A joint probability density function of wind speed and direction for wind energy analysis

A very flexible joint probability density function of wind speed and direction is presented in this paper for use in wind energy analysis. A method that enables angular–linear distributions to be obtained with specified marginal distributions has been used for this purpose. For the marginal distribution of wind speed we use a singly truncated from below Normal–Weibull mixture distribution. The marginal distribution of wind direction comprises a finite mixture of von Mises distributions. The proposed model is applied in this paper to wind direction and wind speed hourly data recorded at several weather stations located in the Canary Islands (Spain). The suitability of the distributions is judged from the coefficient of determination R².

The conclusions reached are that the joint distribution proposed in this paper: (a) can represent unimodal, bimodal and bitangential wind speed frequency distributions, (b) takes into account the frequency of null winds, (c) represents the wind direction regimes in zones with several modes or prevailing wind directions, (d) takes into account the correlation between wind speeds and its directions. It can therefore be used in several tasks involved in the evaluation process of the wind resources available at a potential site. We also conclude that, in the case of the Canary Islands, the proposed model provides better fits in all the cases analysed than those obtained with the models used in the specialised literature on wind energy.     

Fitting wind speed distributions: A case study

The wind speed data represented in the form of frequency curves show the shape of a potential model. The Weibull and Lognormal models are used for this purpose, with hourly mean wind speed data. This study deals with the estimation of the annual Weibull and Lognormal parameters from 20 locations in Navarre. The suitability of both distributions is judged from the R² coefficient with a linear regression for the Weibull distribution and a nonlinear regression for the Lognormal distribution. Both approaches give a good fit, giving better results for the Weibull distribution. A comparison between the estimation and the production for a wind farm is offered.     

Operational analysis of an innovative wind powered reverse osmosis system installed in the Canary Islands

This paper presents an operational analysis of the prototype of an innovative fully autonomous wind powered desalination system. The system consists of a wind farm, made up of two wind turbines and a flywheel, which operates in isolation from the conventional power grids and which supplies the energy needs of a group of eight reverse osmosis (RO) modules throughout the complete desalination process (from the pumping of sea water to the storage of the product water), as well as the energy requirements of the control subsystems. The analysis of the electrical and hydraulic results obtained from this prototype, installed on the island of Gran Canaria in the Canarian Archipelago, shows the technical feasibility of the system design and the automatic operational strategy programmed for it. Amongst other tasks, the automatic operational strategy controls the number of RO plants that have to be connected or disconnected at any given moment in order to match the variable wind energy supply. The results obtained thus far have not revealed any significant variation in the level of quality or average volume of the product water, nor any physical deterioration to the main components of the system as a result of the start-ups and shut-downs required as a result of the variations in the wind energy supply or oscillations of the electrical parameters of voltage and frequency. In conclusion, the system under analysis can be applied to sea water desalination, both on a small and large scale, in coastal regions with a scarcity of water for domestic and/or agricultural use but with wind energy resources.     

A study of the wind speed and wind energy availability in Bangladesh

Characteristics of wind speed data for three recent years, recorded at 14 stations of the Bangladesh Meteorological Department, have been studied. The data have been used to compute the monthly average wind speed and the wind energy availability for the stations. Average values of monthly wind speed for 1931–1960 have been employed to obtain the energy availability from the energy pattern factor, and the two sets of results have been compared. It has been found that, for the Chittagong station, the frequency distributions have good fits of the Weibull type.     

Assessment of wind energy in Iran: A review

In this study, a ten minute period measuring wind speed data for year 2007 at 10 m, 30 m and 40 m heights for different places in Iran, has been statistically analyzed to determine the potential of wind power generation. Sixty eight sites have been studied. The objective is to evaluate the most important characteristics of wind energy in the studied sites. The statistical attitudes permit us to estimate the mean wind speed, the wind speed distribution function, the mean wind power density and the wind rose in the site at three different heights. Some local phenomena are also considered in the characterization of the site.     

A review of wind energy technologies

Energy is an essential ingredient of socio-economic development and economic growth. Renewable energy sources like wind energy is indigenous and can help in reducing the dependency on fossil fuels. Wind is the indirect form of solar energy and is always being replenished by the sun. Wind is caused by differential heating of the earth's surface by the sun. It has been estimated that roughly 10 million MW of energy are continuously available in the earth's wind. Wind energy provides a variable and environmental friendly option and national energy security at a time when decreasing global reserves of fossil fuels threatens the long-term sustainability of global economy. This paper reviews the wind resources assessment models, site selection models and aerodynamic models including wake effect. The different existing performance and reliability evaluation models, various problems related to wind turbine components (blade, gearbox, generator and transformer) and grid for wind energy system have been discussed. This paper also reviews different techniques and loads for design, control systems and economics of wind energy conversion system.     

