Probabilistic Dynamic Line Rating with Line Graph Convolutional LSTM

TL;DR

This paper introduces a probabilistic forecasting model for dynamic line ratings that uses spatial-temporal data and graph convolutional LSTM to improve grid reliability and reduce costs under uncertain weather conditions.

Contribution

It presents a novel network-wide probabilistic DLR forecasting approach combining spatial and temporal information with graph convolutional LSTM, advancing beyond deterministic methods.

Findings

Enhanced grid reliability by accurately capturing true DLR values

Significantly reduced operational costs in case studies

Effectively manages uncertainty in weather-dependent line ratings

Abstract

Dynamic line rating (DLR) is an effective approach to enhancing the utilization of existing transmission line infrastructure by adapting line ratings according to real-time weather conditions. Accurate DLR forecasts are essential for grid operators to effectively schedule generation, manage transmission congestion, and lower operating costs. As renewable generation becomes increasingly variable and weather-dependent, accurate DLR forecasts are also crucial for improving renewable utilization and reducing curtailment during congested periods. Deterministic forecasts, however, often inadequately represent actual line capacities under uncertain weather conditions, leading to operational risks and costly real-time adjustments. To overcome these limitations, we propose a novel network-wide probabilistic DLR forecasting model that leverages both spatial and temporal information, significantly reducing the operational risks and inefficiencies inherent in deterministic methods. Case studies on a synthetic Texas 123-bus system demonstrate that the proposed method not only enhances grid reliability by effectively capturing true DLR values, but also substantially reduces operational costs.