Disaggregating Customer-level Behind-the-Meter PV Generation Using Smart Meter Data and Solar Exemplars

TL;DR

This paper introduces a novel method to disaggregate behind-the-meter PV generation from net demand data using smart meter data and solar exemplars, improving grid-edge observability.

Contribution

It develops a probabilistic approach combining Gaussian mixture modeling and maximum likelihood estimation to accurately estimate customer-level PV generation from low-resolution data.

Findings

High accuracy in PV disaggregation demonstrated with real data

Robustness against load volatility achieved

Effective use of demand correlation and PV profile similarity

Abstract

Customer-level rooftop photovoltaic (PV) has been widely integrated into distribution systems. In most cases, PVs are installed behind-the-meter (BTM), and only the net demand is recorded. Therefore, the native demand and PV generation are unknown to utilities. Separating native demand and solar generation from net demand is critical for improving grid-edge observability. In this paper, a novel approach is proposed for disaggregating customer-level BTM PV generation using low-resolution but widely available hourly smart meter data. The proposed approach exploits the strong correlation between monthly nocturnal and diurnal native demands and the high similarity among PV generation profiles. First, a joint probability density function (PDF) of monthly nocturnal and diurnal native demands is constructed for customers without PVs, using Gaussian mixture modeling (GMM). Deviation from the constructed PDF is utilized to probabilistically assess the monthly solar generation of customers with PVs. Then, to identify hourly BTM solar generation for these customers, their estimated monthly solar generation is decomposed into an hourly timescale; to do this, we have proposed a maximum likelihood estimation (MLE)-based technique that utilizes hourly typical solar exemplars. Leveraging the strong monthly native demand correlation and high PV generation similarity enhances our approach's robustness against the volatility of customers' hourly load and enables highly accurate disaggregation. The proposed approach has been verified using real native demand and PV generation data.