Title: A Data-Driven Framework for Assessing Cold Load Pick-up Demand in Service Restoration,
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.