a b s t r a c tA new two-dimensional (2D) wake model is developed and validated in this article to predict the velocity and turbulence distribution in the wake of a wind turbine. Based on the classical Jensen wake model, this model is further employing a cosine shape function to redistribute the spread of the wake deficit in the crosswind direction. Moreover, a variable wake decay rate is proposed to take into account both the ambient turbulence and the rotor generated turbulence, different from a constant wake decay rate used in the Jensen model. The obtained results are compared to field measurements, wind tunnel experiments, and results of an advanced k À ω turbulence model as well as large eddy simulations. From the comparisons, it is found that the proposed new wake model gives a good prediction in terms of both shape and velocity amplitude of the wake deficit, especially in the far wake which is the region of interest for wind farm development projects.
Abstract:The increasing share of wind energy in the portfolio of energy sources highlights its uncertainties due to changing weather conditions. To account for the uncertainty in predicting wind power production, this article examines the volatility forecasting abilities of different GARCH-type models for wind power production. Moreover, due to characteristic features of the wind power process, such as heteroscedasticity and nonlinearity, we also investigate the use of a Markov regime-switching GARCH (MRS-GARCH) model on forecasting volatility of wind power. The realized volatility, which is derived from lower-scale data, serves as a benchmark for the latent volatility. We find that the MRS-GARCH model significantly outperforms traditional GARCH models in predicting the volatility of wind power, while the exponential GARCH model is superior among traditional GARCH models.
Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. To meet the increasing global demand for renewable energy such as wind energy, more and more new wind parks are installed worldwide. Finding a suitable location, however, requires a detailed and often costly analysis of the local wind conditions. Plain average wind speed maps cannot provide a precise forecast of wind power because of the non-linear relationship between wind speed and production. In this paper, we suggest a new approach of assessing the local wind energy potential: Meteorological reanalysis data are applied to obtain long-term low-scale wind speed data at turbine location and hub height; then, with actual high-frequency production data, the relation between wind data and energy production is determined via a five parameter logistic function. The resulting wind energy index allows for a turbine-specific estimation of the expected wind power at an unobserved location. A map of wind power potential for whole Germany exemplifies the approach.
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Purpose
– The purpose of this paper is to review some challenges of insuring weather risk in agriculture and to discuss potential remedies for these problems.
Design/methodology/approach
– The paper is developed as a narrative on weather insurance based largely on existing literature.
Findings
– Weather risks show characteristics that often violate classical requirements for insurability. First, some weather risks, particularly slowly emerging weather perils like drought, are spatially correlated and cause systemic risks. Second, climatic change may increase the volatility of weather variables and lead to non-stationary loss distributions, which causes difficulties in actuarial ratemaking. Third, limited availability of yield and weather data hinders the estimation of reliable loss distributions.
Practical implications
– Some of the approaches discussed in this review, such as time diversification, local test procedures and the augmentation of observational data by expert knowledge, can be useful for crop insurance companies to improve their risk management and product design.
Originality/value
– This study provides background and development information regarding weather insurance and also presents statistical tools and actuarial methods that support the assessment of weather risks as well as the design of weather and yield insurance products.
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