Stochastic Synthetic Data Generation for Electric Net Load and Its Application

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dc.contributor.authorLiu, Mengwei
dc.contributor.authorReed, Patrick
dc.contributor.authorAnderson, C. Lindsay
dc.date.accessioned2020-12-24T19:38:42Z
dc.date.available2020-12-24T19:38:42Z
dc.date.issued2021-01-05
dc.description.abstractThe 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.
dc.format.extent11 pages
dc.identifier.doi10.24251/HICSS.2021.383
dc.identifier.isbn978-0-9981331-4-0
dc.identifier.urihttp://hdl.handle.net/10125/70998
dc.language.isoEnglish
dc.relation.ispartofProceedings of the 54th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDistributed, Renewable, and Mobile Resources
dc.subjectseasonal detrending technique
dc.subjectstatistical validation
dc.subjectsynthetic net load data generation
dc.subjecttemporal correlation
dc.titleStochastic Synthetic Data Generation for Electric Net Load and Its Application
prism.startingpage3147

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