A Blind Source Separation Framework to Monitor Sectoral Power Demand from Grid-Scale Load Measurements

TL;DR

This paper introduces a blind source separation method using constrained non-negative matrix factorization to estimate sectoral power demand from grid measurements, aiding renewable integration.

Contribution

It develops a novel LCNMF approach that incorporates prior information for sectoral load disaggregation at large grid scales.

Findings

Successfully disaggregated Italian load data into residential, services, and industrial sectors.

Estimated sectoral consumption aligns with official statistics.

Method is applicable to various grid scales without requiring detailed measurements.

Abstract

As demand-side flexibility becomes increasingly necessary to integrate variable renewable energy, understanding electricity demand composition across different grid levels is essential. However, at regional and national scales, visibility into the relative contributions of different consumer categories remains limited due to the complexity and cost of collecting end-use consumption data. To address this challenge, we propose a blind source separation framework to disaggregate open-access high-voltage grid load measurements into sectoral contributions. The approach relies on a constrained variant of non-negative matrix factorization, termed linearly-constrained non-negative matrix factorization (LCNMF), which allows prior information to be incorporated as linear constraints on the factor matrices, thereby providing weak supervision of the separation process. The framework is evaluated using Italian national load data from 2021 to 2023. Results demonstrate the identifiability of residential, services, and industrial load components and provide monthly sectoral consumption estimates consistent with reported statistics. The proposed method is generalizable and applicable to load disaggregation problems across multiple grid scales where disaggregated measurements are unavailable.