Graph Convolutional Networks for probabilistic power system operational planning
Sheikh-Mohamed, Yasmin Bashir; Jakobsen, Sigurd Hofsmo; Bødal, Espen Flo; Haugseth, Fredrik Marinius; Kiel, Erlend Sandø; Riemer-Sørensen, Signe
Chapter, Peer reviewed
Accepted version
Permanent lenke
https://hdl.handle.net/11250/3088197Utgivelsesdato
2023Metadata
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Originalversjon
2023 IEEE Belgrade PowerTechSammendrag
Probabilistic operational planning of power systems usually requires computationally intensive and time consuming simulations. The method presented in this paper provides a time efficient alternative to predict the socio-economic cost of system operational strategies using graph convolutional networks. It is intended for fast screening of operational strategies for the purpose of operational planning. It can also be used as a proxy for operational planning that can be used in long term development studies. The performance of the model is demonstrated on a network inspired by the Nordic power system. Graph Convolutional Networks for probabilistic power system operational planning