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Stochastic Synthetic Data Generation for Electric Net Load and Its Application

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Title:Stochastic Synthetic Data Generation for Electric Net Load and Its Application
Authors:Liu, Mengwei
Reed, Patrick
Anderson, C. Lindsay
Keywords:Distributed, Renewable, and Mobile Resources
seasonal detrending technique
statistical validation
synthetic net load data generation
temporal correlation
Date Issued:05 Jan 2021
Abstract:The increasing integration of renewable energy in electric power systems focuses attention on realistic representation of ”net load” because it aggregates the information from both demand and the renewable supply side; net load is the remaining demand that must be met by non-renewable resources. However, the net load data is not readily accessible because of cost, privacy, and security concerns. Furthermore, even if historical data is available, multiple stochastic scenarios are often needed for a wide range of power system applications. To address these issues, this paper proposes a stochastic synthetic net load profile generation approach. A seasonal detrending technique is combined with the modified Fractional Gaussian Noise method to deal with the complex multi-periodic seasonal trends in the net load profile. A thorough statistical validation and temporal correlation check are performed to show the quality of the synthetic data. The benefits of the synthetic data are demonstrated by a microgrid energy management problem.
Pages/Duration:11 pages
URI:http://hdl.handle.net/10125/70998
ISBN:978-0-9981331-4-0
DOI:10.24251/HICSS.2021.383
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Distributed, Renewable, and Mobile Resources


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