Nebraska Wind Power: A Comparitive Study of Knowledge and Attitudes

Abstract The goal of this project is to assess the knowledge and attitudes of Nebraskans on the issue of wind power. The point of this research is to learn whether the presence of wind power has a positive effect on a person’s knowledge about and attitudes toward wind power and wind turbines. Using mail surveys, qualitative and quantitative data were collected from the towns of Pierce and Ainsworth Nebraska. The surveys aided in seeing patterns of knowledge about wind power and wind turbines and positive and negative attitudes and major concerns regarding wind power.

Investigation of the Impacts of Large-Scale Wind Power Penetration on the Angle and Voltage Stability of Power Systems

The complexity of power systems has increased in recent years due to the operation of existing transmission lines closer to their limits, using flexible AC transmission system (FACTS) devices, and also due to the increased penetration of new types of generators that have more intermittent characteristics and lower inertial response, such as wind generators. This changing nature of a power system has considerable effect on its dynamic behaviors resulting in power swings, dynamic interactions between different power system devices, and less synchronized coupling. This paper presents some analyses of this changing nature of power systems and their dynamic behaviors to identify critical issues that limit the large-scale integration of wind generators and FACTS devices. In addition, this paper addresses some general concerns toward high compensations in different grid topologies. The studies in this paper are conducted on the New England and New York power system model under both small and large disturbances. From the analyses, it can be concluded that high compensation can reduce the security limits under certain operating conditions, and the modes related to operating slip and shaft stiffness are critical as they may limit the large-scale integration of wind generation.

How has Spain become a leader in the wind energy industry during the last decade?

During the last decade, wind energy has been the fastest growing renewable source of energy worldwide. Limited sources of fossil fuel in addition to the negative effects of greenhouse gas emissions on the environment have led many countries to support development of renewable energies such as wind energy. Spain as the fourth biggest producer of wind energy plays an important global role in wind industry. In this paper, some important factors in the rapid growth of wind energy in Spain such as policy design, industry and technology, economic environment and social acceptance have been studied. The objective of this study is to introduce a model based on the successful development of wind energy in Spain which can be implemented by other countries

Influence of local wind speed and direction on wind power dynamics - Application to offshore very short-term forecasting

Wind power time series usually show complex dynamics mainly due to non-linearities related to the wind physics and the power transformation process in wind farms. This article provides an approach to the incorporation of observed local variables (wind speed and direction) to model some of these effects by means of statistical models. To this end, a benchmarking between two different families of varying-coefficient models (regime-switching and conditional parametric models) is carried out. The case of the offshore wind farm of Horns Rev in Denmark has been considered. The analysis is focused on one-step ahead forecasting and a time series resolution of 10 min. It has been found that the local wind direction contributes to model some features of the prevailing winds, such as the impact of the wind direction on the wind variability, whereas the non-linearities related to the power transformation process can be introduced by considering the local wind speed. In both cases, conditional parametric models showed a better performance than the one achieved by the regime-switching strategy. The results attained reinforce the idea that each explanatory variable allows the modelling of different underlying effects in the dynamics of wind power time series.

Pre-Feasibility Study of Hybrid Wind Power-H-2 System Connected to Electrical Grid

The growth of wind power as an electric energy source is profitable from an environmental point of view and improves the energetic independence of countries with little fossil fuel resources. However, the wind resource randomness poses a great challenge in the management of electric grids. This study raises the possibility of using hydrogen as a mean to damp the variability of the wind resource. Thus, it is proposed the use of all the energy produced by a typical wind farm for hydrogen generation, that will in turn be used after for suitable generation of electric energy according to the operation rules in a liberalized electric market.

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.

A wavelet-based approach for large wind power ramp characterisation

A wavelet-based approach for large wind power ramp characterisation

The cup anemometer, a fundamental meteorological instrument for the wind energy industry

The cup anemometer has been used widely by the wind energy industry since its early beginning, covering two fundamental aspects: wind mill performance control and wind energy production forecast. Furthermore, despite modern technological advances such as LIDAR and SODAR, the cup anemometer remains clearly the most used instrument by the wind energy industry. Together with the major advantages of this instrument (precision, robustness), some issues must be taken into account by scientists and researchers when using it. Overspeeding, interaction with stream wakes due to allocation on masts and wind- mills, loss of performance due to wear and tear, change of performance due to different climatic conditions, checking of the maintenance status and recalibration, etc. In the present work a review of the research campaigns carried out at the IDR/UPM Institute to analyze cup anemometer performance is included. Several aspects of this instrument are examined: the calibration process, the loss of performances due to aging and wear and tear, the effect of changes of air density, the rotor aerodynamics, and the harmonic terms contained in the anemometer output signal and their possible relation to the anemometer performances.

