Wind climate from the regional climate model REMO

Selected outputs from simulations with the regional climate model REMO from the Max Planck Institute, Hamburg, Germany were studied in connection with wind energy resource assessment. It was found that the mean wind characteristics based on observations from six mid‐latitude stations are well described by the standard winds derived from the REMO pressure data. The mean wind parameters include the directional wind distribution, directional and omni‐directional mean values and Weibull fitting parameters, spectral analysis and interannual variability of the standard winds. It was also found that, on average, the wind characteristics from REMO are in better agreement with observations than those derived from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) re‐analysis pressure data. The spatial correlation of REMO surface winds in Europe is consistent with that of the NCEP/NCAR surface winds, as well as published observations over Europe at synoptic scales. Therefore, REMO outputs are well suited for wind energy assessment application in Northern Europe. Copyright © 2009 John Wiley & Sons, Ltd.     

风切变指数在风电场风资源评估中的应用

以内蒙古地区3座70m高测风塔连续2年的实测数据来分析风切变指数的变化,结果表明:1)不同高度梯度的风切变指数受地面粗糙度及周围地形地貌的影响较大。2)计算相邻高度的风速时,采用相邻高度间的风切变指数计算得到的结果较好;计算相差较大的高度间风速时,采用拟合曲线得到的风切变指数计算得到的结果较好。3)利用3～25m/s的风切变指数计算各月风速及年均风速结果都与实测值最接近;而利用全部风速数据的风切变指数计算统计各月风速往往比实测值偏大;利用3～25m/s拟合曲线得到的风切变指数统计各月风速比实测值偏小。     

基于多任务学习的风速实时预测方法

准确的秒级风速实时预测能够提高风电机组的运行状况和控制品质,为电网做出最优调度决策提供辅助信息.目前风速实时预测时间分辨率通常为分钟级,且在小数据集的情况下模型泛化能力弱.文章以时间分辨率为5s的风速序列为研究对象,提出了基于多任务学习的风速实时预测方法.该方法结合了变分模态分解方法和长短期记忆神经网络.首先,通过变分...     

Using temperature and state transitions to forecast wind speed

A major issue in forecasting wind speed is non‐linear variability. The probability distribution of wind speed series shows heavy tails, while there are frequent state transitions, in which wind speed changes by large magnitudes, over relatively short time periods. These so‐called large ramp events are one of the critical issues currently facing the wind energy community. Two forecasting algorithms are analyzed here. The first is a regression on lags, including temperature as a causal factor, with time‐varying parameters. The second augments the first using state transition terms. The main innovation in state transition models is that the cumulative density function from regressions on the states is used as a right‐hand side variable in the regressions for wind speed. These two methods are tested against a persistence forecast and several non‐linear models, using eight hourly wind speed series. On average, these two models produce the best results. The state transition model improves slightly over the regression. However, the improvement achieved by both models relative to the persistence forecast is fairly small. These results argue that there are limits to the accuracy that can be achieved in forecasting wind speed data. Copyright © 2007 John Wiley & Sons, Ltd.     

Wind farm power optimization including flow variability

A model-based optimisation approach is used to investigate the potential gain of wind-farm power with a cooperative control strategy between the wind turbines. Based on the Jensen wake model with the Katic wake superposition rule, the potential gain for the Nysted offshore wind farm is calculated to be 1.4–5.4% for standard choices 0.4 ≥ k ≥ 0.25 of the wake expansion parameter. Wake model fits based on short time intervals of length 15sec ≤ T ≤ 10 min within three months of data reveal a strong wake flow variability, resulting in rather broad distributions for the wake expansion parameter. When an optimized wind-farm control strategy, derived from a fixed wake parameter, is facing this flow variability, the potential gain reduces to 0.3–0.5%. An omnipotent control strategy, which has real-time knowledge of the actual wake flow, would be able to increase the gain in wind-farm power to 4.9%.     

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.     

