Öppna denna publikation i ny flik eller fönster >>2023 (Engelska)Ingår i: Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO / [ed] Gini G., Nijmeijer H., Filev D., SciTePress, 2023, Vol. 1, s. 214-220Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]
Optimal Power Flow is a central tool for power system operation and planning. Given the substantial rise in intermittent power and shorter time windows in electricity markets, there’s a need for fast and efficient solutions to the Optimal Power Flow problem. With this in consideration, this paper propose an unsupervised deep learning approach to approximate the optimal solution of Optimal Power Flow problems. Once trained, deep learning models benefit from being several orders of magnitude faster during inference compared to conventional non-linear solvers.
Ort, förlag, år, upplaga, sidor
SciTePress, 2023
Serie
Proceedings of the International Conference on Informatics in Control, Automation and Robotics, ISSN 2184-2809
Nationell ämneskategori
Reglerteknik
Forskningsämne
Fysik, Elektroteknik alt Electrical engineering
Identifikatorer
urn:nbn:se:lnu:diva-129963 (URN)10.5220/0012187400003543 (DOI)2-s2.0-85181567286 (Scopus ID)
Konferens
20th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2023, Rome, 13-15 November 2023
2024-06-052024-06-052024-06-28Bibliografiskt granskad