Simulating intra‐hourly wind power fluctuations on a power system level

In wind integration studies, sub‐hourly, load synchronous wind data are often preferable. These datasets can be generated by a hybrid approach, combining hourly measurements or output from meteorological models with a stochastic simulation of the high‐frequency fluctuations. This paper presents a method for simulating aggregated intra‐hourly wind power fluctuations for a power system, taking into account the time‐varying volatility seen in measurements. Some key elements in the modelling were transformations to stationarity, the use of frequency domain techniques including a search for appropriate phase angles and an adjustment of the resulting time series in order to get correct hourly means. Generation data from Denmark and Germany with 5 and 15 min temporal resolution were used for training models. It is shown that the distribution and non‐stationarity of simulated deviations from hourly means closely follow those of measurements. Power spectral densities and step change distributions agree well. Of particular importance is that the results are good also when the training and objective power systems are not the same. The computational cost is low in comparison with other approaches for generating high‐frequency data. Copyright © 2016 John Wiley & Sons, Ltd.     

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.     

Generating wind time series as a hybrid of measured and simulated data

Certain applications, such as analysing the effect of a wind farm on grid frequency regulation, require several years of wind power data measured at intervals of a few seconds. We have developed a method to generate days to years of non‐stationary wind speed time series sampled at high rates by combining measured and simulated data. Measured wind speed data, typically 10–15 min averages, capture the non‐stationary characteristics of wind speed variation: diurnal variations, the passing of weather fronts, and seasonal variations. Simulated wind speed data, generated from spectral models, add realistic turbulence between the empirical data. The wind speed time series generated with this method agree very well with measured time series, both qualitatively and quantitatively. The power output of a wind turbine simulated with wind data generated by this method demonstrates energy production, ramp rates and reserve requirements that closely match the power output of a turbine simulated turbine with measured wind data. Copyright © 2011 John Wiley & Sons, Ltd.     

Comparison of potential power production at on‐ and offshore sites

This article presents analyses of the potential power production from turbines located in the near‐shore and offshore environment relative to an onshore location, using half‐hourly average wind speed data from four sites in the Danish wind monitoring network. These measurement sites are located in a relatively high wind speed environment, and data from these sites indicate a high degree of spatial coherence. For these sites and representative turbine specifications (rated power 1·3–2 W) the fraction of time with power output in excess of 500 kW is twice as high for the offshore location as for the land site. Also, the fraction of time with negligible power production (defined as <100 kW output from the turbines described herein) is less than 20% for the offshore site and twice as high at the land‐based location. Capacity factors are higher for coastal sites than for the land site, and the annual capacity factor for the offshore location is twice that of the land site. Potential power output at the offshore site exhibits approximately the same seasonal variation as at the land site but little diurnal variation. Copyright © 2001 John Wiley & Sons, Ltd.     

Geographic aggregation of wind power—an optimization methodology for avoiding low outputs

This work investigates macro‐geographic allocation as a means to improve the performance of aggregated wind power output. The focus is on the spatial smoothing effect so as to avoid periods of low output. The work applies multi‐objective optimization, in which two measures of aggregated wind power output variation are minimized, whereas the average output is maximized. The results show that it is possible to allocate wind power so that the frequency of low outputs is substantially reduced, while maintaining the average output at around 30% of nameplate capacity, as compared with the corresponding output of 20% for the present allocation system. We conclude that in a future, fully electrically integrated Europe, geographic allocation can substantially reduce instances of low aggregate output, while impairing little on capacity factor and at the same time providing reduction in of short‐term jumps in output. Copyright © 2016 John Wiley & Sons, Ltd.     

LiDAR‐based characterization of mid‐altitude wind conditions for airborne wind energy systems

Engineers and researchers working on the development of airborne wind energy systems (AWES) still rely on oversimplified wind speed approximations and coarsely sampled reanalysis data because of a lack of high‐resolution wind data at altitudes above 200 m. Ten‐minute average wind speed LiDAR measurements up to an altitude of 1100 m and data from nearby weather stations were investigated with regard to wind energy generation and impact on LiDAR measurements. Data were gathered by a long‐range pulsed Doppler LiDAR device installed on flat terrain. Because of the low overall carrier‐to‐noise ratio, a custom‐filtering technique was applied. Our analyses show that diurnal variation and atmospheric stability significantly affect wind conditions aloft which cause a wide range of wind speeds and a multimodal probability distribution that cannot be represented by a simple Weibull distribution fit. A better representation of the actual wind conditions can be achieved by fitting Weibull distributions separately to stable and unstable conditions. Splitting and clustering the data by simulated surface heat flux reveals substate stratification responsible for the multimodality. We classify different wind conditions based on these substates, which result in different wind energy potential. We assess optimal traction power and optimal operating altitudes statistically as well as for specific days based on a simplified AWES model. Using measured wind speed standard deviation, we estimate average turbulence intensity and show its variation with altitude and time. Selected short‐term data sets illustrate temporal changes in wind conditions and atmospheric stratification with a high temporal and vertical resolution.     

