Show simple item record

dc.contributor.authorHansen, Jonas Berg
dc.contributor.authorAnfinsen, Stian Normann
dc.contributor.authorBianchi, Filippo Maria
dc.date.accessioned2022-12-09T13:46:18Z
dc.date.available2022-12-09T13:46:18Z
dc.date.issued2022-08-01
dc.description.abstractWe propose an end-to-end framework based on a Graph Neural Network (GNN) to balance the power flows in energy grids. The balancing is framed as a supervised vertex regression task, where the GNN is trained to predict the current and power injections at each grid branch that yield a power flow balance. By representing the power grid as a line graph with branches as vertices, we can train a GNN that is accurate and robust to changes in topology. In addition, by using specialized GNN layers, we are able to build a very deep architecture that accounts for large neighborhoods on the graph, while implementing only localized operations. We perform three different experiments to evaluate: i) the benefits of using localized rather than global operations and the tendency of deep GNN models to oversmooth the quantities on the nodes; ii) the resilience to perturbations in the graph topology; and iii) the capability to train the model simultaneously on multiple grid topologies and the consequential improvement in generalization to new, unseen grids. The proposed framework is efficient and, compared to other solvers based on deep learning, is robust to perturbations not only to the physical quantities on the grid components, but also to the topology.en_US
dc.identifier.citationHansen, Anfinsen, Bianchi. Power Flow Balancing With Decentralized Graph Neural Networks. IEEE Transactions on Power Systems. 2022en_US
dc.identifier.cristinIDFRIDAID 2070514
dc.identifier.doi10.1109/TPWRS.2022.3195301
dc.identifier.issn0885-8950
dc.identifier.issn1558-0679
dc.identifier.urihttps://hdl.handle.net/10037/27777
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.journalIEEE Transactions on Power Systems
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.titlePower Flow Balancing With Decentralized Graph Neural Networksen_US
dc.type.versionacceptedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


File(s) in this item

Thumbnail

This item appears in the following collection(s)

Show simple item record