Abstract
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.
Highlights
Incresed levels of distributed photovoltaic (PV) generation determine higher reserve requirements at the system level and violations of voltage and line ampacity constraints in distribution systems during peak production hours [1], [2]
Four estimation algorithms are proposed and compared: the first assumes that the variability in the aggregated power flow measurements are mostly given by variations of the PV generation, the second and third leverage a model of the demand to improve estimation performance, and the fourth assumes that there is a certain frequency range in which the aggregated power flow measurement is dominated by PV generation components
The algorithms are tested with measurements from a real-life setup of four houses with monitored rooftop PV plants and grid-connected battery systems, enabling the testing of estimation performance even when the demand is correlated with PV generation
Summary
Incresed levels of distributed photovoltaic (PV) generation determine higher reserve requirements at the system level and violations of voltage and line ampacity constraints in distribution systems during peak production hours [1], [2]. A requirement for the implementation of those strategies is the availability of real-time production measurements from PV facilities. These are useful to train data-driven prediction models As an alternative to direct monitoring of PV systems, we consider in this paper the problem of disaggregating PV generation from the aggregated active power measurements of a group of prosumers. The algorithms are tested with measurements from a real-life setup of four houses with monitored rooftop PV plants and grid-connected battery systems, enabling the testing of estimation performance even when the demand is correlated with PV generation
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