A Data-Driven Framework for Assessing Cold Load Pick-Up Demand in Service Restoration Article Swipe
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· 2019
· Open Access
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· DOI: https://doi.org/10.1109/tpwrs.2019.2922333
· OA: W2897669159
Cold load pick-up (CLPU) has been a critical concern to utilities.\nResearchers and industry practitioners have underlined the impact of CLPU on\ndistribution system design and service restoration. The recent large-scale\ndeployment of smart meters has provided the industry with a huge amount of data\nthat is highly granular, both temporally and spatially. In this paper, a\ndata-driven framework is proposed for assessing CLPU demand of residential\ncustomers using smart meter data. The proposed framework consists of two\ninterconnected layers: 1) At the feeder level, a nonlinear auto-regression\nmodel is applied to estimate the diversified demand during the system\nrestoration and calculate the CLPU demand ratio. 2) At the customer level,\nGaussian Mixture Models (GMM) and probabilistic reasoning are used to quantify\nthe CLPU demand increase. The proposed methodology has been verified using real\nsmart meter data and outage cases.\n
