Effective identification of distributed energy resources using smart meter net‐demand data

Abstract International policies and targets to globally reduce carbon dioxide emissions have contributed to increasing penetration of distributed energy resources (DER) in low‐voltage distribution networks. The growth of technologies such as rooftop photovoltaic (PV) systems and electric vehicles (EV) has, to date, not been rigorously monitored and record keeping is deficient. Non‐intrusive load monitoring (NILM) methods contribute to the effective integration of clean technologies within existing distribution networks. In this study, a novel NILM method is developed for the identification of DER electrical signatures from smart meter net‐demand data. Electrical profiles of EV and PV systems are allocated within aggregated measurements including conventional electrical appliances. Data from several households in the United States are used to train and test classification and regression models. The usage of conventional machine learning techniques provides the proposed algorithm with fast processing times and low system complexity, key factors needed to differentiate highly variable DER power profiles from other loads. The results confirm the effectiveness of the proposed methodology to individually classify DER with performance metrics of 96% for EV and 99% for PV. This demonstrates the potential of the proposed method as an embedded function of smart meters to increase observability in distribution networks.