Use of STATCOM in wind farms with fixed-speed generators for grid code compliance

The increasing penetration of wind energy into power systems has pushed grid operators to set new requirements for this kind of generating plants in order to keep acceptable and reliable operation of the system. In addition to the low voltage ride through capability, wind farms are required to participate in voltage support, stability enhancement and power quality improvement. This paper presents a solution for wind farms with fixed-speed generators based on the use of STATCOM with braking resistor and additional series impedances, with an adequate control strategy. The focus is put on guaranteeing the grid code compliance when the wind farm faces an extensive series of grid disturbances.

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.

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.

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.

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.

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.

MultiPARTES: Multicore Virtualization for Mixed-Criticality Systems

Modern embedded applications typically integrate a multitude of functionalities with potentially different criticality levels into a single system. Without appropriate preconditions, the integration of mixed-criticality subsystems can lead to a significant and potentially unacceptable increase of engineering and certification costs. A promising solution is to incorporate mechanisms that establish multiple partitions with strict temporal and spatial separation between the individual partitions. In this approach, subsystems with different levels of criticality can be placed in different partitions and can be verified and validated in isolation. The MultiPARTES FP7 project aims at supporting mixed- criticality integration for embedded systems based on virtualization techniques for heterogeneous multicore processors. A major outcome of the project is the MultiPARTES XtratuM, an open source hypervisor designed as a generic virtualization layer for heterogeneous multicore. MultiPARTES evaluates the developed technology through selected use cases from the offshore wind power, space, visual surveillance, and automotive domains. The impact of MultiPARTES on the targeted domains will be also discussed. In a number of ongoing research initiatives (e.g., RECOMP, ARAMIS, MultiPARTES, CERTAINTY) mixed-criticality integration is considered in multicore processors. Key challenges are the combination of software virtualization and hardware segregation and the extension of partitioning mechanisms to jointly address significant non-functional requirements (e.g., time, energy and power budgets, adaptivity, reliability, safety, security, volume, weight, etc.) along with development and certification methodology.

A wavelet-based approach for large wind power ramp characterisation

A wavelet-based approach for large wind power ramp characterisation

Comparisson of hydrogen applications for storage and energy vector

For the decades to come can be foreseen that electricity and water will keep be playing a key role in the countries development, both can be considered the most important energy vectors and its control can be crucial for governments, companies and leaders in general. Energy is essential for all human activities and its availability is critical to economic and social development. In particular, electricity, a form of energy, is required to produce goods, to provide medical assistance and basic civic services in education, to assure availability of clean water, to create conducive environment for prosperity and improvement, and to keep an acceptable quality of life. The way in which electricity is generated from different resources varies through the different countries. Nuclear energy controlled within reactors to steam production, gas, fuel-oil and coal fired in power stations, water, solar and wind energy among others are employed, sometimes not very efficiently, to produce electricity. The so call energy mix of an individual country is formed up by the contribution of each resource or form of energy to the electricity generation market of the so country. During the last decade the establishment of proper energy mixes for countries has gained much importance, and energy drivers should enforce long term plans and policies. Hints, reports and guides giving tracks on energy resources contribution are been developed by noticeable organisations like the IEA (International Energy Agency) or the IAEA (International Atomic Energy Agency) and the WEC (World Energy Council). This paper evaluates energy issues the market and countries are facing today regarding energy mix scheduling and panorama. This paper revises and seeks to improve methodology available that are applicable on energy mix plan definition. Key Factors are identified, established and assessed through this paper for the common implementation, the themes driving the future energy mix methodology proposal. Those have a clear influence and are closely related to future environmental policies. Key Factors take into consideration sustainability, energy security, social and economic growth, climate change, air quality and social stability. The strength of the Key Factors application on energy system planning to different countries is contingent on country resources, location, electricity demand and electricity generation industry, technology available, economic situation and prospects, energy policy and regulation

