Spatially Dependent Sampling of Component Failures for Power System Preventive Control Against Hurricane

TL;DR

This paper introduces a spatially dependent sampling method for modeling correlated component failures in power systems during hurricanes, improving scenario realism and risk assessment for preventive control strategies.

Contribution

It develops a novel sampling approach that captures spatial correlations in weather-induced failures, addressing a key gap in existing independent sampling methods.

Findings

Correlated weather intensity leads to interdependent failures.

The method uncovers more high-severity failure scenarios.

Ignoring correlations underestimates risk and reduces control robustness.

Abstract

Preventive control is a crucial strategy for power system operation against impending natural hazards, and its effectiveness fundamentally relies on the realism of scenario generation. While most existing studies employ sequential Monte Carlo simulation and assume independent sampling of component failures, this oversimplification neglects the spatial correlations induced by meteorological factors such as hurricanes. In this paper, we identify and address the gap in modeling spatial dependence among component failures under extreme weather. We analyze how the mean, variance, and correlation structure of weather intensity random variables influence the correlation of component failures. To fill this gap, we propose a spatially dependent sampling method that enables joint sampling of multiple component failures by generating correlated meteorological intensity random variables. Comparative studies show that our approach captures long-tailed scenarios and reveals more extreme events than conventional methods. Furthermore, we evaluate the impact of scenario selection on preventive control performance. Our key findings are: (1) Strong spatial correlations in uncertain weather intensity consistently lead to interdependent component failures, regardless of mean value level; (2) The proposed method uncovers more high-severity scenarios that are missed by independent sampling; (3) Preventive control requires balancing load curtailment and over-generation costs under different scenario severities; (4) Ignoring failure correlations results in underestimating risk from high-severity events, undermining the robustness of preventive control strategies.