Space and time: Wind in an investment planning model

Investment planning models inform investment decisions and government policies. Current models do not capture the intermittent nature of renewable energy sources, restricting the applicability of the models for high penetrations of renewables. We provide a methodology to capture spatial variation in wind output in combination with transmission constraints. The representation of wind distributions using stochastic approaches or using extensive historic data sets exceeds computational constraints for real world application. Hence we restrict the amount of input data, and use bootstrapping to illustrate the robustness of the results. For the UK power system we model wind deployment and the value of transmission capacity.     

A statistical analysis of wind energy potential at the eastern region of Saudi Arabia

The energy potential of wind for the eastern region of Saudi Arabia is investigated based on measurements of a complete year data at a coastal location in eastern Saudi Arabia. A suitable Weibull distribution is generated and a comparison of this model is made with the Rayleigh distribution of wind power densities. Two horizontal‐axis type of wind energy conversion systems which operate at fixed rpm are considered for the determination of the extractable wind power, and a model of quadratic power output function is used between the cut‐in speed and rated speed. It is shown that small‐scale wind energy systems are suitable in the eastern part of Saudi Arabia for power generation and irrigation purposes. Copyright © 1999 John Wiley & Sons, Ltd.     

Short‐term variations in wind power: Some quantile‐type models for probabilistic forecasting

We discuss some ways of formulating quantile‐type models for forecasting variations in wind power in the short term (within a few hours). Such models predict quantiles of the conditional distribution of the wind power available at some future time using information presently available. A natural reference for models of this kind is a ‘probabilistic‐persistence’ quantile forecast whose only input is the present wind power. Using data from some New Zealand wind farms, we find that more complex quantile models can readily improve on probabilistic persistence in resolution but not in sharpness. The most valuable model inputs, apart from the present power, are found to be real‐time air pressure measurements and a power total‐variation indicator. Copyright © 2010 John Wiley & Sons, Ltd.     

风力发电系统输出功率随机波动的仿真分析

针对当前风力发电系统输出功率随机波动的问题,以永磁同步风力发电机(PMSG)与直流侧储能系统(钒氧化还原电池)整合的风力发电系统为基础,进行数字仿真建模,采用MATLAB/Simulink软件对固定负载,变化风速工况;固定风速,负荷瞬变工况;风速和负荷同时变化工况;进行了仿真试验和分析。结果表明,对于采用储能技术的风电场并网功率随机波动的平抑控制,可以利用蓄电池的充放电特性,在风速变化以及负荷瞬变时进行功率平衡的调节。     

Possible power of down‐regulated offshore wind power plants: The PossPOW algorithm

This paper proposes a method for real‐time estimation of the possible power of an offshore wind power plant when it is down‐regulated. The main purpose of the method is to provide an industrially applicable estimate of the possible (or reserve) power. The method also yields a real‐time power curve, which can be used for operation monitoring and wind farm control. Currently, there is no verified approach regarding estimation of possible power at wind farm scale. The key challenge in possible power estimation at wind farm level is to correct the reduction in wake losses, which occurs due to the down‐regulation. Therefore, firstly, the 1‐second wind speeds at the upstream turbines are estimated, since they are not affected by the reduced wake. Then they are introduced into the wake model, adjusted for the same time resolution, to correct the wake losses. To mitigate the uncertainties due to dynamic changes within the large offshore wind farms, the algorithm is updated at every turbine downstream, considering the local axial and lateral turbulence effects. The PossPOW algorithm uses only 1‐Hz turbine data as inputs and provides possible power output. The algorithm is trained and validated in Thanet and Horns Rev‐I offshore wind farms under nominal operation, where the turbines are following the optimum power curve. The results indicate that the PossPOW algorithm performs well; in the Horns Rev‐I wind farm, the strict power system requirements are met more than 70% of the time over the 24‐hour data set on which the algorithm was evaluated.     

