Data-Driven Operational Risk Assessment in Renewable-Rich Power System. An Automated Probabilistic Power Flow and SCADA Data Mining Framework

Abstract

The increasing penetration of variable renewable energy (VRE) introduces profound stochastic uncertainties into power systems, rendering traditional deterministic security assessments insufficient. This paper addresses this challenge by developing a comprehensive, AI-ready operational risk assessment framework based on a data-driven Probabilistic Power Flow (PPF) approach. Unlike conventional methods that rely on assumed statistical distributions, the proposed methodology employs a statistical learning pipeline to identify suitable probability density functions directly from historical SCADA telemetry, thereby ensuring high-fidelity input modeling. Furthermore, a cluster-based correlation strategy, rooted in unsupervised pattern recognition, is constructed to rigorously capture the spatiotemporal dependencies among renewable generation sources. The probabilistic framework is executed via an enhanced Monte Carlo simulation (MCS) utilizing Latin Hypercube Sampling (LHS) to improve computational efficiency. Validated through a case study on the Central Vietnam Power System, the results demonstrate that the proposed framework effectively uncovers hidden operational vulnerabilities, specifically identifying a 4.57% probability of exceeding the 90% branch-loading warning threshold on the critical 220 kV Quy Nhon - Tuy Hoa transmission line, while the probability of actual thermal overload above 100% is nearly zero. This near-limit operating risk profile may be masked by conventional deterministic snapshots. The findings emphasize that accurately modeling the correlation structure of renewable sources is indispensable for realistic risk quantification, providing system operators with a robust, algorithm-driven decision-support tool for grid security management.