Probabilistic Dynamic Line Rating Forecasting with Line Graph Convolutional LSTM

TL;DR

This paper introduces a probabilistic forecasting model using line graph convolutional LSTM to improve dynamic line rating predictions by capturing both temporal and spatial correlations, enhancing reliability and sharpness.

Contribution

The paper presents a novel line graph convolutional LSTM model that effectively incorporates spatial and temporal correlations for probabilistic DLR forecasting, outperforming existing models.

Findings

Model significantly outperforms baseline methods in reliability.

Model achieves better sharpness in predictions.

Uses fewer parameters than competing models.

Abstract

Dynamic line rating (DLR) is a promising solution to increase the utilization of transmission lines by adjusting ratings based on real-time weather conditions. Accurate DLR forecast at the scheduling stage is thus necessary for system operators to proactively optimize power flows, manage congestion, and reduce the cost of grid operations. However, the DLR forecast remains challenging due to weather uncertainty. To reliably predict DLRs, we propose a new probabilistic forecasting model based on line graph convolutional LSTM. Like standard LSTM networks, our model accounts for temporal correlations between DLRs across the planning horizon. The line graph-structured network additionally allows us to leverage the spatial correlations of DLR features across the grid to improve the quality of predictions. Simulation results on the synthetic Texas 123-bus system demonstrate that the proposed model significantly outperforms the baseline probabilistic DLR forecasting models regarding reliability and sharpness while using the fewest parameters.