Real-Time Estimation Frameworks for Feeder-Level Load Disaggregation and PEVs’ Charging Behavior Characteristics Extraction

In this article, a model-based real-time approach is proposed to disaggregate a feeder-level load and estimate the total energy of plug-in electric vehicles (PEVs), considering the controlled charging mode and the vehicle-to-grid capability of PEVs. To this end, aggregate demand of load categories participating at the feeder-head as well as the total energy of PEVs are analytically modeled. Then, the state-space representation of the system according to the mentioned models and their relations is proposed. Finally, a Kalman filter-based method is applied to disaggregate the feeder-level load into the aggregate demand of load categories and estimate the total energy of PEVs in real time. The accuracy and complexity of the proposed method are compared with two model-free methods, i.e., a nonlinear autoregressive with exogenous inputs-based shallow learning model and a long short-term memory-based deep learning approach, by using real data. They employ distribution substation measurements along with charging data of a very small subset of PEVs. The comparison results indicate that although the artificial neural network-based methods can effectively represent the nonlinear behavior of the feeder-level load and its components, the Kalman filter-based method significantly improves the PEVs’ total energy estimation by taking into account modeling and measurement uncertainties.