Regime‐switching models and multiple causal factors in forecasting wind speed

This study evaluates two types of models for wind speed forecasting. The first is models with multiple causal factors, such as offsite readings of wind speed and meteorological variables. These can be estimated using either regressions or neural networks. The second is state transition and the closely related class of regime‐switching transition models. These are attractive in that they can be used to predict outlying fluctuations or large ramp events. The regime‐switching model uses a persistence forecast during periods of high wind speed, and regressions for low and intermediate speeds. These techniques are tested on three databases. Two main criteria are used to evaluate the outcomes, the number of high and low states than can be predicted correctly and the mean absolute percent error of the forecast. Neural nets are found to predict the state transitions somewhat better than logistic regressions, although the regressions do not do badly. Three methods all achieve about the same degree of forecast accuracy: multivariate regressions, state transition and regime‐switching models. If the states could be predicted perfectly, the regime‐switching model would improve forecast accuracy by an additional 2.5 to 3 percentage points. Analysis of the density functions of wind speed and the forecasting models finds that the regime‐switching method more closely approximates the distribution of the actual data. Copyright © 2009 John Wiley & Sons, Ltd.     

全球风电发展现状和展望

本文回顾了2011年全球风电整体发展形势,分析了不同国家和地区的风电市场,并对全球风电未来5年的发展趋势做了大致预测。     

Wind farm power prediction: a data‐mining approach

In this paper, models for short‐ and long‐term prediction of wind farm power are discussed. The models are built using weather forecasting data generated at different time scales and horizons. The maximum forecast length of the short‐term prediction model is 12 h, and the maximum forecast length of the long‐term prediction model is 84 h. The wind farm power prediction models are built with five different data mining algorithms. The accuracy of the generated models is analysed. The model generated by a neural network outperforms all other models for both short‐ and long‐term prediction. Two basic prediction methods are presented: the direct prediction model, whereby the power prediction is generated directly from the weather forecasting data, and the integrated prediction model, whereby the prediction of wind speed is generated with the weather data, and then the power is generated with the predicted wind speed. The direct prediction model offers better prediction performance than the integrated prediction model. The main source of the prediction error appears to be contributed by the weather forecasting data. Copyright © 2008 John Wiley & Sons, Ltd.     

Wind power forecast using neural networks: Tuning with optimization techniques and error analysis

The increased integration of wind power into the power system implies many challenges to the network operators, mainly due to the hard to predict and variability of wind power generation. Thus, an accurate wind power forecast is imperative for systems operators, aiming at an efficient and economical wind power operation and integration into the power system. This work addresses the issue of forecasting short‐term wind speed and wind power for 1 hour ahead, combining artificial neural networks (ANNs) with optimization techniques on real historical wind speed and wind power data. Levenberg‐Marquardt (LM) and particle swarm optimization (PSO) are used as training algorithms to update the weights and bias of the ANN applied to wind speed predictions. The forecasting performance produced by the proposed models are compared with each other, as well as with the benchmark persistence model. Test results show higher performance for ANN‐LM wind speed forecasting model, outperforming both ANN‐PSO and persistence. The application of ANN‐LM to wind power forecast revealed also a good performance, with an average improvement of 2.8% in relation to persistence. An innovative analysis of mean absolute percentage error (MAPE) behaviour in time and in typical days is finally offered in the paper.     

风电场风功率实时预测效果综合评价方法

超短期风电功率预测对含大规模风电的电力系统安全经济运行有着重要意义。但目前对预测结果的评价均停留在常规统计学指标上,缺乏合理的评价体系来评价某特定风电场所选取预测模型的优劣。简述了目前风电功率预测结果评价指标的不足,提出一种基于预测误差评价和预报考核等指标的风电场输出功率实时预测效果评估方法,为不同地区风电场根据其风电输出功率变化的特点,选择预测模型以及风电场输出功率预测效果的工程检验提供依据。最后,利用吉林省某风电场实测数据,采用该评估方法对不同预测模型的实时预测结果进行分析评价,实现了该风电场不同预测模型间的择优,验证了该评价方法的指导价值。     

