A new IGDT-based robust model for day-ahead scheduling of smart power system integrated with compressed air energy storage and dynamic rating of transformers and lines

Autor: Aghdam, Elyar Asadzadeh ^a, Moslemi, Sahar ^b, Nakisaee, Mohammad Sadegh ^c, Fakhrooeian, Mahan ^d, Al-Hassanawy, Ali Jawad Kadhim ^e, Masali, Milad Hadizadeh ^f, Seyyedi, Abbas Zare Ghaleh ^{g, ⁎}

Publikováno v: In Journal of Energy Storage 1 January 2025 105

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SmarTrafo: A Probabilistic Predictive Tool for Dynamic Transformer Rating

Autor: Guido Carpinelli, Antonio Bracale, Pasquale De Falco, Pierluigi Caramia

Publikováno v: IEEE Transactions on Power Delivery. 36:1619-1630

Operating electrical networks by Dynamic Transformer Rating (DTR) unlocks the capacity of grids and allows to increase the exploitation of distributed generation and renewables. However, there is risk associated with the operation of transformers by

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::60765ad97a511644552cc3257a70fc40
https://doi.org/10.1109/tpwrd.2020.3012180

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Grey-box modeling for hot-spot temperature prediction of oil-immersed transformers in power distribution networks

Autor: E.M.V. Blomgren, F. D’Ettorre, O. Samuelsson, M. Banaei, R. Ebrahimy, M.E. Rasmussen, N.H. Nielsen, A.R. Larsen, H. Madsen

Publikováno v: Blomgren, E M V, D'Ettorre, F, Samuelsson, O, Banaei, M, Ebrahimy, R, Rasmussen, M E, Nielsen, N H, Larsen, A R & Madsen, H 2023, ' Grey-box modeling for hot-spot temperature prediction of oil-immersed transformers in power distribution networks ', Sustainable Energy, Grids and Networks, vol. 34, 101048 . https://doi.org/10.1016/j.segan.2023.101048

Power transformers are one of the most costly assets in power grids. Due to increasing electricity demand and levels of distributed generation, they are more and more often loaded above their rated limits. Transformer ratings are traditionally set as

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::abcdc6accf5a796fe3b76eeb572d3110
https://doi.org/10.1016/j.segan.2023.101048