Risk-Based Security Assessment with Big Data Driven Probabilistic Modeling for WET Snow Extreme Events

Abstract

Security analyses in modern power systems require to analyse the vulnerabilities to natural threats which affect the power system in the future 24–72 hours, potentially causing multiple, dependent contingencies. These events often lead to high impact on the system, so that decision-making aimed to enhance security may become difficult. An effective risk-based contingency analysis and contingency ranking can be performed by considering uncertainty of incumbent threats, system state and response. To this purpose, threat and vulnerability analyses can benefit from the latest developments in big data applications. The paper presents the integration of accurate forecasts, coming from advanced numerical weather prediction systems and related to the specific hazard of wet snows, with a risk-based security assessment tool. Simulation results are compared against public information about outages recorded during a recent extreme wet snow event in the North of Italy, confirming the importance of data-driven hazard analyses integrated in security assessment applications.