  We consider the problem of estimating the unobserved amount of photovoltaic
(PV) generation and demand in a power distribution network starting from
measurements of the aggregated power flow at the point of common coupling (PCC)
and local global horizontal irradiance (GHI). The estimation principle relies
on modeling the PV generation as a function of the measured GHI, enabling the
identification of PV production patterns in the aggregated power flow
measurements. Four estimation algorithms are proposed: the first assumes that
variability in the aggregated PV generation is given by variations of PV
generation, the next two use a model of the demand to improve estimation
performance, and the fourth assumes that, in a certain frequency range, the
aggregated power flow is dominated by PV generation dynamics. These algorithms
leverage irradiance transposition models to explore several azimuth/tilt
configurations and explain PV generation patterns from multiple plants with
non-uniform installation characteristics. Their estimation performance is
compared and validated with measurements from a real-life setup including 4
houses with rooftop PV installations and battery systems for PV
self-consumption.