Implications of seasonal and diurnal variations of wind velocity for power output estimation of a turbine: a case study of Grenada

This case study highlights the importance of taking into consideration diurnal variations of wind velocity for wind energy resources assessment. Previous studies of wind energy distribution that are based on the two-parameter Weibull density function have so far neglected to consider time of day fluctuations in wind speed, instead concentrating primarily on seasonal deviations. However, this has serious implications where such a wind energy model is the underpinning of calculations for the potential power production from a wind turbine and in particular where the timing of the energy output is essential to meet electricity loads. In the case of Grenada the energy output from a wind turbine during the day is approximately two times the output at night thereby fluctuating enormously around the seasonal mean distribution. When this is not taken into account the economic and technological viability of a wind turbine project may be overestimated or not even be identified. This work shows how a wind energy resources assessment based on the Weibull distribution model can be done and how the power output of a horizontal axis turbine is calculated. An analysis of the recorded wind data confirms the application of the Weibull density function as a suitable tool for modelling wind regimes. Copyright © 2003 John Wiley & Sons, Ltd.     

Investigation on the integral output power model of a large-scale wind farm

The integral output power model of a large-scale wind farm is needed when estimating the wind farm’s output over a period of time in the future. The actual wind speed power model and calculation method of a wind farm made up of many wind turbine units are discussed. After analyzing the incoming wind flow characteristics and their energy distributions, and after considering the multi-effects among the wind turbine units and certain assumptions, the incoming wind flow model of multi-units is built. The calculation algorithms and steps of the integral output power model of a large-scale wind farm are provided. Finally, an actual power output of the wind farm is calculated and analyzed by using the practical measurement wind speed data. The characteristics of a large-scale wind farm are also discussed.     

Model‐based receding horizon control of wind farms for secondary frequency regulation

In this study, we propose the use of model‐based receding horizon control to enable a wind farm to provide secondary frequency regulation for a power grid. The controller is built by first proposing a time‐varying one‐dimensional wake model, which is validated against large eddy simulations of a wind farm at startup. This wake model is then used as a plant model for a closed‐loop receding horizon controller that uses wind speed measurements at each turbine as feedback. The control method is tested in large eddy simulations with actuator disk wind turbine models representing an 84‐turbine wind farm that aims to track sample frequency regulation reference signals spanning 40 min time intervals. This type of control generally requires wind turbines to reduce their power set points or curtail wind power output (derate the power output) by the same amount as the maximum upward variation in power level required by the reference signal. However, our control approach provides good tracking performance in the test system considered with only a 4% derate for a regulation signal with an 8% maximum upward variation. This performance improvement has the potential to reduce the opportunity cost associated with lost revenue in the bulk power market that is typically associated with providing frequency regulation services. Copyright © 2017 John Wiley & Sons, Ltd.     

Detecting,categorizing and forecasting large ramps in wind farm power output using meteorological observations and WPPT

The Wind Power Prediction Tool (WPPT) has been installed in Australia for the first time, to forecast the power output from the 65MW Roaring 40s Renewable Energy P/L Woolnorth Bluff Point wind farm. This article analyses the general performance of WPPT as well as its performance during large ramps (swings) in power output. In addition to this, detected large ramps are studied in detail and categorized. WPPT combines wind speed and direction forecasts from the Australian Bureau of Meteorology regional numerical weather prediction model, MesoLAPS, with real‐time wind power observations to make hourly forecasts of the wind farm power output. The general performances of MesoLAPS and WPPT are evaluated over 1 year using the root mean square error (RMSE). The errors are significantly lower than for basic benchmark forecasts but higher than for many other WPPT installations, where the site conditions are not as complicated as Woolnorth Bluff Point. Large ramps are considered critical events for a wind power forecast for energy trading as well as managing power system security. A methodology is developed to detect large ramp events in the wind farm power data. Forty‐one large ramp events are detected over 1 year and these are categorized according to their predictability by MesoLAPS, the mechanical behaviour of the wind turbine, the power change observed on the grid and the source weather event. During these events, MesoLAPS and WPPT are found to give an RMSE only roughly equivalent to just predicting the mean (climatology forecast). Copyright © 2007 John Wiley & Sons, Ltd.     

Simulating subhourly variability of wind power output

As installed wind power capacity grows, subhourly variability in wind power output becomes increasingly important for determining the system flexibility needs, operating reserve requirements, and cost associated with wind integration. This paper presents a new methodology for simulating subhourly wind power output based on hourly average time series, which are often produced for system planning analyses, for both existing wind plants and expanded, hypothetical portfolios of wind plants. The subhourly model has an AR(p)‐ARCH(q) structure with exogenous input in the heteroskedasticity term. Model coefficients may be fit directly to high‐pass filtered historical data if it exists; for sets of wind plants containing hypothetical plants for which there are no historical data, this paper presents a method to determine model coefficients based on wind plant capacities, capacity factors, and pairwise distances. Unlike predecessors, the model presented in this paper is independent of wind speed data, captures explicitly the high variability associated with intermediate levels of power output, and captures distance‐dependent correlation between the power output of wind plants across subhourly frequencies. The model is parameterized with 1‐minute 2014 plant‐level wind power data from Electric Reliability Council of Texas (ERCOT) and validated out‐of‐sample against analogous 2015 data. The expanded‐capacity model, fit to 2014 data, produces accurate subhourly time series for the 2015 wind fleet (a 49% capacity expansion) based only on the 2015 system's wind plant capacities, capacity factors, and pairwise distances. This supports its use in simulating subhourly fleet aggregate wind power variability for future high‐wind scenarios.     

