Wind power forecasts for an aggregation of wind farms have become pivotal to many user groups, e.g., system operators and market actors. However, deviations from these forecasts impact system stability and energy prices in power systems with a high share of installed wind power. Hence, modeling such deviations with probabilistic wind power forecasting approaches has become more relevant. Most methods account for different look-ahead timestamps separately and neglect the temporal propagation of wind power forecast errors. This paper presents a new approach that applies the so-called pair-copula constructions or vine copulae to generate time-coupled wind power infeed scenarios for an aggregation of wind farms. By using a copula approach, the modeling of the temporal dependence structure can be separated from the modeling of the wind power uncertainty in each timestamp. A D-vine structure is proposed and compared with a C-vine and a Gaussian copula in a case study on Belgian offshore wind farms. Different pair-copula selections and estimation procedures are tested. The evaluation results appear promising, especially for D-vines. |
