Robust Dynamic State Estimator of Integrated Energy Systems based on Natural Gas Partial Differential Equations

TL;DR

This paper introduces a robust dynamic state estimation method for integrated natural gas and electric power systems using a Kalman filter that incorporates gas physical models and handles bad data effectively.

Contribution

The paper presents a novel coupled gas-power state estimation approach that improves accuracy and robustness over traditional separate methods by integrating physical models and bad data handling.

Findings

Accurately estimates dynamic states under various measurement errors.

Outperforms separate estimation methods in filtering performance.

Remains robust in the presence of bad measurement data.

Abstract

The reliability and precision of dynamic database are vital for the optimal operating and global control of integrated energy systems. One of the effective ways to obtain the accurate states is state estimations. A novel robust dynamic state estimation methodology for integrated natural gas and electric power systems is proposed based on Kalman filter. To take full advantage of measurement redundancies and predictions for enhancing the estimating accuracy, the dynamic state estimation model coupling gas and power systems by gas turbine units is established. The exponential smoothing technique and gas physical model are integrated in Kalman filter. Additionally, the time-varying scalar matrix is proposed to conquer bad data in Kalman filter algorithm. The proposed method is applied to an integrated gas and power systems formed by GasLib-40 and IEEE 39-bus system with five gas turbine units. The simulating results show that the method can obtain the accurate dynamic states under three different measurement error conditions, and the filtering performance are better than separate estimation methods. Additionally, the proposed method is robust when the measurements experience bad data.