Stochastic Assessment and Risk Management of Transient Stability based on PowerFactory and Python Interface

Abstract

This paper presents a novel simulation tool that exploits the feasibility of communicating DIgSILENT PowerFactory and Python to evaluate the impact of the electrical demand uncertainty regarding power system transient stability, considering the probabilistic analysis of the Critical Clearing Time (CCT). The probabilistic analysis of transient stability allows establishing a probability density function (PDF) of the CCT behavior. This PDF is calculated by means of Monte Carlo simulations in which the CCT is evaluated for each generated scenario. The bisection method is applied for calculating the CCT. This method consists in iteratively modifying the fault duration time (tf) and evaluating transient stability for each tf via the hybrid method SIME (Single Machine Equivalent). Finally, a proposal for assessing the risk involved in the transient stability assessment is structured using the concepts of Value at Risk (VaR) and Conditional Value at Risk (CVaR). The proposed methodology is tested in the 39-bus IEEE New England test system.