A Vine-Copula Based Voltage State Assessment with Wind Power Integration

Abstract

With the increasing rate of wind power installed capacity, voltage state assessment with large-scale wind power integration is of great significance. In this paper, a vine-copula based voltage state assessment method with large-scale wind power integration is proposed. Firstly, the nonparametric kernel density estimation is used to fit the wind speed distribution, and vine-copula is used to construct the wind speed joint distribution model of multiple regions. In order to obtain voltage distribution characteristics, probabilistic load flow based on the semi-invariant method and wind speed independent transformation based on the Rosenblatt transformation are described. On this basis, a voltage state assessment index is established for the more comprehensive evaluation of voltage characteristics, and a voltage state assessment procedure is proposed. Taking actual wind speed as an example, the case study of the IEEE 24-node power system and the east Inner Mongolia power system for voltage state assessment with large-scale wind power integration are studied. The simulation results verify the effectiveness of the proposed voltage state assessment method.

Modeling Wind Speed Using Parametric and Non-Parametric Distribution Functions

“…The non-parametric kernel density estimation (KDE) PDF has been used in multiple studies to model wind speed [7][8][9][10][11]. The wind speed used to model the power produced by WPGs was modeled in [7,8] using the non-parametric KDE PDF.…”

“…The non-parametric kernel density estimation (KDE) PDF has been used in multiple studies to model wind speed [7][8][9][10][11]. The wind speed used to model the power produced by WPGs was modeled in [7,8] using the non-parametric KDE PDF. However, the authors in [7,8] did not evaluate the accuracy with which the non-parametric KDE PDF can model wind speed since it was not compared to other PDFs.…”

“…The wind speed used to model the power produced by WPGs was modeled in [7,8] using the non-parametric KDE PDF. However, the authors in [7,8] did not evaluate the accuracy with which the non-parametric KDE PDF can model wind speed since it was not compared to other PDFs. In [9,10], the non-parametric KDE PDF has been compared to parametric PDFs to determine which is more accurate when modeling wind speed.…”

“…The results in [11] show that the KDE mixture model has a better fit to wind speed than the parametric PDFs. It should be mentioned that the authors in [7][8][9][10][11] did not evaluate if the PDFs can model the wind speed range. Therefore, it is not clear whether the PDFs used in [7][8][9][10][11] could model the minimum and maximum wind speed at the sites.…”

Modeling and Managing Joint Price and Volumetric Risk for Volatile Electricity Portfolios

“…Copulas disentangle the dependence structure of multiple variables from their marginal distributions. They are regularly used in applications of mathematical finance and economics, but gained interest in energy research in the past years [25,26]. With the combination of fitted marginal models and copulas, market prices and infeed volumes under a joint distribution can be simulated.…”