Hugo Algarvio, Hugo Algário
Most transmission system operators (TSOs) traditionally use seasonally static models based on extreme weather conditions for calculating the transmission capacity of power lines. This study aims to present and test three pre-solve methodologies that facilitate the rapid identification of critical sectors in overhead lines utilizing dynamic line rating (DLR) models. By addressing the computational challenges posed by the comprehensive indirect DLR methodology, these methodologies provide an efficient means to avoid the overestimation of ampacity caused by inaccurate forecasts during real-time operations. Using real data from the transmission grid and corresponding weather conditions from two regions in Portugal, the methodologies demonstrated a remarkable accuracy, achieving less than 1% error in DLR computation compared to the complete CIGRÉ model, while significantly reducing the computation time to identify critical sectors. The findings suggest that integrating these pre-solve methodologies into TSO operations can enhance resource sustainability by reducing the need for additional infrastructure and allowing for more adaptive grid management based on dynamic environmental conditions.
@article{24ef371a-3666-4899-8a12-b27706de6abe,
title={Pre Solve Methodologies for Short Run Identificati},
author={Hugo Algarvio and Hugo Algário},
year={2026},
language={en}
}TY - JOUR TI - Pre Solve Methodologies for Short Run Identificati AU - Hugo Algarvio AU - Hugo Algário PY - 2026 LA - en ER -
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