Comparison of turbulence models for the computational fluid dynamics simulation of wind turbine wakes in the atmospheric boundary layer

An elliptic computational fluid dynamics wake model based on the actuator disk concept is used to simulate a wind turbine, approximated by a disk upon which a distribution of forces, defined as axial momentum sources, is applied on an incoming non-uniform shear flow. The rotor is supposed to be uniformly loaded with the exerted forces estimated as a function of the incident wind speed, thrust coefficient and rotor diameter. The model is assessed in terms of wind speed deficit and added turbulence intensity for different turbulence models and is validated from experimental measurements of the Sexbierum wind turbine experiment.

Flow and wakes in large wind farms in complex terrain and offshore

Power losses due to wind turbine wakes are of the order of 10 and 20% of total power output in large wind farms. The focus of this research carried out within the EC funded UPWIND project is wind speed and turbulence modelling for large wind farms/wind turbines in complex terrain and offshore in order to optimise wind farm layouts to reduce wake losses and loads.

Numerical CFD modelling of non-neutral atmospheric boundary layers for offshore wind resource assessment based on Monin-Obukhov theory

The presented works aim at proposing a methodology for the simulation of offshore wind conditions using CFD. The main objective is the development of a numerical model for the characterization of atmospheric boundary layers of different stability levels, as the most important issue in offshore wind resource assessment. Based on Monin-Obukhov theory, the steady k-ε Standard turbulence model is modified to take into account thermal stratification in the surface layer. The validity of Monin-Obukhov theory in offshore conditions is discussed with an analysis of a three day episode at FINO-1 platform.

Comparing linear and non linear wind flow models

Assessing wind conditions on complex terrain has become a hard task as terrain complexity increases. That is why there is a need to extrapolate in a reliable manner some wind parameters that determine wind farms viability such as annual average wind speed at all hub heights as well as turbulence intensities. The development of these tasks began in the early 90´s with the widely used linear model WAsP and WAsP Engineering especially designed for simple terrain with remarkable results on them but not so good on complex orographies. Simultaneously non-linearized Navier Stokes solvers have been rapidly developed in the last decade through CFD (Computational Fluid Dynamics) codes allowing simulating atmospheric boundary layer flows over steep complex terrain more accurately reducing uncertainties. This paper describes the features of these models by validating them through meteorological masts installed in a highly complex terrain. The study compares the results of the mentioned models in terms of wind speed and turbulence intensity.

RANS simulations of wind flow at the Bolund experiment

As part of their development, the predictions of numerical wind flow models must be compared with measurements in order to estimate the uncertainty related to their use. Of course, the most rigorous such comparison is under blind conditions. The following paper includes a detailed description of three different wind flow models, all based on a Reynolds-averaged Navier-Stokes approach and two-equation k-ε closure, that were tested as part of the Bolund blind comparison (itself based on the Bolund experiment which measured the wind around a small coastal island). The models are evaluated in terms of predicted normalized wind speed and turbulent kinetic energy at 2 m and 5 m above ground level for a westerly wind direction. Results show that all models predict the mean velocity reasonably well; however accurate prediction of the turbulent kinetic energy remains achallenge.

Simulation of wind farms in flat and complex terrain using CFD

Use of computational fluid dynamic (CFD) methods to predict the power production from wind entire wind farms in flat and complex terrain is presented in this paper. Two full 3D Navier–Stokes solvers for incompressible flow are employed that incorporate the k–ε and k–ω turbulence models respectively. The wind turbines (W/Ts) are modelled as momentum absorbers by means of their thrust coefficient using the actuator disk approach. The WT thrust is estimated using the wind speed one diameter upstream of the rotor at hub height. An alternative method that employs an induction-factor based concept is also tested. This method features the advantage of not utilizing the wind speed at a specific distance from the rotor disk, which is a doubtful approximation when a W/T is located in the wake of another and/or the terrain is complex. To account for the underestimation of the near wake deficit, a correction is introduced to the turbulence model. The turbulence time scale is bounded using the general “realizability” constraint for the turbulent velocities. Application is made on two wind farms, a five-machine one located in flat terrain and another 43-machine one located in complex terrain. In the flat terrain case, the combination of the induction factor method along with the turbulence correction provides satisfactory results. In the complex terrain case, there are some significant discrepancies with the measurements, which are discussed. In this case, the induction factor method does not provide satisfactory results.

