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PV hosting capacity estimation in low voltage feeders through bayesian statistical inference

  • PV hosting capacity provides utilities the knowledge of the maximum amount of solar installations possible to accommodate in low voltage grids such that no operational problems arise. As the quantification of the hosting capacity requires data collection, grid modelling, and often time-consuming simulations, simplified estimations for large-scale applications are of interest. In this paper, Bayesian statistical inference is applied to estimate the hosting capacities of more than 5000 real feeders in Austria. The results show that the hosting capacity of 95% of the total feeders can be estimated with a mean error below 20% by only having knowledge of a random sample of 5%. Moreover, the hosting capacity estimation at a regional level shows a maximum error below 9%, also relying on a random sample of 5% of the total feeders. Furthermore, the approach proposed provides a methodology to assess new parameters aiming to improve the accuracy of the hosting capacity estimation at a feeder level.

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Metadaten
Author:Ruben Lliuyacc-Blas, Svein Olav Nyberg, Subodha Tharangi Ireshika Muhandiram ArachchigeORCiD, Mohan Lal Kolhe, Peter KepplingerORCiD
DOI:https://doi.org/10.1109/CPEEE54404.2022.9738661
ISBN:978-1-6654-2049-5
Parent Title (English):2022 12th International Conference on Power, Energy and Electrical Engineering (CPEEE). February 25-27, 2022. Shiga, Japan
Publisher:IEEE
Place of publication:Piscataway, NJ
Document Type:Conference Proceeding
Language:English
Year of publication:2022
Release Date:2022/06/15
First Page:250
Last Page:255
Organisationseinheit:Forschung / Forschungszentrum Energie
DDC classes:600 Technik, Medizin, angewandte Wissenschaften
JEL-Classification:C Mathematical and Quantitative Methods
Open Access?:nein
Peer review:wiss. Beitrag, peer-reviewed
Publicationlist:Kepplinger, Peter
Muhandiram Arachchige, Subodha Ireshika
Lliuyacc Blas, Ruben Ronald