Spatiotemporal variability of the wind power resource in Argentina and Uruguay

The spatiotemporal variability of the wind power resource over Argentina and Uruguay is assessed based on the Modern‐Era Retrospective Analysis for Research and Applications 2 (MERRA2) dataset. Hourly wind speeds were interpolated to 100‐m height, and then, wind power outputs were computed using power curves of three International Electrotechnical Commission (IEC) wind classes. The time series of wind power outputs were filtered using a fast Fourier transform (FFT) to separate regular (annual and daily) from irregular (interannual and synoptic scale) cycles. An empirical orthogonal function analysis was applied to the resulting datasets to obtain the main modes of variability. The results show that the combination of wind power outputs from southern and northern Patagonia broadly follows the average annual electric load. Patagonia exhibits the highest variability on the interannual, annual, and synoptic timescales. On the interannual and synoptic timescales, the variability modes are associated with known and distinct atmospheric circulation modes. The interannual modes of variability are associated with opposite surface level pressure (SLP) anomalies between middle and high latitudes.     

Short‐term forecasting of surface layer wind speed using a continuous random cascade model

This paper describes a statistical method for short‐term forecasting (1–12 h ahead) of surface layer wind speed using only recent observations, relying on the notion of continuous cascades. Inspired by recent empirical findings that suggest the existence of some cascading process in the mesoscale range, we consider that wind speed can be described by a seasonal component and a fluctuating part represented by a ‘multifractal noise’ associated with a random cascade. Performances of our model are tested on hourly wind speed series gathered at various locations in Corsica (France) and the Netherlands. The obtained results show that a better modeling of the noise term based on cascade process enhances the forecast; furthermore, there is a systematic improvement in the prediction as compared with reference models. Copyright © 2011 John Wiley & Sons, Ltd.     

Long‐term loads for a monopile‐supported offshore wind turbine

Accurate prediction of long‐term ‘characteristic’ loads associated with an ultimate limit state for design of a 5‐MW bottom‐supported offshore wind turbine is the focus of this study. Specifically, we focus on predicting the long‐term fore–aft tower bending moment at the mudline and the out‐of‐plane bending moment at the blade root of a monopile‐supported shallow‐water offshore wind turbine. We employ alternative probabilistic predictions of long‐term loads using inverse reliability procedures in establishing the characteristic loads for design. Because load variability depends on the environmental conditions (defining the wind speed and wave height), we show that long‐term predictions that explicitly account for such load variability are more accurate, especially for environmental states associated with above‐rated wind speeds and associated wave heights. Copyright © 2012 John Wiley & Sons, Ltd.     

Comparison of simulators for variable‐speed wind turbine transient analysis

This paper presents a comparison of three variable‐speed wind turbine simulators used for a 2 MW wind turbine short‐term transient behaviour study during a symmetrical network disturbance. The simulator with doubly fed induction generator (DFIG) analytical model, the simulator with a finite element method (FEM) DFIG model and the wind turbine simulator with an analytical model of DFIG are compared. The comparison of the simulation results shows the influence of the different modelling approaches on the short‐term transient simulation accuracy. Copyright © 2005 John Wiley & Sons, Ltd.     

Realistic simulations of the July 1, 2011 severe wind event over the Buffalo Ridge Wind Farm