CFD modelling of wind farms in complex terrain

Modelling of entire wind farms in flat and complex terrain using a full 3D Navier–Stokes solver for incompressible flow is presented in this paper. Numerical integration of the governing equations is performed using an implicit pressure correction scheme, where the wind turbines (W/Ts) are modelled as momentum absorbers through their thrust coefficient. The k–ω turbulence model, suitably modified for atmospheric flows, is employed for closure. A correction is introduced to account for the underestimation of the near wake deficit, in which the turbulence time scale is bounded using a general “realizability” constraint for the fluctuating velocities. The second modelling issue that is discussed in this paper is related to the determination of the reference wind speed for the thrust calculation of the machines. Dealing with large wind farms and wind farms in complex terrain, determining the reference wind speed is not obvious when a W/T operates in the wake of another WT and/or in complex terrain. Two alternatives are compared: using the wind speed value at hub height one diameter upstream of the W/T and adopting an induction factor-based concept to overcome the utilization of a wind speed at a certain distance upwind of the rotor. Application is made in two wind farms, a five-machine one located in flat terrain and a 43-machine one located in complex terrain.

Analysis and validation of CFD wind farm models in complex terrain. Effects induced by topography and wind turbines

Wind farms have been extensively simulated through engineering models for the estimation of wind speed and power deficits inside wind farms. These models were designed initially for a few wind turbines located in flat terrain. Other models based on the parabolic approximation of Navier Stokes equations were developed, making more realistic and feasible the operational resolution of big wind farms in flat terrain and offshore sites. These models have demonstrated to be accurate enough when solving wake effects for this type of environments. Nevertheless, few analyses exist on how complex terrain can affect the behaviour of wind farm wake flow. Recent numerical studies have demonstrated that topographical wakes induce a significant effect on wind turbines wakes, compared to that on flat terrain. This circumstance has recommended the development of elliptic CFD models which allow global simulation of wind turbine wakes in complex terrain. An accurate simplification for the analysis of wind turbine wakes is the actuator disk technique. Coupling this technique with CFD wind models enables the estimation of wind farm wakes preserving the extraction of axial momentum present inside wind farms. This paper describes the analysis and validation of the elliptical wake model CFDWake 1.0 against experimental data from an operating wind farm located in complex terrain. The analysis also reports whether it is possible or not to superimpose linearly the effect of terrain and wind turbine wakes. It also represents one of the first attempts to observe the performance of engineering models compares in large complex terrain wind farms.

Parameterization of the atmospheric boundary layer for offshore wind resource assessment with a limited length-scale k-ε model

The structure of the atmospheric boundary layer (ABL) is modelled with the limited- length-scale k-ε model of Apsley and Castro. Contrary to the standard k-ε model, the limited-length-scale k-ε model imposes a maximum mixing length which is derived from the boundary layer height, for neutral and unstable atmospheric situations, or by Monin-Obukhov length when the atmosphere is stably stratified. The model is first verified reproducing the famous Leipzig wind profile. Then the performance of the model is tested with measurements from FINO-1 platform using sonic anemometers to derive the appropriate maximum mixing length.

Prediction of wake effects on wind farm power production using a RANS approach. Part II. Offshore: Case studies from the UPWIND project

The estimation of power losses due to wind turbine wakes is crucial to understanding overall wind farm economics. This is especially true for large offshore wind farms, as it represents the primary source of losses in available power, given the regular arrangement of rotors, their generally largerdiameter and the lower ambient turbulence level, all of which conspire to dramatically affect wake expansion and, consequently, the power deficit. Simulation of wake effects in offshore wind farms (in reasonable computational time) is currently feasible using CFD tools. An elliptic CFD model basedon the actuator disk method and various RANS turbulence closure schemes is tested and validated using power ratios extracted from Horns Rev and Nysted wind farms, collected as part of the EU-funded UPWIND project. The primary focus of the present work is on turbulence modeling, as turbulent mixing is the main mechanism for flow recovery inside wind farms. A higher-order approach, based on the anisotropic RSM model, is tested to better take into account the imbalance in the length scales inside and outside of the wake, not well reproduced by current two-equation closure schemes.

Modelling wake effects in large wind farms in complex terrain: the problem, the methods and the issues

Computational fluid dynamic (CFD) methods are used in this paper to predict the power production from entire wind farms in complex terrain and to shed some light into the wake flow patterns. Two full three-dimensional Navier–Stokes solvers for incompressible fluid flow, employing k − ϵ and k − ω turbulence closures, are used. The wind turbines are modeled as momentum absorbers by means of their thrust coefficient through the actuator disk approach. Alternative methods for estimating the reference wind speed in the calculation of the thrust are tested. The work presented in this paper is part of the work being undertaken within the UpWind Integrated Project that aims to develop the design tools for next generation of large wind turbines. In this part of UpWind, the performance of wind farm and wake models is being examined in complex terrain environment where there are few pre-existing relevant measurements. The focus of the work being carried out is to evaluate the performance of CFD models in large wind farm applications in complex terrain and to examine the development of the wakes in a complex terrain environment.

