Multi-Time Scale State Estimation for Integrated Electric–Gas–Hydrogen Systems

The injections of hydrogen into gas pipelines contribute to the accommodation of intermittent renewables and decarbonization of natural gas. However, the hydrogen-gas blending results in operational risk for integrated electric-gas–hydrogen systems (IEGHSs). As such, it is desirable for system operators to monitor/estimate real-time operating conditions of IEGHS. This article develops a segmented cubature Kalman filter (KF) state estimation method based on a dynamic model of an IEGHS network. Our approach considers the impact of hydrogen injection on density, compressibility factor, and hydraulic friction coefficient and develops a dynamic state estimation model that incorporates pressure-flow and hydrogen advection-diffusion dynamics. Moreover, a segmented state estimation algorithm based on the unified theory of time series is used, which can address differing response times of the electric grid and gas network. Namely, our model allows tailored time scales for each energy system. The adaptive cubature KF (ACKF) algorithm is used to estimate the state of IEGHS. Simulations on a test system comprising 24 electrical buses and 20 gas buses demonstrate the accuracy and efficiency of the proposed approach.