A Robust State Estimator for Integrated Electrical and Heating Networks

State estimation has been widely used in power system energy management systems. However, the application of state estimation for integrated electrical and heating networks (IEHNs) remains in a preliminary stage. This paper addresses this issue by proposing a robust state estimation method for IEHNs based on the weighted least absolute value in conjunction with equality constraints. The robust performance of the proposed estimator resolves the disadvantages of existing combined state estimators. A heating load pseudo-measurement model based on an artificial neural network and real-time measurements is developed to suppress the negative effects of measurements that contain bad data, and thereby ensure an adequate basis for accurate state estimation and guarantee the observability of the heating network. The effectiveness of the proposed state estimation method and its robustness to bad data are verified by comparison with the performance of the conventional largest normalized residual test based on the equality-constrained weighted least squares state estimation of IEHNs in numerical simulations employing a simple IEHN and/or the Barry Island IEHN as case studies.