Public perceptions of wind energy developments: Case studies from New Zealand

Although the public generally hold positive attitudes towards wind energy, proposals for the construction of new wind farms are often met with strong resistance. In New Zealand, where the government has recently introduced ambitious policy targets for renewable energy generation, negative perceptions of wind farms are increasingly evident and have the potential to prevent the achievement of these targets. This research sets out to examine what influences social resistance to wind farms in New Zealand. Drawing from public submissions on three wind farm proposals, a framework developed by Devine-Wright [Devine-Wright, P., 2005a. Beyond NIMBYism: towards an integrated Framework for Understanding Public Perceptions of Wind Energy. Wind Energy 8, 125–139.] was used as the basis for identification of factors affecting public perceptions of wind farms. The research found firstly that there was no apparent relationship between the proximity of submitters to a proposed wind farm and their likelihood of having a negative perception of the proposal. A wide range of factors written in submissions appeared to have affected the submitter's decision to support or oppose the wind farm proposal. Some of these were consistent with Devine-Wright's findings, but ten further factors were added to the framework to adequately cover the aspects raised in submissions. The findings have implications for the achievement of New Zealand's energy policy aspirations.     

The cost of transmission for wind energy in the United States: A review of transmission planning studies

Rapid development of wind capacity in the United States has been coupled with a concern that increasing wind capacity will require substantial transmission infrastructure. This report summarizes the implied transmission cost per kW of wind from a sample of 40 transmission studies. This sample of studies, completed from 2001 to 2008, covers a broad geographic area across the U.S. The primary goal in the review is to develop a better understanding of the transmission costs needed to access increasing quantities of wind generation. A secondary goal is to gain a better appreciation of the differences in transmission planning approaches, in order to identify those methodologies that seem most able to estimate the incremental transmission costs associated with wind development. The total range in transmission costs per kW of wind implicit in the study sample is vast - ranging from $0/kW to over $1500/kW. The median cost of transmission from all scenarios in the sample is $300/kW, roughly 15-20% of the cost of building a wind project. The median cost of transmission is near the upper end of the range implied by two higher-level assessments of transmission required to provide 20% wind electricity in the U.S. by 2030.     

A review of wind energy activities in Japan

By 2012, Japan must cut down on its annual emissions of greenhouse gases by 6% from 1990 levels. However, greenhouse emissions increased by 6.4% in 2006. More effective responses to reduce greenhouse gases are required. Attention is currently focused on increasing the use of renewable energy, and wind energy has received a lot of attention. The national target for wind power capacity in the year 2010 is 3000 MW; however, there are many barriers to the development of wind energy. Japan's climate differs from that of the European Union countries. It often experiences typhoons and lightning strikes because of its meteorological characteristics. Wind has a stronger turbulence level due to the complex terrain. Furthermore, power fluctuation of wind causes power system problems because of issues related to the grid connection. Many endeavours have been made to find the best solutions for these problems. This paper reviews the wind energy activities in Japan, including wind resources, market trends, environment, prospects and research and development. Copyright © 2009 John Wiley & Sons, Ltd.     

Copula-based joint distribution analysis of wind speed and wind direction: Wind energy development for Hong Kong

Accurate and reliable assessment of wind energy potential has important implication to the wind energy industry. Most previous studies on wind energy assessment focused solely on wind speed, whereas the dependence of wind energy on wind direction was much less considered and documented. In this paper, a copula-based method is proposed to better characterize the direction-related wind energy potential at six typical sites in Hong Kong. The joint probability density function (JPDF) of wind speed and wind direction is constructed by a series of copula models. It shows that Frank copula has the best performance to fit the JPDF at hilltop and offshore sites while Gumbel copula outperforms other models at urban sites. The derived JPDFs are applied to estimate the direction-related wind power density at the considered sites. The obtained maximum direction-related wind energy density varies from 41.3 W/m² at an urban site to 507.9 W/m² at a hilltop site. These outcomes are expected to facilitate accurate micro-site selection of wind turbines, thereby improving the economic benefits of wind farms in Hong Kong. Meanwhile, the developed copula-based method provides useful references for further investigations regarding direction-related wind energy assessments at various terrain regions. Notably, the proposed copula-based method can also be applied to characterize the direction-related wind energy potential somewhere other than Hong Kong.     

