Multi-source-data state estimation for integrated electricity–heat-building networks

Abstract

Building heating systems (BHS) play a major role in integrated electricity and heat systems (IEHS). To capture the real-time dynamic operating state of IEHS based on multi-source measurement data, this paper develops a comprehensive state estimation (SE) framework for IEHS with BHS fleets. First, a thermo-electrical SE model integrating BHS, electric power grid (EPG), and district heating network (DHN) is developed. Then, to jointly estimate the state variables of IEHS with different time scales of dynamics, a holistic SE algorithm is proposed based on the partial equivalence between the Weighted Least Squares (WLS) estimation problem and the correction stage of the Iterative Extended-Kalman Filter (IEKF). It also includes a data anomaly processing algorithm to combat data corruptions from sensing and communication processes. Simulation results show that the proposed framework can accurately track the thermo-electrical states of the system both in the presence of measurement noise and anomalies during various dynamics in IEHS.