Identifying future congestion points in electricity distribution networks is an important challenge distribution system operators face. A proven approach for addressing this challenge is to assess distribution grid adequacy using probabilistic models of future demand. However, computational cost can become a severe challenge when evaluating large probabilistic electricity demand forecasting models with long forecasting horizons. In this paper, Monte Carlo methods are developed to increase the computational efficiency of obtaining asset overload probabilities from a bottom-up stochastic demand model. Cross-entropy optimised importance sampling is contrasted with conventional Monte Carlo sampling. Benchmark results of the proposed methods suggest that the importance sampling-based methods introduced in this work are suitable for estimating rare overload probabilities for assets with a small number of customers.@en

Since the volatility of the power load is expected to keep increasing due to new energy technologies, modelling the stochastic properties of the power loads becomes increasingly important for distribution network operators. Due to limited measurements in these grids, often bottom-up methods are used to create load estimations with which the peak load of the power customers is calculated. However, in average electricity consumption profiles, as used in most bottom-up methods, the stochastic behaviour of the customers energy consumption is neglected. In this study, the effect of neglecting the stochastic behaviour is investigated and is shown to be particularly strong in situations with a low number of consumers. To cope with this problem, several efficient methods to quantify the uncertainty and to determine peak loads have been evaluated. These methods were applied and validated on a data set with nearly thousand consumer measurement series, measured over 3.5 years on a 15 min resolution. In low-voltage networks with <10 power consumers, the conventional methods are shown to be at least a factor 2 too low. The suggested 'individual rescaling method' is accurate within 10%.@en