Dynamic wind turbine output power reduction under varying wind speed conditions due to inertia

The inertia of wind turbines causes a reduction in their output power due to their inability to operate at the turbine maximum co‐efficient of performance point under dynamic wind conditions. In this paper, this dynamic power reduction is studied analytically and using simulations, assuming that a steady‐state optimal torque control strategy is used. The concepts of the natural and actual turbine time‐constant are introduced, and typical values for these parameters are examined. It is shown that for the typical turbine co‐efficient of performance curve used, the average turbine speed can be assumed to be determined by the average wind speed. With this assumption, analytical expressions for the power reduction with infinite and then finite turbine inertia are determined for sine‐wave wind speed variations. The results are then generalized for arbitrary wind speed profiles. A numerical wind turbine system simulation model is used to validate the analytical results for step and sine‐wave wind speed variations. Finally, it is used with real wind speed data to compare with the analytical predictions. Copyright © 2012 John Wiley & Sons, Ltd.     

Statistical wind direction modeling for the analysis of large scale wind power generation

Understanding the effects of large‐scale wind power generation on the electric power system is growing in importance as the amount of installed generation increases. In addition to wind speed, the direction of the wind is important when considering wind farms, as the aggregate generation of the farm depends on the direction of the wind. This paper introduces the wrapped Gaussian vector autoregressive process for the statistical modeling of wind directions in multiple locations. The model is estimated using measured wind direction data from Finland. The presented methodology can be used to model new locations without wind direction measurements. This capability is tested with two locations that were left out of the estimation procedure. Through long‐term Monte Carlo simulations, the methodology is used to analyze two large‐scale wind power scenarios with different geographical distributions of installed generation. Wind generation data are simulated for each wind farm using wind direction and wind speed simulations and technical wind farm information. It is shown that, compared with only using wind speed data in simulations, the inclusion of simulated wind directions enables a more detailed analysis of the aggregate wind generation probability distribution. Copyright © 2016 John Wiley & Sons, Ltd.     

基于电池储能系统和双重扩展卡尔曼滤波的风能发电智能调度技术研究

风能等新能源发电系统在供电体系中的占比越来越大,但其随机性和波动性问题,将风力发电厂输出的电力直接向电网调度会造成安全隐患。为了解决这一问题,基于电池储能系统提出了一种风能发电智能调度技术,该技术以风力发电动力学模型和电池储能系统状态模型为基础,利用双重扩展卡尔曼滤波算法实现了风能发电系统的稳定输出。以某地风速实测数据和电网需求功率为参考,对不同算法的输出功率预测值进行了仿真分析和实验对比。结果表明：提出的改进算法预测的风速值误差相比于传感器观测值平均误差降低了28%以上,可以更准确地提供发电系统输出功率;提出的智能调度技术可以使电压波动幅度降低60%以上,系统整体输出功率稳定在参考功率附近,误差不超过2%,有一定的实用意义。     

Wind turbine generator trends for site‐specific tailoring

Turbine optimization for specific wind regimes and climate conditions is becoming more common as the market expands into new territories (offshore, low‐wind regimes) and as technology matures. Tailoring turbines for specific sites by varying rotor diameter, tower height and power electronics may be a viable technique to make wind energy more economic and less intermittent. By better understanding the wind resource trends and evaluating important wind turbine performance parameters such as specific power (ratio of rated power and rotor swept area), developers and operators can optimize plant output and better anticipate operational impacts. This article presents a methodology to evaluate site‐specific wind data for turbine tailoring. Wind characteristics for the Tehachapi wind resource area in California were utilized for this study. These data were used to evaluate the performance of a range of wind turbine configurations. The goal was to analyse the variations in wind power output for the area, assess the changes in these levels with the time of day and season and determine how turbine configuration affects the output. Wind turbine output was compared with California statewide system electrical demand to evaluate the correlation of the wind resource site with local peak demand loads. A comparison of the commercial value of electricity and corresponding wind generation is also presented using a time‐dependent valuation methodology. Copyright © 2005 John Wiley & Sons, Ltd.