  Customer-level rooftop photovoltaic (PV) has been widely integrated into
distribution systems. In most cases, PVs are installed behind-the-meter (BTM),
and only the net demand is recorded. Therefore, the native demand and PV
generation are unknown to utilities. Separating native demand and solar
generation from net demand is critical for improving grid-edge observability.
In this paper, a novel approach is proposed for disaggregating customer-level
BTM PV generation using low-resolution but widely available hourly smart meter
data. The proposed approach exploits the strong correlation between monthly
nocturnal and diurnal native demands and the high similarity among PV
generation profiles. First, a joint probability density function (PDF) of
monthly nocturnal and diurnal native demands is constructed for customers
without PVs, using Gaussian mixture modeling (GMM). Deviation from the
constructed PDF is utilized to probabilistically assess the monthly solar
generation of customers with PVs. Then, to identify hourly BTM solar generation
for these customers, their estimated monthly solar generation is decomposed
into an hourly timescale; to do this, we have proposed a maximum likelihood
estimation (MLE)-based technique that utilizes hourly typical solar exemplars.
Leveraging the strong monthly native demand correlation and high PV generation
similarity enhances our approach's robustness against the volatility of
customers' hourly load and enables highly accurate disaggregation. The proposed
approach has been verified using real native demand and PV generation data.
