Uncertainty Aggregation Characterization for Multi Spatial–Temporal Distributed Energy Resources: A Cloud-Edge-End Collaboration Framework

Uncertainty aggregation characterization of multi spatial–temporal distributed energy resources (DERs) is crucial for effective decision-making and control in power systems. In this article, we propose a cloud-edge-end collaboration approach to quantify the aggregated uncertainty of power generation from multi spatial–temporal DERs. First, considering the temporal dynamic and electrical topology correlation of DERs, a local uncertainty aggregation model based on a spatial-temporal graph neural network (STGNN) is developed. This model can effectively extract the spatial-temporal characteristics of data. Second, addressing the data silo problem caused by the unwillingness of various stakeholders managing the DERs to share data due to privacy concerns, an uncertainty aggregation model training mechanism based on an adaptive secure federated learning is proposed. This mechanism enables collaborative modeling of uncertainty aggregation models across stakeholders while preserving user privacy. In addition, it improves the quality of local model training by adaptively extracting parameter information from the global model for local model initialization. Moreover, since the probability distribution of the aggregated uncertainty is unknown, this article combines STGNN with the weighted quantile regression model to characterize the aggregated uncertainty without prior assumptions about the distribution, and by assigning differentiated weights to aggregation results under different confidence levels based on their importance, the proposed method can better meet the diverse needs of power grid. Finally, simulations conducted on the IEEE 33-bus system and IEEE 69-bus system validate the effectiveness of the proposed method.