  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 meters. Disaggregating BTM PV generation from net demand is
critical to utilities for enhancing grid-edge observability. In this paper, a
data-driven approach is proposed for BTM PV generation disaggregation using
solar and demand exemplars. First, a data clustering procedure is developed to
construct a library of candidate load/solar exemplars. To handle the volatility
of BTM resources, a novel game-theoretic learning process is proposed to
adaptively generate optimal composite exemplars using the constructed library
of candidate exemplars, through repeated evaluation of disaggregation
residuals. Finally, the composite native demand and solar exemplars are
employed to disaggregate solar generation from net demand using a
semi-supervised source separator. The proposed methodology has been verified
using real smart meter data and feeder models.
