# A Data-Driven Game-Theoretic Approach for Behind-the-Meter PV Generation Disaggregation

## TL;DR

This paper introduces a data-driven, game-theoretic method for disaggregating behind-the-meter PV generation from net demand data, improving grid observability with real smart meter data.

## Contribution

It develops a novel game-theoretic learning process to adaptively generate optimal exemplars for PV disaggregation, advancing beyond traditional static methods.

## Key findings

- Effective disaggregation demonstrated with real smart meter data
- Improved accuracy over existing methods
- Robustness to PV generation volatility

## Abstract

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.