Prediction of Electric Vehicles Schedulable Capacity Based on Graph Convolution Network

Konferenz: PCIM Asia 2024 - International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management
28.08.2024-30.08.2024 in Shenzhen, China

doi:10.30420/566414067

Tagungsband: PCIM Asia 2024

Seiten: 5Sprache: EnglischTyp: PDF

Autoren:
Mao, Meiqin; Wu, Jixun; Yang, Cheng; Wang, Yuanyue; Zhu, Minglei; Hatziargyriou, Nikos

Inhalt:
Spatial-temporal prediction of the electric vehicles schedulable capacity is necessary for their integration into power grid management. This paper proposes a spatial-temporal prediction model based on graph convolution. Each charging station within a region is considered a node, the connection relationship is determined based on the spatial distance between charging stations, obtaining the charging station connectivity graph. Subsequently, using the graph convolution network to explore spatial features from the charging data, and the gate recurrent unit is used to mine temporal features, resulting in spatial-temporal predictions of the schedulable capacity of electric vehicles. Finally, the proposed method is validated and compared with other prediction methods.