Reproducing the Bolund experiment in wind tunnel

The Bolund experiment has been reproduced in a neutral boundary layer wind tunnel (WT) at scale 1:115 for two Reynolds numbers. All the results have been obtained for an incoming flow from the 270o wind direction (transect B in the Bolund experiment jargon). Vertical scans of the velocity field are obtained using non-time resolved two components particle image velocimetry. Time-resolved velocity time series with a three component hot-wire probe have been also measured for transects at 2 and 5 m height and in the vertical transects at met masts M6, M3 and M8 locations. Special attention has been devoted to the detailed characterization of the inflow in order to reduce uncertainties in future comparisons with other physical and numerical simulations. Emphasis is placed on the analysis of spectral functions of the undisturbed flow and those of the flow above the island. The result?s reproducibility and trustworthiness have been addressed through redundancy measurements using particle image velocimetry, two and three components hot-wire anemometry. The bias in the prediction of the mean speed is similar to the one reported during the Bolund experiment by the physical modellers. However, certain reduction of the bias in the estimation of the turbulent kinetic energy is achieved. TheWT results of spectra and cosprectra have revealed a behaviour similar to the full-scale measurements in some relevant locations, showing that WT modelling can contribute to provide valid information about these important structural loading factors.

Frequency control support of a wind-solar isolated system by a hydropower plant with long tail-race tunnel

Pumped storage hydro plants (PSHP) can provide adequate energy storage and frequency regulation capacities in isolated power systems having significant renewable energy resources. Due to its high wind and solar potential, several plans have been developed for La Palma Island in the Canary archipelago, aimed at increasing the penetration of these energy sources. In this paper, the performance of the frequency control of La Palma power system is assessed, when the demand is supplied by the available wind and solar generation with the support of a PSHP which has been predesigned for this purpose. The frequency regulation is provided exclusively by the PSHP. Due to topographic and environmental constraints, this plant has a long tail-race tunnel without a surge tank. In this configuration, the effects of pressure waves cannot be neglected and, therefore, usual recommendations for PID governor tuning provide poor performance. A PI governor tuning criterion is proposed for the hydro plant and compared with other criteria according to several performance indices. Several scenarios considering solar and wind energy penetration have been simulated to check the plant response using the proposed criterion. This tuning of the PI governor maintains La Palma system frequency within grid code requirements.

Hybrid wind-diesel power plant valuation and optimization using real option theory and dynamic programming