Comparative analysis of support mechanisms for renewable energy technologies using probability distributions

In this paper, a probabilistic model is developed to assess the effects of different support mechanisms on the financial return of small-scale hydroelectric, wind energy and solar PV systems. Besides, the results from this model are used to compare the economic effectiveness of each mechanism in increasing the profitability of these projects. We focus on three renewable energy support mechanisms: governmental grants, feed in tariffs (FiT) and renewable energy certificates (RECs). We also consider the effect of the carbon credits on the net present value of renewable projects and compare it with the other support mechanisms. The simulation results demonstrate that the feed in tariffs is the best mechanism to increase the profitability of solar PV systems and wind energy projects. Conversely, green certificate mechanism favors the most competitive technology as the hydropower. In addition, it is shown that the governmental grants and carbon credits are secondary support mechanisms compared to FiT and RECs. And, the carbon credits play a more important role than governmental grants as the energy output of the system increase. Finally, it can be concluded that the efficiency of the support mechanisms varies depending on stage of development of the renewable technologies that are implemented.     

Climate change impacts on wind energy: A review

Expansion of wind energy installed capacity is poised to play a key role in climate change mitigation. However, wind energy is also susceptible to global climate change. Some changes associated with climate evolution will likely benefit the wind energy industry while other changes may negatively impact wind energy developments, with such ‘gains and losses’ depending on the region under consideration. Herein we review possible mechanisms by which global climate variability and change may influence the wind energy resource and operating conditions, summarize some of the tools that are being employed to quantify these effects and the sources of uncertainty in making such projections, and discuss results of studies conducted to date. We present illustrative examples of research from northern Europe. Climate change analyses conducted for this region, which has shown considerable penetration of wind energy, imply that in the near-term (i.e. to the middle of the current century) natural variability exceeds the climate change signal in the wind energy resource and extreme wind speeds, but there will likely be a decline in icing frequency and sea ice both of which will tend to benefit the wind energy industry. By the end of the twenty-first century there is evidence for small magnitude changes in the wind resource (though the sign of the change remains uncertain), for increases in extreme wind speeds, and continued declines in sea ice and icing frequencies. Thus the current state-of-the-art suggests no detectable change in the wind resource or other external conditions that could jeopardize the continued exploitation of wind energy in northern Europe, though further research is needed to provide greater confidence in these projections.     

A review on the forecasting of wind speed and generated power

In the world, wind power is rapidly becoming a generation technology of significance. Unpredictability and variability of wind power generation is one of the fundamental difficulties faced by power system operators. Good forecasting tools are urgent needed under the relevant issues associated with the integration of wind energy into the power system. This paper gives a bibliographical survey on the general background of research and developments in the fields of wind speed and wind power forecasting. Based on the assessment of wind power forecasting models, further direction for additional research and application is proposed.     

Application of Bayesian model averaging in modeling long-term wind speed distributions

Accurate estimation of wind speed distribution is critical to the assessment of wind energy potential, the site selection of wind farms, and the operations management of wind power conversion systems. This paper proposes a new approach for deriving more reliable and robust wind speed distributions than conventional statistical modeling approach. This approach combines Bayesian model averaging (BMA) and Markov Chain Monte Carlo (MCMC) sampling methods. The derived BMA probability density function (PDF) of the wind speed is an average of the model PDFs included in the model space weighted by their posterior probabilities over the sample data. MCMC method provides an effective way for numerically computing marginal likelihoods, which are essential for obtaining the posterior model probabilities. The approach is applied to multiple sites with high wind power potential in North Dakota. The wind speed data at these sites are the mean hourly wind speeds collected over two years. It is demonstrated that indeed none of the conventional statistical models such as Weibull distribution are universally plausible for all the sites. However, the BMA approach can provide comparative reliability and robustness in describing the long-term wind speed distributions for all sites, while making the traditional model comparison based on goodness-of-fit statistics unnecessary.     

基于瑞利概率分布的风力发电系统中组合风速的改进及建模仿真

风速作为风力发电机组的输入信号,其模拟的正确性直接影响着整个风力发电系统的性能分析与研究。通常计算机仿真所得风速是通过对不同风速的简单组合而实现的某个地区及时间段的平均风速,仿真风速无法体现自然风的多样性。针对此问题,文章提出一种实现自然风的方法。依据某风电场风资源评估报告所给出的风频分布曲线,以瑞利分布规律随机配置各种风速信号,在Matlab/Simulink的运行环境下,仿真出改进的风速,使不同风速随机分配,并且在某个时间段内各种风速出现的概率符合瑞利概率分布。据此方法所得风速更加接近实际自然风,且涵盖从切入风速到切出风速范围内所有风速类型,对风力发电系统的性能分析与研究具有一定的实际意义。     

A comparison between wind speed distributions derived from the maximum entropy principle and Weibull distribution. Case of study; six regions of Algeria

The knowledge of the probability density function of wind speed is of paramount importance in many applications such as wind energy conversion systems and bridges construction. An accurate determination of the probability distribution of wind speed allows an efficient use of wind energy, thus rendering wind energy conversion system more productive. In the present paper, the maximum entropy principle (MEP) is used to derive a family of pre-exponential distributions in order to fit wind speed distributions. Using averaged hourly wind speed of six different regions in Algeria, it has been found that the proposed pre-exponential distributions fit the wind speed distributions better than the conventional Weibull distributions in terms of root mean square error. However, it has been found also that MEP based distributions have shown some practical limitations such as the choice of pre-exponential order and interval of definition.     

