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.     

The impact of the North Atlantic Oscillation on electricity markets: A case study on Ireland

The North Atlantic Oscillation (NAO) is a large-scale atmospheric circulation pattern driving climate variability in north-western Europe. As the deployment of wind-powered generation expands on electricity networks across Europe, the impacts of the NAO on the electricity system will be amplified. This study assesses the impact of the NAO, via wind-power generation, on the electricity market considering thermal generation costs, wholesale electricity prices and wind generation subsidies. A Monte Carlo approach is used to model NAO phases and generate hourly wind speed time-series data, electricity demand and fuel input data. A least-cost unit commitment and economic dispatch model is used to simulate an island electricity system, modelled on the all-island Irish electricity system. The impact of the NAO obviously depends on the level of wind capacity within an electricity system. Our results indicate that on average a switch from negative to positive NAO phase can reduce thermal generation costs by up to 8%, reduce wholesale electricity prices by as much as €1.5/MWh, and increase wind power generators' revenue by 12%.     

Framework design of a hybrid energy system by combining wind farm with small gas turbine power plants

Owing to the stochastic characteristic of natural wind speed, the output fluctuation of wind farm has a negative impact on power grid when a large-scale wind farm is connected to a power grid. It is very difficult to overcome this impact only by wind farm itself. A novel power system called wind-gas turbine hybrid energy system was discussed, and the framework design of this hybrid energy system was presented in detail in this paper. The hybrid energy system combines wind farm with several small gas turbine power plants to form an integrated power station to provide a relatively firm output power. The small gas turbine power plant has such special advantages as fast start-up, shutdown, and quick load regulation to fit the requirement of the hybrid energy system. Therefore, the hybrid energy system uses the output from the small gas turbine power plants to compensate for the output fluctuation from the wind farm for the firm output from the whole power system. To put this hybrid energy system into practice, the framework must be designed first. The capacity of the wind farm is chosen according to the capacity and units of small gas turbine power plants, load requirement from power grid, and local wind energy resource distribution. Finally, a framework design case of hybrid energy system was suggested according to typical wind energy resource in Xinjiang Autonomous Region in China.     

Framework design of a hybrid energy system by combining wind farm with small gas turbine power plants

Owing to the stochastic characteristic of natural wind speed, the output fluctuation of wind farm has a negative impact on power grid when a large-scale wind farm is connected to a power grid. It is very difficult to overcome this impact only by wind farm itself. A novel power system called wind-gas turbine hybrid energy system was discussed, and the framework design of this hybrid energy system was presented in detail in this paper. The hybrid energy system combines wind farm with several small gas turbine power plants to form an integrated power station to provide a relatively firm output power. The small gas turbine power plant has such special advantages as fast start-up, shutdown, and quick load regulation to fit the requirement of the hybrid energy system. Therefore, the hybrid energy system uses the output from the small gas turbine power plants to compensate for the output fluctuation from the wind farm for the firm output from the whole power system. To put this hybrid energy system into practice, the framework must be designed first. The capacity of the wind farm is chosen according to the capacity and units of small gas turbine power plants, load requirement from power grid, and local wind energy resource distribution. Finally, a framework design case of hybrid energy system was suggested according to typical wind energy resource in Xinjiang Autonomous Region in China.     

Sensitivity of southern California wind energy to turbine characteristics

Using output from a high‐resolution meteorological simulation, we evaluate the sensitivity of southern California wind energy generation to variations in key characteristics of current wind turbines. These characteristics include hub height, rotor diameter and rated power, and depend on turbine make and model. They shape the turbine's power curve and thus have large implications for the energy generation capacity of wind farms. For each characteristic, we find complex and substantial geographical variations in the sensitivity of energy generation. However, the sensitivity associated with each characteristic can be predicted by a single corresponding climate statistic, greatly simplifying understanding of the relationship between climate and turbine optimization for energy production. In the case of the sensitivity to rotor diameter, the change in energy output per unit change in rotor diameter at any location is directly proportional to the weighted average wind speed between the cut‐in speed and the rated speed. The sensitivity to rated power variations is likewise captured by the percent of the wind speed distribution between the turbines rated and cut‐out speeds. Finally, the sensitivity to hub height is proportional to lower atmospheric wind shear. Using a wind turbine component cost model, we also evaluate energy output increase per dollar investment in each turbine characteristic. We find that rotor diameter increases typically provide a much larger wind energy boost per dollar invested, although there are some zones where investment in the other two characteristics is competitive. Our study underscores the need for joint analysis of regional climate, turbine engineering and economic modeling to optimize wind energy production. Copyright © 2012 John Wiley & Sons, Ltd.     

