Demand Response Using Disturbance Estimation-Based Kalman Filtering for the Frequency Control

Abstract

Demand response (DR) has a great potential for stabilizing the frequency of power systems. However, the performance is limited by the accuracy of the frequency detection, which is affected by measurement disturbances. To overcome this problem, this paper proposes a disturbance estimation-based Kalman filtering method, which is utilized for the frequency control. By using the rate of change of frequency (RoCoF), the Kalman filtering method can estimate the state of the ON/OFF loads well. In this way, the influence of detection error can be reduced, and the DR performance can be improved. Test results show that the proposed disturbance estimation-based Kalman filtering method has a higher accuracy of frequency detection than existing methods (such as the low-pass filter method) and therefore improves the frequency control performance of DR.