Weighing benefits and risks of thermal rating amid uncertainty in asset parameters and load patterns in times of congestion: computational method and key observations

One of the strategies to optimize the utility of the existing assets, is the implementation of cyclic rating methods for MV-distribution cables. Cyclic limits are based on modelling the thermal behaviour of the cables using their time-dependent load profile. Analysis of the cyclic rating of each individual cable system within the grid showed cyclicity can vary significantly from year to year. In this paper we propose and demonstrate a method to tackle this problem through uncertainty quantification using a statistical approach on the population-level. Monte Carlobased thermal modelling is employed, wherein model inputs are represented as probabilitydistributions derived from empirical research and measurements. This framework enables the estimation of key metrics; ampacity and impact distributions. By quantifying the benefits and risks in determining higher cyclic cable ratings under condition of significant environmental and load characteristic uncertainty, the approach contributes to shape asset management policy and optimize grid utilization.