The Cup Anemometer, a Fundamental Meteorological Instrument for the Wind Energy Industry. Research at the IDR/UPM Institute

The results of several research campaigns investigating cup anemometer performance carried out since 2008 at the IDR/UPM Institute are included in the present paper. Several analysis of large series of calibrations were done by studying the effect of the rotor’s geometry, climatic conditions during calibration, and anemometers’ ageing. More specific testing campaigns were done regarding the cup anemometer rotor aerodynamics, and the anemometer signals. The effect of the rotor’s geometry on the cup anemometer transfer function has been investigated experimentally and analytically. The analysis of the anemometer’s output signal as a way of monitoring the anemometer status is revealed as a promising procedure for detecting anomalies.

A dynamic factor model for mid-term forecasting of wind power generation

The main objective of this paper is the development and application of multivariate time series models for forecasting aggregated wind power production in a country or region. Nowadays, in Spain, Denmark or Germany there is an increasing penetration of this kind of renewable energy, somehow to reduce energy dependence on the exterior, but always linked with the increaseand uncertainty affecting the prices of fossil fuels. The disposal of accurate predictions of wind power generation is a crucial task both for the System Operator as well as for all the agents of the Market. However, the vast majority of works rarely onsider forecasting horizons longer than 48 hours, although they are of interest for the system planning and operation. In this paper we use Dynamic Factor Analysis, adapting and modifying it conveniently, to reach our aim: the computation of accurate forecasts for the aggregated wind power production in a country for a forecasting horizon as long as possible, particularly up to 60 days (2 months). We illustrate this methodology and the results obtained for real data in the leading country in wind power production: Denmark

Detecting and characterising ramp events in wind power time series

In order to implement accurate models for wind power ramp forecasting, ramps need to be previously characterised. This issue has been typically addressed by performing binary ramp/non-ramp classifications based on ad-hoc assessed thresholds. However, recent works question this approach. This paper presents the ramp function, an innovative wavelet- based tool which detects and characterises ramp events in wind power time series. The underlying idea is to assess a continuous index related to the ramp intensity at each time step, which is obtained by considering large power output gradients evaluated under different time scales (up to typical ramp durations). The ramp function overcomes some of the drawbacks shown by the aforementioned binary classification and permits forecasters to easily reveal specific features of the ramp behaviour observed at a wind farm. As an example, the daily profile of the ramp-up and ramp-down intensities are obtained for the case of a wind farm located in Spain

Big business in the Russian economy and politics under Putin's rule; The Russian power industry shortly before reforms; Ukrainian metallurgy: the economic link in the oligarchical system. OSW Study 5/2002

Big business in Russia: The pace of ownership transfer in the Russian economy has speeded up considerably over the last year. There has been a significant rise in the number of acquisitions of whole enterprises, and large blocks of shares in individual firms and plants. Similarly the number of mergers, bankruptcies and take-overs of failing firms by their strongest competitors has grown. The Russian power industry: This study is an overview of the current condition and principles on which the Russian power sector has been functioning so far. This analysis has been carried out against the background of the changes that have been taking place in the sector since the beginning of the 1990s. This text also contains a description of guidelines and progress made so far in implementing the reform of the Russian power industry, the draft of which was adopted by the government of the Russian Federation in summer 2001. However, the purpose of this study is not an economic analysis of the draft, but an attempt to present the political conditions and possible consequences of the transformations carried out in the Russian power sector. The final part attempts to evaluate the possibilities and threats related to the implementation of the reform in its present shape. Ukrainian metallurgy: The metallurgic sector, like the east-west transit of energy raw materials, is a strategic source of revenue for Ukraine. Over the last ten years, this sector has become Kiev's most important source of foreign currency inflows, accounting for over 40 per cent of its total export revenues. The growth of metallurgic production, which has continued almost without interruption since the mid-1990s, has contributed considerably to the increase in GDP which Ukraine showed in 2000, for the first time in its independent history.