Probabilistic Approach for Risk Evaluation of Oscillatory Stability in Power Systems

Abstract

The use of probabilistic framework is of great importance for the development of comprehensive approaches, which are suitable for coping with increasing uncertainties in power system operation and planning. While huge effort has been put in the past into the conception of probabilistic methods to deal with stochastic load flow calculation, there is an increasing interest on the development of new approaches to ascertain the implications of changing operating conditions in terms of power system dynamic performance. Among the main concerns on this regard is the determination of the degree of exposure to poorly damped low-frequency oscillations (LFOs), which occur typically in the range of 0.1–1.0 Hz. This chapter concerns the implementation of a Monte Carlo (MC)-based approach for evaluation of oscillatory instability risk by using the functionalities for modelling and programming of DIgSILENT PowerFactory. Particularly, the DIgSILENT programming language (DPL) is used to structure the steps of the MC repetitive procedure, namely sampling of uncertain input variables, automated scenario generation, and storage of eigenanalysis outcomes. The chapter also illustrates the implementation of the PST 16 benchmark system, which has a relative large size, and is appropriated to study different kinds of stability problems, especially LFOs. Based on characteristic parameters of the European power systems, different built-in models available in DIgSILENT are used to model the system components. Numerical experiments performed on this system support the relevance of the MC-based approach.