Uncertainty Assessment of Dynamic Thermal Line Rating for Operational Use at Transmission System Operators

TL;DR

This paper develops a Monte Carlo-based method to quantify the uncertainty in Dynamic Thermal Rating (DTR) for transmission lines, enhancing operational decision-making for system safety and efficiency.

Contribution

It introduces a practical approach to estimate DTR uncertainty using weather data and temperature measurements, suitable for real-time use by transmission system operators.

Findings

Monte Carlo simulations effectively quantify DTR uncertainty.

The method integrates weather forecast errors into ampacity risk assessment.

Operational implementation demonstrated at Slovenian transmission network.

Abstract

Transmission system operators (TSOs) in recent years have faced challenges in order to ensure maximum transmission capacity of the system to satisfy market needs, while maintaining operational safety and permissible impact on the environment. A great help in the decision-making process was introduced with the Dynamic Thermal Rating (DTR) - an instrument to monitor and predict the maximal allowed ampacity of the power grid based on weather measurements and forecast. However, the introduction of DTR raises a number of questions related to the accuracy and uncertainty of the results of thermal assessment and the level of acceptable risk and its management. In this paper, we present a solution for estimating DTR uncertainty, appropriate for operational use at TSOs. With the help of conductor surface temperature measurements, weather measurements and predicted weather data, we also estimate the error of the weather forecast and the DTR itself. Following the results of the data analyses, we build an operative solution for estimating the ampacity uncertainty based on Monte Carlo random simulations and integrate it into the operational environment of ELES - the operator of the Slovenian electric power transmission network.