Dynamic State Estimation for Integrated Natural Gas and Electric Power Systems

TL;DR

This paper introduces a novel dynamic state estimation method for integrated natural gas and electric power systems, utilizing a Kalman filter-based linear model to accurately predict system states amid measurement errors.

Contribution

It develops a coupling model of gas and power systems and applies a Kalman filter-based DSE approach to improve state estimation accuracy in integrated systems.

Findings

Accurate dynamic state estimation achieved under various measurement errors.

The method effectively integrates gas pipeline and power system models.

Simulation results validate the approach's robustness and precision.

Abstract

A dynamic state estimation method of integrated natural gas and electric power systems (IGESs) in proposed. Firstly, the coupling model of gas pipeline networks and power systems by gas turbine units (GTUs) is established. Secondly, the Kalman filter based linear DSE model for the IGES is built. The gas density and mass flow rate, as well as the real and imaginary parts of bus voltages are taken as states, which are predicted by the linearized fluid dynamic equations of gases and exponential smoothing techniques. Boundary conditions of pipeline networks are used as supplementary constraints in the system model. At last, the proposed method is applied to an IGES including a 30-node pipeline network and IEEE 39-bus system coupled by two GTUs. Two indexes are used to evaluate the DSE performance under three measurement error conditions, and the results show that the DSE can obtain the accurate dynamic states in different conditions.