Dynamic optimization method for complex operating conditions of power supply services based on spatiotemporal graph neural networks
TL;DR
A condition aware dynamic source-load aggregation (CA-DSSA), validated on the IEEE 33-bus distribution system across seven distinct operating conditions, whose core innovation is a differentiable soft graph partitioning layer whose zone boundaries are continuously generated by an operating-condition encoder, enabling joint end-to-end optimization of partitioning and feature learning without any predefined zoning rules.
Abstract
Increasing penetration of renewable energy in distribution networks severely disrupts traditional source-load aggregation, particularly under complex operating conditions. This paper proposes a condition aware dynamic source-load aggregation(CA-DSSA,), validated on the IEEE 33-bus distribution system across seven distinct operating conditions. The core innovation is a differentiable soft graph partitioning layer (SGPL) whose zone boundaries are continuously generated by an operating-condition encoder, enabling joint end-to-end optimization of partitioning and feature learning without any predefined zoning rules. A relational graph attention network (R-GAT) captures heterogeneous bus and branch semantics, while a dilated temporal convolutional network (D-TCN) models multi-scale temporal dynamics. Comparative experiments against DCRNN and STGAT demonstrate that CA-DSSA reduces Mean Absolute Percentage Error (MAPE), achieves a prediction interval coverage probability (PICP) above 95%.