A Data-Driven Framework for Assessing Cold Load Pick-up Demand in Service Restoration

TL;DR

This paper introduces a data-driven framework utilizing smart meter data to assess cold load pick-up demand during service restoration, combining feeder-level modeling and customer-level probabilistic analysis.

Contribution

It presents a novel two-layer framework that integrates auto-regression and Gaussian Mixture Models for detailed CLPU demand assessment using high-resolution smart meter data.

Findings

Framework accurately estimates CLPU demand ratios

Method verified with real outage and smart meter data

Provides granular insights into customer demand increases

Abstract

Cold load pick-up (CLPU) has been a critical concern to utilities. Researchers and industry practitioners have underlined the impact of CLPU on distribution system design and service restoration. The recent large-scale deployment of smart meters has provided the industry with a huge amount of data that is highly granular, both temporally and spatially. In this paper, a data-driven framework is proposed for assessing CLPU demand of residential customers using smart meter data. The proposed framework consists of two interconnected layers: 1) At the feeder level, a nonlinear auto-regression model is applied to estimate the diversified demand during the system restoration and calculate the CLPU demand ratio. 2) At the customer level, Gaussian Mixture Models (GMM) and probabilistic reasoning are used to quantify the CLPU demand increase. The proposed methodology has been verified using real smart meter data and outage cases.