Dynamic State Estimation for The Low-Carbon Integrated Electricity-Heat-Gas System

Abstract

The integrated energy system (IES) concept has been proposed in recent years to efficiently lower the whole system’s operation cost and carbon emission. The uncertainty and volatility of various sources pose challenges to identifying and maintaining the normal state of the IES during daily operation. This paper proposes a dynamic state estimation model for the integrated electricity-heat-gas system based on the Kalman Filter. By applying the finite difference method, the partial differential equations that describe the dynamic characteristics of gas and heat are transformed into a set of algebraic equations. Then, the discrete-time system equations of IES are developed. Finally, the Kalman filter is applied to establish the dynamic state estimation model of IES. Simulation results verify the effectiveness of the proposed method.