Data-driven probabilistic evaluation of voltage stability limit considering joint wind uncertainties

Abstract

The voltage stability limit (VSL) is crucial for power system operators to ensure grid security by defining the maximum loading before risking voltage collapse, thus preventing cascading events and maintaining reliable power supply. Higher proportion of renewable integration necessitates the accurate consideration of forecast uncertainties when determining VSL distribution. However, probabilistic VSL evaluation has received limited attention in the literature. This article puts forth a novel framework for efficient and accurate probabilistic VSL evaluation in large, contemporary power systems. Addressing the limitations of existing methods, the proposed framework incorporates several key advancements. Firstly, Transversality Enforced Newton-Raphson (TENR) is employed as an efficient alternative to Continuation Power Flow (CPF) for VSL determination, significantly reducing computational burden. Secondly, the framework utilizes R-vine copula, a flexible and data-driven model, to accurately capture complex dependence structure among wind power forecasts, surpassing the limitations of standard Gaussian and restricted vine copula models. Finally, a novel scenario generation approach integrating Maximum Projection Design (MPD) based R-vine sampling is proposed to enhance the accuracy-efficiency balance of probabilistic VSL evaluation. The proposed framework is rigorously validated on modified 39-bus, 118-bus, and 2383-bus test systems against four state-of-the-art benchmark methods. Numerical results substantiate the superior VSL distribution estimation accuracy of the proposed framework, alongside an approximate 25-fold computational speedup compared to existing benchmarks for large-scale networks, fulfilling a critical industry need for a scalable and accurate VSL evaluation tool for step-ahead and day-ahead security studies with N -1 contingency compliance.