Dynamic State Estimation for Integrated Electricity-gas Systems Based on Kalman Filter

In recent years, integrated electricity-gas systems (IEGSs) have attracted widespread attention. The unified scheduling and control of the IEGS depends on high-precision operating data. To this end, it is necessary to establish an appropriate state estimation (SE) model for IEGS to filter the raw measured data. Considering that power systems and natural gas systems have different time scales and sampling periods, this paper proposes a dynamic state estimation (DSE) method based on a Kalman filter that can consider the dynamic characteristics of natural gas pipelines. First, the standardized state transition equations for the gas system are developed by applying the finite difference method to the partial differential equations (PDEs) of the gas system; then the DSE model for IEGS is formulated based on a Kalman filter; also, the measurements from the electricity system and the gas system with different sampling periods are fused to ensure the observability of DSE by using the interpolation method. The IEEE 39-bus electricity system and the 18-nodes Belgium gas system are integrated as the test systems. Simulation results verify the proposed method's accuracy and calculation efficiency.

Automated summary

This paper proposes a dynamic state estimation method based on a Kalman filter for the unified scheduling and control of integrated electricity-gas systems. By considering the dynamic characteristics of natural gas pipelines and fusing measurements with different sampling periods using the interpolation method, high-precision operation data filtering is achieved.