Blackout Events and Grid Reliability Indicators: A Comparative Analysis of Infrastructure Quality Standards Across Geographical Regions

This paper presents innovative research on power grid reliability, which is essential for the overall sustainability of energy systems in the emerging age of electricity. The research primarily analyzes the profound methodological disparities between the fragmented European approach and the exact statistical model employed in the United States (the 2.5 Beta method defined by the IEEE 1366 standard). A novel dimension addressed in this research is the compounding effect of climate hazards and the massive proliferation of artificial intelligence (AI) data centers. Unlike conventional single-layer machine learning models (such as standard Support Vector Machines or regressions) that rely solely on historical weather data, this study proposes the Hierarchical Spatiotemporal Multiplex Networks (HMN-RTS) predictive framework. By dynamically fusing structured environmental data with unstructured social sensor data (Geographic Information Systems—GIS, and social media feeds), the proposed HMN-RTS framework significantly outperforms traditional models in predicting outage risks and their exact durations.