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With the emergence of cost effective battery storage and the decline in the solar photovoltaic (PV) levelized cost of energy (LCOE), the number of behind-the-meter solar PV systems is expected to increase steadily. The ability to estimate solar generation from these latent systems is crucial for a range of applications, including distribution system planning and operation, demand response, and non-intrusive load monitoring (NILM). This paper investigates the problem of disaggregating solar generation from smart meter data when historical disaggregated data from the target home is unavailable, and deployment characteristics of the PV system are unknown. The proposed approach entails inferring the physical characteristics from smart meter data and disaggregating solar generation using an iterative algorithm. This algorithm takes advantage of solar generation data (aka proxy measurements) from a few sites that are located in the same area as the target home, and solar generation data synthesized using a physical PV model. We evaluate our methods with 4 different proxy settings on around 160 homes in the United States and Australia, and show that the solar disaggregation accuracy is improved by 32.31% and 15.66% over two state-of-the-art methods using only one real proxy along with three synthetic proxies. Furthermore, we demonstrate that using the disaggregated home load rather than the net load data could improve the overall accuracy of three popular NILM methods by at least 22%.
Rooftop solar photovoltaic (PV) power generator is a widely used distributed energy resource (DER) in distribution systems. Currently, the majority of PVs are installed behind-the-meter (BTM), where only customers net demand is recorded by smart mete
Large solar power stations usually locate in remote areas and connect to the main grid via a long transmission line. Energy storage unit is deployed locally with the solar plant to smooth its output. Capacities of the grid-connection transmission lin
The availability of large datasets is crucial for the development of new power system applications and tools; unfortunately, very few are publicly and freely available. We designed an end-to-end generative framework for the creation of synthetic bus-
A system of a systems approach that analyzes energy and water systems simultaneously is called energy-water nexus. Neglecting the interrelationship between energy and water drives vulnerabilities whereby limits on one resource can cause constraints o
We consider some crucial problems related to the secure and reliable operation of power systems with high renewable penetrations: how much reserve should we procure, how should reserve resources distribute among different locations, and how should we