@inproceedings{LliuyaccBlasOlavNybergIreshikaetal.2022,
  author    = {Ruben Lliuyacc-Blas and Svein Olav Nyberg and Muhandiram Arachchige Subodha Tharangi Ireshika and Mohan Lal Kolhe and Peter Kepplinger},
  title     = {PV hosting capacity estimation in low voltage feeders through bayesian statistical inference},
  series = {2022 12th International Conference on Power, Energy and Electrical Engineering (CPEEE). February 25-27, 2022. Shiga, Japan},
  publisher = {IEEE},
  address   = {Piscataway, NJ},
  isbn      = {978-1-6654-2049-5},
  doi       = {10.1109/CPEEE54404.2022.9738661},
  pages     = {250 -- 255},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}