El sistema de energía eólica-diesel híbrido tiene un gran potencial en la prestación de suministro de energía a comunidades remotas. En comparación con los sistemas tradicionales de diesel, las plantas de energía híbridas ofrecen grandes ventajas tales como el suministro de capacidad de energía extra para "microgrids", reducción de los contaminantes y emisiones de gases de efecto invernadero, y la cobertura del riesgo de aumento inesperado del precio del combustible. El principal objetivo de la presente tesis es proporcionar nuevos conocimientos para la evaluación y optimización de los sistemas de energía híbrido eólico-diesel considerando las incertidumbres. Dado que la energía eólica es una variable estocástica, ésta no puede ser controlada ni predecirse con exactitud. La naturaleza incierta del viento como fuente de energía produce serios problemas tanto para la operación como para la evaluación del valor del sistema de energía eólica-diesel híbrido. Por un lado, la regulación de la potencia inyectada desde las turbinas de viento es una difícil tarea cuando opera el sistema híbrido. Por otro lado, el bene.cio económico de un sistema eólico-diesel híbrido se logra directamente a través de la energía entregada a la red de alimentación de la energía eólica. Consecuentemente, la incertidumbre de los recursos eólicos incrementa la dificultad de estimar los beneficios globales en la etapa de planificación. La principal preocupación del modelo tradicional determinista es no tener en cuenta la incertidumbre futura a la hora de tomar la decisión de operación. Con lo cual, no se prevé las acciones operativas flexibles en respuesta a los escenarios futuros. El análisis del rendimiento y simulación por ordenador en el Proyecto Eólico San Cristóbal demuestra que la incertidumbre sobre la energía eólica, las estrategias de control, almacenamiento de energía, y la curva de potencia de aerogeneradores tienen un impacto significativo sobre el rendimiento del sistema. En la presente tesis, se analiza la relación entre la teoría de valoración de opciones y el proceso de toma de decisiones. La opción real se desarrolla con un modelo y se presenta a través de ejemplos prácticos para evaluar el valor de los sistemas de energía eólica-diesel híbridos. Los resultados muestran que las opciones operacionales pueden aportar un valor adicional para el sistema de energía híbrida, cuando esta flexibilidad operativa se utiliza correctamente. Este marco se puede aplicar en la optimización de la operación a corto plazo teniendo en cuenta la naturaleza dependiente de la trayectoria de la política óptima de despacho, dadas las plausibles futuras realizaciones de la producción de energía eólica. En comparación con los métodos de valoración y optimización existentes, el resultado del caso de estudio numérico muestra que la política de operación resultante del modelo de optimización propuesto presenta una notable actuación en la reducción del con- sumo total de combustible del sistema eólico-diesel. Con el .n de tomar decisiones óptimas, los operadores de plantas de energía y los gestores de éstas no deben centrarse sólo en el resultado directo de cada acción operativa, tampoco deberían tomar decisiones deterministas. La forma correcta es gestionar dinámicamente el sistema de energía teniendo en cuenta el valor futuro condicionado en cada opción frente a la incertidumbre. ABSTRACT Hybrid wind-diesel power systems have a great potential in providing energy supply to remote communities. Compared with the traditional diesel systems, hybrid power plants are providing many advantages such as providing extra energy capacity to the micro-grid, reducing pollution and greenhouse-gas emissions, and hedging the risk of unexpected fuel price increases. This dissertation aims at providing novel insights for assessing and optimizing hybrid wind-diesel power systems considering the related uncertainties. Since wind power can neither be controlled nor accurately predicted, the energy harvested from a wind turbine may be considered a stochastic variable. This uncertain nature of wind energy source results in serious problems for both the operation and value assessment of the hybrid wind-diesel power system. On the one hand, regulating the uncertain power injected from wind turbines is a difficult task when operating the hybrid system. On the other hand, the economic profit of a hybrid wind-diesel system is achieved directly through the energy delivered to the power grid from the wind energy. Therefore, the uncertainty of wind resources has increased the difficulty in estimating the total benefits in the planning stage. The main concern of the traditional deterministic model is that it does not consider the future uncertainty when making the dispatch decision. Thus, it does not provide flexible operational actions in response to the uncertain future scenarios. Performance analysis and computer simulation on the San Cristobal Wind Project demonstrate that the wind power uncertainty, control strategies, energy storage, and the wind turbine power curve have a significant impact on the performance of the system. In this dissertation, the relationship between option pricing theory and decision making process is discussed. A real option model is developed and presented through practical examples for assessing the value of hybrid wind-diesel power systems. Results show that operational options can provide additional value to the hybrid power system when this operational flexibility is correctly utilized. This framework can be applied in optimizing short term dispatch decisions considering the path-dependent nature of the optimal dispatch policy, given the plausible future realizations of the wind power production. Comparing with the existing valuation and optimization methods, result from numerical example shows that the dispatch policy resulting from the proposed optimization model exhibits a remarkable performance in minimizing the total fuel consumption of the wind-diesel system. In order to make optimal decisions, power plant operators and managers should not just focus on the direct outcome of each operational action; neither should they make deterministic decisions. The correct way is to dynamically manage the power system by taking into consideration the conditional future value in each option in response to the uncertainty.

Emulation of an OWC Ocean Energy Plant With PMSG and Irregular Wave Model

Ocean energy is a promising resource for renewable electricity generation that presents many advantages, such as being more predictable than wind energy, but also some disadvantages such as large and slow amplitude variations in the generated power. This paper presents a hardware-in-the-loop prototype that allows the study of the electric power profile generated by a wave power plant based on the oscillating water column (OWC) principle. In particular, it facilitates the development of new solutions to improve the intermittent profile of the power fed into the grid or the test of the OWC behavior when facing a voltage dip. Also, to obtain a more realistic model behavior, statistical models of real waves have been implemented.