Forecasting-aided state estimation of integrated energy systems based on improved particle filter

Abstract

Efficient and accurate state estimation is the basis for the safety and stability of the integrated energy system (IES). Particle filter has high precision and strong adaptability to nonlinear systems,and it has been applied to state estimation of power systems. To improve the precision of state estimation in IES,a forecasting-aided state estimation method based on improved particle filter is proposed. Firstly,a regional IES model including an electricity-heat-gas network is constructed. Secondly,the particle filter algorithm is applied to the electricity-heat-gas network. The prediction step of the particle filter is improved because of the tracking error problem of traditional particle filtering algorithm,which is based on particle filter theory. Finally,the improved particle filter algorithm is verified by using the classical IES example. The results show that this method can effectively solve the tracking error problem of the traditional particle filter algorithm,which can improve the precision of state estimation in IES.

Keywords

• integrated energy system
• state estimation
• particle filter algorithm
• electricity-heat-gas network
• tracking error
• forecasting-aided