Asynchronous and Adaptive State Estimation of Integrated Electricity–Gas Energy Systems

Abstract

The combination of Internet of Things technology and power grid technology greatly strengthens the situational awareness in integrated energy systems. However, present development fails to consider the internal dynamic characteristics of the natural gas system. This is addressed in the present study by proposing an asynchronous distributed dynamic state estimation method for integrated electricity-gas energy systems. First, static state estimation based on a weighted least square method is applied to the electric power network, and an improved extended Kalman filter state estimation method based on a transient model is applied to the natural gas network. The asynchronous exchange of information between the two subsystems is addressed by applying an iterative update step correction to the dynamic state estimation of the natural gas network. Second, we propose an adaptive state estimation execution cycle adjustment method to track the state of the natural gas subsystem in real time under conditions of large disturbances in that subsystem. Application of the proposed algorithm to simulation case studies demonstrates that it can greatly improve the estimation accuracy and trajectory tracking performance of the state estimation process relative to conventional methods.