Efficient Assessment of Electricity Distribution Network Adequacy with the Cross-Entropy Method

TL;DR

This paper introduces an efficient Monte Carlo-based approach using cross-entropy importance sampling to accurately estimate rare overload probabilities in electricity distribution networks, reducing computational costs for large probabilistic demand models.

Contribution

It develops and benchmarks a novel importance sampling method with cross-entropy optimization for assessing distribution network adequacy more efficiently.

Findings

Importance sampling outperforms conventional Monte Carlo in estimating rare overloads.

The method is effective for assets with few customers.

Significant reduction in computational effort achieved.

Abstract

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.