The use of wind pumps for greenhouse microirrigation: A case study for tomato in Cuba

A methodology based on daily estimation balance between water needs and water availability was used to evaluate the feasibility of the most economic windmill irrigation system. For this purpose, several factors were included: three-hourly wind velocity (W3 h, m/s), flow supplied by the wind pump as a function of the elevation height (H, m) and daily greenhouse evapotranspiration as a function of crop planting date. Monthly volumes of water required for irrigation (Dr, m3/ha) and in the water tank (Vd, m3), as well as the monthly irrigable area (Ar, ha), were estimated by cumulative deficit water budgeting taking in account these factors.

Development of a wake model for wind farms based on an open source CFD solver. Strategies on parabolization and turbulence modeling

Wake effect represents one of the most important aspects to be analyzed at the engineering phase of every wind farm since it supposes an important power deficit and an increase of turbulence levels with the consequent decrease of the lifetime. It depends on the wind farm design, wind turbine type and the atmospheric conditions prevailing at the site. Traditionally industry has used analytical models, quick and robust, which allow carry out at the preliminary stages wind farm engineering in a flexible way. However, new models based on Computational Fluid Dynamics (CFD) are needed. These models must increase the accuracy of the output variables avoiding at the same time an increase in the computational time. Among them, the elliptic models based on the actuator disk technique have reached an extended use during the last years. These models present three important problems in case of being used by default for the solution of large wind farms: the estimation of the reference wind speed upstream of each rotor disk, turbulence modeling and computational time. In order to minimize the consequence of these problems, this PhD Thesis proposes solutions implemented under the open source CFD solver OpenFOAM and adapted for each type of site: a correction on the reference wind speed for the general elliptic models, the semi-parabollic model for large offshore wind farms and the hybrid model for wind farms in complex terrain. All the models are validated in terms of power ratios by means of experimental data derived from real operating wind farms.

