Power Grid Reliability Estimation via Adaptive Importance Sampling

TL;DR

This paper introduces an adaptive importance sampling method for efficiently estimating power grid failure probabilities under renewable energy fluctuations, enabling real-time reliability assessment with fewer samples.

Contribution

It presents a novel physics-informed adaptive importance sampling algorithm tailored for power grid reliability estimation, improving efficiency over existing methods.

Findings

The proposed method achieves accurate failure probability estimates with significantly fewer samples.

It outperforms state-of-the-art techniques in multiple IEEE power grid test cases.

The approach enables real-time reliability assessment for high-voltage direct current grids.

Abstract

Electricity production currently generates approximately 25% of greenhouse gas emissions in the USA. Thus, increasing the amount of renewable energy is a key step to carbon neutrality. However, integrating a large amount of fluctuating renewable generation is a significant challenge for power grid operating and planning. Grid reliability, i.e., an ability to meet operational constraints under power fluctuations, is probably the most important of them. In this paper, we propose computationally efficient and accurate methods to estimate the probability of failure, i.e. reliability constraints violation, under a known distribution of renewable energy generation. To this end, we investigate an importance sampling approach, a flexible extension of Monte-Carlo methods, which adaptively changes the sampling distribution to generate more samples near the reliability boundary. The approach allows to estimate failure probability in real-time based only on a few dozens of random samples, compared to thousands required by the plain Monte-Carlo. Our study focuses on high voltage direct current power transmission grids with linear reliability constraints on power injections and line currents. We propose a novel theoretically justified physics-informed adaptive importance sampling algorithm and compare its performance to state-of-the-art methods on multiple IEEE power grid test cases.