Ultra-short-term load forecasting and risk assessment method for distribution networks based on the VMD–DeepAR model

Introduction With increasing uncertainties on both the generation and load sides in power systems, ultra-short-term load forecasting (USTLF) and risk assessment have become crucial for ensuring the secure and optimal operations of power systems, especially in distribution networks. Methods This paper proposed a probabilistic load forecasting method that integrates variational mode decomposition (VMD) with an improved deep autoregressive probabilistic forecasting (DeepAR) model. VMD reduces the non-stationarity of the load sequence, and a future feature enhancement mechanism was introduced to improve the accuracy under multi-step predictions. Based on the proposed method, an integrated assessment framework covering voltage deviations and transformer overload risks was constructed. Exponential aggregation functions and nonlinear normalization methods were utilized to evaluate the combined risk index with multidimensional risk indicators with different units. Results Case studies demonstrated that the proposed VMD with the improved DeepAR model improved the accuracy of load forecasting over traditional models. Discussion Moreover, the proposed risk assessment method can provide quantitative and systematic early risk-warning support for distribution network operations and decision-making.