A Novel Non-Intrusive Framework for Real-Time Disaggregation of Behind-the-Meter Solar Generation from Smart Meter Data

Abstract

As the number of behind-the-meter (BTM) photovoltaic (PV) modules installed in residential premises increases, it is important to develop a non-intrusive framework for the real-time assessment of BTM PV generation from the smart meter data of the end users. This framework not only enhances the observability of residential premises but also enables the electric utility to implement various distribution grid operation strategies such as demand response programs, load forecasting, and electric energy procurement, among others. This work proposes a novel non-intrusive approach based on the Universal Adaptive Stabilization (UAS) algorithm to assess the generation of BTM PV modules in real-time using smart meter data obtained from residential customers. The proposed approach is characterized by its simplicity, robustness, and fully unsupervised operation without the need for complicated and detailed system dynamics. The accuracy and convergence of the estimated BTM solar PV generation and residential load consumption to their actual values are proved by a detailed mathematical justification. Further, the effectiveness of the proposed framework is evaluated by comparing it against several advanced algorithms using a publicly available dataset. The results of the evaluation indicate that the proposed framework outperforms existing algorithms by providing more precise and accurate estimates.

Keywords: Behind-the-meter solar generation, power distribution systems, smart metering, universal adaptive stabilization