Towards Probabilistic Dynamic Security Assessment and Enhancement of Large Power Systems

TL;DR

This paper introduces a comprehensive probabilistic framework for dynamic security assessment of large power systems, incorporating load variability, contingencies, and cascade effects, enhanced with machine learning for root cause analysis.

Contribution

It presents a novel methodology combining statistical sampling, contingency screening, and interpretable machine learning for security assessment and enhancement in power systems.

Findings

Effective risk estimation with statistical error control.

Contingency screening reduces computational load.

Machine learning aids root cause identification.

Abstract

This paper proposes a novel methodology for probabilistic dynamic security assessment and enhancement of power systems that considers load and generation variability, N-2 contingencies, and uncertain cascade propagation caused by uncertain protection system behaviour. In this methodology, a database of likely operating conditions is generated via weather data, a market model and a model of operators' preventive actions. System states are sampled from this database and contingencies are applied to them to perform the security assessment. Rigorous statistical indicators are proposed to decide how many biased and unbiased samples to simulate to reach a target accuracy on the statistical error on the estimated risk from individual contingencies. Optionally, a screening of contingencies can be performed to limit the computational burden of the analysis. Finally, interpretable machine learning techniques are used to identify the root causes of the risk from critical contingencies, to ease the interpretation of the results, and to help with security enhancement. The method is demonstrated on the 73-bus reliability test system, and the scalability to large power systems (with thousands of buses) is also discussed.