A study into the accuracy of using meteorological wind data to estimate turbine generation output

Meteorological (met) station data is used as the basis for a number of influential studies into the impacts of the variability of renewable resources. Real turbine output data is not often easy to acquire, whereas meteorological wind data, supplied at a standardised height of 10 m, is widely available. This data can be extrapolated to a standard turbine height using the wind profile power law and used to simulate the hypothetical power output of a turbine. Utilising a number of met sites in such a manner can develop a model of future wind generation output. However, the accuracy of this extrapolation is strongly dependent on the choice of the wind shear exponent α. This paper investigates the accuracy of the simulated generation output compared to reality using a wind farm in North Rhins, Scotland and a nearby met station in West Freugh. The results show that while a single annual average value for α may be selected to accurately represent the long term energy generation from a simulated wind farm, there are significant differences between simulation and reality on an hourly power generation basis, with implications for understanding the impact of variability of renewables on short timescales, particularly system balancing and the way that conventional generation may be asked to respond to a high level of variable renewable generation on the grid in the future.     

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.     

Climate change implications for wind power resources in the Northwest United States

Using statistically downscaled output from four general circulation models (GCMs), we have investigated scenarios of climate change impacts on wind power generation potential in a five-state region within the Northwest United States (Idaho, Montana, Oregon, Washington, and Wyoming). All GCM simulations were extracted from the standardized set of runs created for the Intergovernmental Panel on Climate Change (IPCC). Analysis of model runs for the 20th century (20c3m) simulations revealed that the direct output of wind statistics from these models is of relatively poor quality compared with observations at airport weather stations within each state. When the GCM output was statistically downscaled, the resulting estimates of current climate wind statistics are substantially better. Furthermore, in looking at the GCM wind statistics for two IPCC future climate scenarios from the Special Report on Emissions Scenarios (SRES A1B and A2), there was significant disagreement in the direct model output from the four GCMs. When statistical downscaling was applied to the future climate simulations, a more coherent story unfolded related to the likely impact of climate change on the region's wind power resource. Specifically, the results suggest that summertime wind speeds in the Northwest may decrease by 5–10%, while wintertime wind speeds may decrease by relatively little, or possibly increase slightly. When these wind statistics are projected to typical turbine hub heights and nominal wind turbine power curves are applied, the impact of the climate change scenarios on wind power may be as high as a 40% reduction in summertime generation potential.     

解决新疆风能资源大规模开发瓶颈的探讨

本文对如何解决新疆风能资源进行大规模开发时所遇到的瓶颈进行了探讨,提出了一种解决问题的新思路。用详细的数据介绍了新疆丰富的风能资源分布,讨论了在大规模开发时遇到的电网稳定性、有效负荷需求不足以及没有联网电网的支持等问题,最后,根据新疆拥有丰富的天然气资源,提出了采用一种新型的风电-燃气轮机互补发电系统来彻底解决风电场负荷的波动对电网稳定性不利的影响,为新疆的风能大规模开发提供了一种新的可能。     

风剪切下风力机组俯仰控制策略研究

为增加风电场总输出功率,采用大涡模拟(LES)方法,利用致动线法(ALM),基于开源CFD软件OpenFOAM对风剪切下的风力机组4种风轮俯仰工况进行数值模拟,对比每种工况下的风电场总输出功率,并结合流场参数分析输出功率存在差别的内在原因。结果表明:风电场上游风力机尾迹可对下游风力机性能产生严重影响;风轮俯仰角增加时,风电场上游风力机输出功率先增后减,下游风力机输出功率持续增加;仰角为9°时风电场总输出功率比无俯仰控制策略时提升393982.25 W。     

