A Review on Machine Learning Models in Forecasting of Virtual Power Plant Uncertainties

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dc.contributor.authorDogan, Ahmet
dc.contributor.authorCidem Dogan, Demet
dc.date.accessioned2025-02-24T17:18:44Z
dc.date.available2025-02-24T17:18:44Z
dc.date.issued2023
dc.departmentNuh Naci Yazgan
dc.description.abstractThe penetration rates of renewable sources and energy storage systems in the energy market have risen considerably due to environmental and economic concerns. In addition, new types of loads such as electric vehicle charging are added to the grid recently. Inherent uncertainty of renewable generation and new type of loads make the power grid more complex and difficult to manage from economic and technical aspects. Virtual power plant (VPP) is a key concept of future smart grid integrating a variety of power sources, controllable loads, and storage devices. VPP environment aims to enhance the stability of the grid and maximize the revenue. Achieving these objectives mostly depends on the precise forecasting of three major uncertainties; renewable generation, load demand and electricity price. On the other side, machine learning (ML) models are quite efficient for complex uncertainties with large scale dataset compared to traditional approaches. In this paper, mostly employed ML models for forecasting VPP uncertainties are analyzed. Firstly, VPP components and operation of the system are explained. Then, preprocessing techniques, ML methods and performance evaluation criteria for forecasting approaches are presented. Contributions and limitations of recent works are critically discussed and separately tabulated. Finally, several future research opportunities are released at the conclusion of this paper.
dc.identifier.doi10.1007/s11831-022-09860-2
dc.identifier.endpage2103
dc.identifier.issn1134-3060
dc.identifier.issn1886-1784
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85143138066
dc.identifier.scopusqualityQ1
dc.identifier.startpage2081
dc.identifier.urihttps://doi.org/10.1007/s11831-022-09860-2
dc.identifier.urihttps://hdl.handle.net/20.500.14440/828
dc.identifier.volume30
dc.identifier.wosWOS:000892908300001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofArchives of Computational Methods in Engineering
dc.relation.publicationcategoryDiğer
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250201
dc.subjectFed Induction Generator
dc.subjectLow-Voltage Ride
dc.subjectEnergy-Conversion Systems
dc.subjectGrey Wolf Optimizer
dc.subjectO Mppt Algorithm
dc.subjectWind Turbine
dc.subjectFrequency-Control
dc.subjectControl Strategy
dc.subjectPoint Tracking
dc.subjectThrough Enhancement
dc.titleA Review on Machine Learning Models in Forecasting of Virtual Power Plant Uncertainties
dc.typeReview

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