A modular framework for estimating annual averaged power output generation of wind turbines. (1st October 2020) - Wacker, Benjamin; Seebaß, Johann V.; Schlüter, Jan Chr.
Graphical abstract: Highlights: A general framework for annual averaged power output generation for wind turbines is developed. The pipeline relies on wind speed data, power curves of wind turbines and wind speed distributions. Considerations regarding uncertainty quantification are presented. Detailed examples with cubic or logistic power curves and Weibull, Kappa or Wakeby wind speed distributions are presented. Simulation studies of weather stations from California (United States) and Germany prove the framework's flexibility. Abstract: Wind energy represents an important future energy source due to rising global interest in renewable energies. For this reason, power output prediction of wind turbines is a prominent task for supporting decisions regarding future sites. The aim of this study is therefore the development of a general framework for estimating annual averaged power output generation of wind turbines. This modular framework relies on general large wind speed data sets, general power curve modeling and general wind speed distributions - possible examples are Weibull, Kappa or Wakeby distributions. Cubic spline interpolation or logistic power curves and the three aforementioned wind speed distributions are applied as example combinations of the abstract framework to one weather station located at List, Germany in detail. Cubic spline interpolation for power curves and different wind speed distributions are finally adapted to weather stations from California and Germany for annual averaged wind power output predictions. As a main result of the computational study, comparison of semi-empirical power output predictions and estimated power output predictions showed that Kappa and Wakeby distributions are superior to two-parameter Weibull distributions. Summarizing, the proposed modular framework proves to be a flexible, unifying and useful tool for future assessment and future comparative studies of different prediction combinations.