# Unsupervised Disaggregation of PhotoVoltaic Production from Composite Power Flow Measurements of Heterogeneous Prosumers

## TL;DR

This paper develops and compares four algorithms to estimate unobserved photovoltaic generation and demand from aggregated power flow data and irradiance measurements, validated with real-world data from a heterogeneous prosumer setup.

## Contribution

It introduces four novel estimation algorithms that leverage irradiance models and demand assumptions to disaggregate PV and demand in power flow measurements.

## Key findings

- Algorithms accurately estimate PV generation in real-world data.
- Using demand models improves disaggregation performance.
- PV generation patterns are explainable through irradiance and installation configurations.

## 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.