Statistical models for short-term wind power ramp forecasting

La predicción de energía eólica ha desempeñado en la última década un papel fundamental en el aprovechamiento de este recurso renovable, ya que permite reducir el impacto que tiene la naturaleza fluctuante del viento en la actividad de diversos agentes implicados en su integración, tales como el operador del sistema o los agentes del mercado eléctrico. Los altos niveles de penetración eólica alcanzados recientemente por algunos países han puesto de manifiesto la necesidad de mejorar las predicciones durante eventos en los que se experimenta una variación importante de la potencia generada por un parque o un conjunto de ellos en un tiempo relativamente corto (del orden de unas pocas horas). Estos eventos, conocidos como rampas, no tienen una única causa, ya que pueden estar motivados por procesos meteorológicos que se dan en muy diferentes escalas espacio-temporales, desde el paso de grandes frentes en la macroescala a procesos convectivos locales como tormentas. Además, el propio proceso de conversión del viento en energía eléctrica juega un papel relevante en la ocurrencia de rampas debido, entre otros factores, a la relación no lineal que impone la curva de potencia del aerogenerador, la desalineación de la máquina con respecto al viento y la interacción aerodinámica entre aerogeneradores. En este trabajo se aborda la aplicación de modelos estadísticos a la predicción de rampas a muy corto plazo. Además, se investiga la relación de este tipo de eventos con procesos atmosféricos en la macroescala. Los modelos se emplean para generar predicciones de punto a partir del modelado estocástico de una serie temporal de potencia generada por un parque eólico. Los horizontes de predicción considerados van de una a seis horas. Como primer paso, se ha elaborado una metodología para caracterizar rampas en series temporales. La denominada función-rampa está basada en la transformada wavelet y proporciona un índice en cada paso temporal. Este índice caracteriza la intensidad de rampa en base a los gradientes de potencia experimentados en un rango determinado de escalas temporales. Se han implementado tres tipos de modelos predictivos de cara a evaluar el papel que juega la complejidad de un modelo en su desempeño: modelos lineales autorregresivos (AR), modelos de coeficientes variables (VCMs) y modelos basado en redes neuronales (ANNs). Los modelos se han entrenado en base a la minimización del error cuadrático medio y la configuración de cada uno de ellos se ha determinado mediante validación cruzada. De cara a analizar la contribución del estado macroescalar de la atmósfera en la predicción de rampas, se ha propuesto una metodología que permite extraer, a partir de las salidas de modelos meteorológicos, información relevante para explicar la ocurrencia de estos eventos. La metodología se basa en el análisis de componentes principales (PCA) para la síntesis de la datos de la atmósfera y en el uso de la información mutua (MI) para estimar la dependencia no lineal entre dos señales. Esta metodología se ha aplicado a datos de reanálisis generados con un modelo de circulación general (GCM) de cara a generar variables exógenas que posteriormente se han introducido en los modelos predictivos. Los casos de estudio considerados corresponden a dos parques eólicos ubicados en España. Los resultados muestran que el modelado de la serie de potencias permitió una mejora notable con respecto al modelo predictivo de referencia (la persistencia) y que al añadir información de la macroescala se obtuvieron mejoras adicionales del mismo orden. Estas mejoras resultaron mayores para el caso de rampas de bajada. Los resultados también indican distintos grados de conexión entre la macroescala y la ocurrencia de rampas en los dos parques considerados. Abstract One of the main drawbacks of wind energy is that it exhibits intermittent generation greatly depending on environmental conditions. Wind power forecasting has proven to be an effective tool for facilitating wind power integration from both the technical and the economical perspective. Indeed, system operators and energy traders benefit from the use of forecasting techniques, because the reduction of the inherent uncertainty of wind power allows them the adoption of optimal decisions. Wind power integration imposes new challenges as higher wind penetration levels are attained. Wind power ramp forecasting is an example of such a recent topic of interest. The term ramp makes reference to a large and rapid variation (1-4 hours) observed in the wind power output of a wind farm or portfolio. Ramp events can be motivated by a broad number of meteorological processes that occur at different time/spatial scales, from the passage of large-scale frontal systems to local processes such as thunderstorms and thermally-driven flows. Ramp events may also be conditioned by features related to the wind-to-power conversion process, such as yaw misalignment, the wind turbine shut-down and the aerodynamic interaction between wind turbines of a wind farm (wake effect). This work is devoted to wind power ramp forecasting, with special focus on the connection between the global scale and ramp events observed at the wind farm level. The framework of this study is the point-forecasting approach. Time series based models were implemented for very short-term prediction, this being characterised by prediction horizons up to six hours ahead. As a first step, a methodology to characterise ramps within a wind power time series was proposed. The so-called ramp function is based on the wavelet transform and it provides a continuous index related to the ramp intensity at each time step. The underlying idea is that ramps are characterised by high power output gradients evaluated under different time scales. A number of state-of-the-art time series based models were considered, namely linear autoregressive (AR) models, varying-coefficient models (VCMs) and artificial neural networks (ANNs). This allowed us to gain insights into how the complexity of the model contributes to the accuracy of the wind power time series modelling. The models were trained in base of a mean squared error criterion and the final set-up of each model was determined through cross-validation techniques. In order to investigate the contribution of the global scale into wind power ramp forecasting, a methodological proposal to identify features in atmospheric raw data that are relevant for explaining wind power ramp events was presented. The proposed methodology is based on two techniques: principal component analysis (PCA) for atmospheric data compression and mutual information (MI) for assessing non-linear dependence between variables. The methodology was applied to reanalysis data generated with a general circulation model (GCM). This allowed for the elaboration of explanatory variables meaningful for ramp forecasting that were utilized as exogenous variables by the forecasting models. The study covered two wind farms located in Spain. All the models outperformed the reference model (the persistence) during both ramp and non-ramp situations. Adding atmospheric information had a noticeable impact on the forecasting performance, specially during ramp-down events. Results also suggested different levels of connection between the ramp occurrence at the wind farm level and the global scale.

Wind intermittency and CO2 reductions: the case of the spanish power system

Renewable energy sources are believed to reduce drastically greenhouse gas emissions that would otherwise be generated from fossil fuels used to generate electricity. This implies that a unit of renewable energy will replace a unit of fossil-fuel, with its CO2 emissions, on an equivalent basis (with no other effects on the grid). But, the fuel economy and emissions in the existing power systems are not proportional with the electricity production of intermittent sources due to cycling of the fossil fuel plants that make up the balance of the grid (i.e. changing the power output makes thermal units to operate less efficiently). This study focuses in the interactions between wind generation and thermal plants cycling, by establishing the levels of extra fuel use caused by decreased efficiencies of fossil back-up for wind electricity in Spain. We analyze the production of all thermal plants in 2011, studying different scenarios where wind penetration causes major deviations in programming, while we define a procedure for quantifying the carbon reductions by using emission factors and efficiency curves from the existing installations. The objectives are to discuss the real contributions of renewable energies to the environmental targets as well as suggest alternatives that would improve the reliability of future power systems.