Advanced Probabilistic Power Flow Method Using Vine Copulas for Wind Power Capacity Expansion

Abstract

As the use of renewable energy is continuously increasing, power systems are currently exposed to greater uncertainty and variability, which can lead to severe power system stability issues. Therefore, a power system analysis tool should be devised to assess the impact of renewable energy integration along with an accurate modeling of their stochastic characteristics. In this study, an advanced probabilistic power flow (PPF) method is developed using vine copulas that captures the complex dependency of the stochastic wind power generated from multiple wind sites. The proposed method also involves the use of a function for selecting the probability models of wind speeds by regions in a sophisticated manner. The effectiveness of the proposed method is tested on an IEEE bus system as well as, on a South Korean power system with thousands of buses and transmission lines using PSS/E with Python API. The simulations demonstrate that the proposed method can more accurately evaluate the power system risks with the sophisticated modeling of wind power in multiple sites as compared to the deterministic approach or the PPF with independent sampling.

A Wind Power Scenario Generation Method Based on Copula Functions and Forecast Errors

“…In this paper, the Gaussian copula function was used, but to account for tail dependence between variables, the Vine copula function was used [24]. The authors of [25] used the Vine copula function to account for the spatiotemporal correlation of wind power generation in scenario generation, which improved the accuracy of voltage and current analysis. However, these papers did not consider the forecast errors of the forecast model in the scenario generation process.…”