Congestion forecast framework based on probabilistic power flow and machine learning for smart distribution grids-Reference-Cited by-同舟云学术

Congestion forecast framework based on probabilistic power flow and machine learning for smart distribution grids

Published:2024-02 Issue: Volume:156 Page:109695
ISSN:0142-0615
Container-title:International Journal of Electrical Power & Energy Systems
language:en
Short-container-title:International Journal of Electrical Power & Energy Systems

Author:

Hernandez-Matheus Alejandro^ORCID,Berg Kjersti^ORCID,Gadelha Vinicius^ORCID,Aragüés-Peñalba Mònica^ORCID,Bullich-Massagué Eduard^ORCID,Galceran-Arellano Samuel^ORCID

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Reference50 articles.

1. A data-driven probabilistic power flow method based on convolutional neural networks;Wang;Int Trans Electr Energy Syst,2020

2. Coordination strategies for distribution grid congestion management in a multi-actor, multi-objective setting;Bach Andersen,2012

3. Distribution-level flexibility market for congestion management;Esmat;Energies,2018

4. Congestion management in power systems - A review;Pillay;Int J Electr Power Energy Syst,2015

5. Predicting transmission line congestion in energy systems with a high share of renewables;Staudt,2019

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