Exact and Efficient Algorithm to Discover Extreme Stochastic Events in Wind Generation over Transmission Power Grids

TL;DR

This paper introduces an exact and efficient polynomial-time algorithm to identify the most probable extreme stochastic events in power grids caused by wind power variability, aiding in failure prediction and grid stability analysis.

Contribution

The paper presents a novel polynomial-time algorithm for discovering extreme stochastic events in power grids with wind generation, assuming known wind forecast distributions.

Findings

Algorithm successfully identifies extreme events in IEEE RTS-96 model.

Probability of failure varies with renewable penetration and distribution.

Algorithm is efficient for low-parametric control and log-concave distributions.

Abstract

In this manuscript we continue the thread of [M. Chertkov, F. Pan, M. Stepanov, Predicting Failures in Power Grids: The Case of Static Overloads, IEEE Smart Grid 2011] and suggest a new algorithm discovering most probable extreme stochastic events in static power grids associated with intermittent generation of wind turbines. The algorithm becomes EXACT and EFFICIENT (polynomial) in the case of the proportional (or other low parametric) control of standard generation, and log-concave probability distribution of the renewable generation, assumed known from the wind forecast. We illustrate the algorithm's ability to discover problematic extreme events on the example of the IEEE RTS-96 model of transmission with additions of 10%, 20% and 30% of renewable generation. We observe that the probability of failure may grow but it may also decrease with increase in renewable penetration, if the latter is sufficiently diversified and distributed.