Severe winds from thunderstorm outflows pose a challenge to wind turbine arrays. They can cause significant power ramps and disruption in energy production. They can also cause extreme structural damage to turbines as was seen in the severe storm event over the Buffalo Ridge Wind Farm on July 1, 2011. At this southwestern Minnesota site, blades from multiple turbines broke away and a tower buckled in the intense winds. In this study, we attempt to characterize meteorological conditions over the Buffalo Ridge Wind Farm area during this event. The observational network included NEXRAD radars, automated surface observation stations and a wind profiler. Storm reports from the Storm Prediction Center and damage surveys provided additional insight to the in situ measurements. Even with these datasets, assessing wind speeds around turbine rotors is difficult. Thus, Weather Research and Forecasting model simulations of the event are carried out that consider current and anticipated future operational model setups. This work addresses model spatial resolution versus parameterization complexity. Parameterizations of the planetary boundary layer and microphysics processes are evaluated based on their impact on storm dynamics and the low‐level wind field. Results are also compared with the Wind Integration National Dataset, which utilizes data assimilation and an extensive continental domain. Enhanced horizontal resolution with simplistic parameterization helps increase resolved wind speeds and ramp intensity. Enhanced sophistication of microphysics parameterizations also helps increase resolved wind speeds, improve storm timing and structure and resolve higher values of turbulent kinetic energy in the lowest 1 km of the atmosphere. Copyright © 2017 John Wiley & Sons, Ltd.     

Numerical error estimation of conventional anemometry mounted on offshore floating met‐masts

Offshore wind energy is moving towards a future where the main challenge is to cope with the increasing water depth needed to access more and better wind resources. One of the first steps to be undertaken is the development of floating structures to support either wind turbines or measurement devices for the proper characterization of wind energy resources. The use of floating devices for measuring wind speed involves a number of uncertainties not presented by seabed fixed systems. These sources of uncertainty or error are present both in instantaneous wind measurements and averaged (10 min or hourly) values because of (i) variability in the measurement height, (ii) the tilt of the anemometer and (iii) the relative velocity between the anemometer and the wind, among others. In this paper, a methodology for assessing the error in the wind measurement characterization because of the movement of a floating meteorological mast is presented. By the numerical simulation of a floating mast, the short‐ and long‐term error in the characterization of the wind at different heights has been evaluated. In general, the error because of the tilt can reach up to 80% of the total error; the error because of the variation of the vertical position of the anemometer reaches values of up to 15% in some cases; moreover, the error associated with the relative velocity between the anemometer and the wind, for averaged values, is significantly less. Finally, it can be concluded that the total error is lower than 0.5% for 10 min averaged wind speed of up to 24 m/s. Copyright © 2016 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.     

计及随机波动风速、风向的风电场建模方法研究

提出风电场建模方法建立风电场随机模型,对风电场运行特性进行仿真,为实现大规模风力发电的可预测、可控制目标服务。通过对风电场等值模型与详细模型的仿真比较,验证建模方法的合理性,并得出在研究风电场动态特性及其对电网影响时应考虑风速、风向的随机波动建立风电场模型。     

Modeling of the dynamics of wind to power conversion including high wind speed behavior

This paper proposes and validates an efficient, generic and computationally simple dynamic model for the conversion of the wind speed at hub height into the electrical power by a wind turbine. This proposed wind turbine model was developed as a first step to simulate wind power time series for power system studies. This paper focuses on describing and validating the single wind turbine model, and is therefore neither describing wind speed modeling nor aggregation of contributions from a whole wind farm or a power system area. The state‐of‐the‐art is to use static power curves for the purpose of power system studies, but the idea of the proposed wind turbine model is to include the main dynamic effects in order to have a better representation of the fluctuations in the output power and of the fast power ramping especially because of high wind speed shutdowns of the wind turbine. The high wind speed shutdowns and restarts are represented as on–off switching rules that govern the output of the wind turbine at extreme wind speed conditions. The model uses the concept of equivalent wind speed, estimated from the single point (hub height) wind speed using a second‐order dynamic filter that is derived from an admittance function. The equivalent wind speed is a representation of the averaging of the wind speeds over the wind turbine rotor plane and is used as input to the static power curve to get the output power. The proposed wind turbine model is validated for the whole operating range using measurements available from the DONG Energy offshore wind farm Horns Rev 2. Copyright © 2015 John Wiley & Sons, Ltd.