-d）上进行的，并使用结果进行了统计模型，该模型得出了相当准确地捕获其第二瞬间和饱和性质，以及基于时间序列的预测模型，以提供预测。作为应用程序，预测模型用于模型预测控制框架中，以确保向下坡道有限的分布式能源资源协调方案的最佳跟踪性能。结果为AGC信号的性质提供了宝贵的见解，并指示了这些模型复制其行为的有效性。
The performance of frequency regulating units for automatic generation control (AGC) of power systems depends on their ability to track the AGC signal accurately. In addition, representative models and advanced analysis and analytics can yield forecasts of the AGC signal that aids in controller design. In this paper, time-series analyses are conducted on an AGC signal, specifically the PJM Reg-D, and using the results, a statistical model is derived that fairly accurately captures its second moments and saturated nature, as well as a time-series-based predictive model to provide forecasts. As an application, the predictive model is used in a model predictive control framework to ensure optimal tracking performance of a down ramp-limited distributed energy resource coordination scheme. The results provide valuable insight into the properties of the AGC signal and indicate the effectiveness of these models in replicating its behavior.
