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

TL;DR

The paper tackles the underestimation of risk in power-system preventive control caused by ignoring spatial correlations in weather-induced component failures. It introduces spatially dependent sampling (SDS), which jointly samples correlated weather intensities $w_i$ across components and propagates them through fragility-based failure models, using a covariance-based SDS framework with a Cholesky decomposition to generate scenarios. Through a case study on a synthetic Texas grid under Hurricane Harvey, SDS yields heavier-tailed, more extreme scenarios than conventional independent sampling and improves the robustness of a two-stage stochastic unit-commitment preventive-control model. The results highlight the importance of incorporating spatial dependence in scenario generation to avoid underestimating risk and to balance load curtailment and over-generation costs under varying severities, with a hazard- and asset-agnostic framework that can accommodate richer weather-uncertainty representations.

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.

Figures (20)

• Figure 1: Structure of literature review on preventive control methods
• Figure 2: Fragility curve and sample space for single component failure
• Figure 3: Comparison of different sampling methods in the sample space
• Figure 4: Sampling process of two components under correlated weather intensity
• Figure 5: Sensitivity of $\mathrm{Corr}(x_i,x_j)$ to $\overline{w}^*_i$, $\overline{w}^*_j$, $\sigma^*_i$, $\sigma^*_j$, and $\rho_{ij}$.