A comparative multi-disciplinary policy review in wind energy developments in Europe

Over recent decades, European Union countries have committed to increasing their electricity production from renewable energy sources (RESs). Wind energy plays a significant role in a sustainable future. This paper presents a political, economic, social, technological, legal and environmental analysis. Although these countries have made many improvements in their legal frameworks aiming to attract investors and boost the RE sector, there are still challenges. The UK focuses on offshore wind energy, adjusts the economic strategy and changes the legislation context. Germany has the healthiest economic conditions, as it keeps following its initiative to design a new programme for an energy transition from conventional to RESs with emphasis on the onshore. Greece has only a few installations and much room for development but needs to make further changes in the legislation and economy so as to attract more investors in the long term. The purpose of this research is to analyse, highlight and discuss vital aspects of these countries as well as the European environment, with reference to their current wind energy activities. Ultimately, it attempts to give a wider perspective and to serve as a guide for future studies on the wind energy sector.     

A new probabilistic method to estimate the long-term wind speed characteristics at a potential wind energy conversion site

This paper proposes the use of a new Measure-Correlate-Predict (MCP) method to estimate the long-term wind speed characteristics at a potential wind energy conversion site. The proposed method uses the probability density function of the wind speed at a candidate site conditioned to the wind speed at a reference site. Contingency-type bivariate distributions with specified marginal distributions are used for this purpose. The proposed model was applied in this paper to wind speeds recorded at six weather stations located in the Canary Islands (Spain). The conclusion reached is that the method presented in this paper, in the majority of cases, provides better results than those obtained with other MCP methods used for purposes of comparison. The metrics employed in the analysis were the coefficient of determination (R²) and the root relative squared error (RRSE). The characteristics that were analysed were the capacity of the model to estimate the long-term wind speed probability distribution function, the long-term wind power density probability distribution function and the long-term wind turbine power output probability distribution function at the candidate site.     

Comparing the statistical distributions of energy efficiency in manufacturing: meta-analysis of 24 Case studies to develop industry-specific energy performance indicators (EPI)

There is growing interest among policy makers and others regarding the role that industrial energy efficiency can play in mitigation of climate change. For over 10 years, the US Environmental Protection Agency (EPA) has supported the development of sector-specific industrial energy efficiency case studies using statistical analysis of plant level on energy use, controlling for a variety of plant production activities and characteristics. These case studies are the basis for the ENERGY STAR^® Energy Performance Indicators (EPIs). These case studies fill an important gap by estimating the distribution of efficiency using detailed, sector-specific, plant-level data. These estimated distributions allow Energy Star to create “energy-efficient plant benchmarks” in a variety of industries using the upper quartile of the estimated efficiency distribution. Case studies have been conducted for 14 broad industries, 2 dozen sectors, and many more detailed product types. This paper is a meta-analysis of the approach that has been used in this research and the general findings regarding the estimated distribution of performance within and across industries. We find that there are few sectors that are well represented by a simple “energy per widget” benchmark that less energy-intensive sectors tend to exhibit a wider range of within-industry efficiency than energy-intensive sectors, but changes over time in the level and range of energy efficiency do not reveal any single pattern.     

Characterization of the wind speed variability and future change in the Iberian Peninsula and the Balearic Islands

Wind energy is susceptible to global climate change because it could alter the wind patterns. Then, improvement of our knowledge of wind field variability is crucial to optimize the use of wind resources in a given region. Here, we quantify the effects of climate change on the surface wind speed field over the Iberian Peninsula and Balearic Islands using an ensemble of four regional climate models driven by a global climate model. Regions of the Iberian Peninsula with coherent temporal variability in wind speed in each of the models are identified and analysed using cluster analysis. These regions are continuous in each model and exhibit a high degree of overlap across the models. The models forced by the European Reanalysis Interim (ERA‐Interim) reanalysis are validated against the European Climate Assessment and Dataset wind. We find that regional models are able to simulate with reasonable skill the spatial distribution of wind speed at 10 m in the Iberian Peninsula, identifying areas with common wind variability. Under the Special Report on Emissions Scenarios (SRES) A1B climate change scenario, the wind speed in the identified regions for 2031–2050 is up to 5% less than during the 1980–1999 control period for all models. The models also agree on the time evolution of spatially averaged wind speed in each region, showing a negative trend for all of them. These tendencies depend on the region and are significant at p = 5% or slightly more for annual trends, while seasonal trends are not significant in most of the regions and seasons. Copyright © 2015 John Wiley & Sons, Ltd.