Comparison and testing of power reserve control strategies for grid‐connected wind turbines

The stability of the electrical grid depends on enough generators being able to provide appropriate responses to sudden losses in generation capacity, increases in power demand or similar events. Within the United States, wind turbines largely do not provide such generation support, which has been acceptable because the penetration of wind energy into the grid has been relatively low. However, frequency support capabilities may need to be built into future generations of wind turbines to enable high penetration levels over approximately 20%. In this paper, we describe control strategies that can enable power reserve by leaving some wind energy uncaptured. Our focus is on the control strategies used by an operating turbine, where the turbine is asked to track a power reference signal supplied by the wind farm operator. We compare the strategies in terms of their control performance as well as their effects on the turbine itself, such as the possibility for increased loads on turbine components. It is assumed that the wind farm operator has access to the necessary grid information to generate the power reference provided to the turbine, and we do not simulate the electrical interaction between the turbine and the utility grid. Copyright © 2013 John Wiley & Sons, Ltd.     

Large eddy simulations of the effect of vertical staggering in large wind farms

In order to study the effect of vertical staggering in large wind farms, large eddy simulations (LES) of large wind farms with a regular turbine layout aligned with the given wind direction were conducted. In the simulations, we varied the hub heights of consecutive downstream rows to create vertically staggered wind farms. We analysed the effect of streamwise and spanwise turbine spacing, the wind farm layout, the turbine rotor diameter, and hub height difference between consecutive downstream turbine rows on the average power output. We find that vertical staggering significantly increases the power production in the entrance region of large wind farms and is more effective when the streamwise turbine spacing and turbine diameter are smaller. Surprisingly, vertical staggering does not significantly improve the power production in the fully developed regime of the wind farm. The reason is that the downward vertical kinetic energy flux, which brings high velocity fluid from above the wind farm towards the hub height plane, does not increase due to vertical staggering. Thus, the shorter wind turbines are effectively sheltered from the atmospheric flow above the wind farm that supplies the energy, which limits the benefit of vertical staggering. In some cases, a vertically staggered wind farm even produced less power than the corresponding non vertically staggered reference wind farm. In such cases, the production of shorter turbines is significantly negatively impacted while the production of the taller turbine is only increased marginally.     

大型风电机组功率曲线的分析与修正

讨论了风电机组不同情况下的功率曲线定义,分析了功率曲线绘制过程中的风速处理方法,可以适用于绘制风力发电机组静、动态功率曲线;讨论了影响机组功率曲线的各种因素,并给出了影响因子,使得根据功率曲线进行风场发电量的计算可以取得更可靠的结果。     

The energy storage system output control strategy to improve theshort term windpower forecastingaccuracy rate

目前对于储能系统应用于平抑新能源发电的波动性、移峰填谷等场景的控制策略已有文献研究,但对于风功率预测准确率影响风电场效益的机制下储能系统应用的可行性尚未见研究。本文提出了一种以减小风电场短期功率预测偏差为目标的储能系统出力控制策略,控制策略以风电场实时出力数据（秒级）为数据源,采用线性外推加以移动平均优化的方法预测下一时刻风电场出力,通过比较风电场短期功率预测值与实时预测值,计算储能系统期望出力,并根据储能系统不同SOC区间内的出力能力进行约束,输出储能系统出力指令,最后进行了仿真验证。结果表明,本文提出的储能系统出力控制策略,能够使风电场通过配置储能系统,减少短期功率预测准确度考核,对风电场的精益化运行具有指导意义。     

Uncertainty quantification of infinite aligned wind farm performance using non‐intrusive polynomial chaos and a distributed roughness model

Uncertainty of wind farm parameters can have a significant effect on wind farm power output. Knowledge of the uncertainty‐produced stochastic distribution of the entire wind farm power output and the corresponding uncertainty propagation mechanisms is very important for evaluating the uncertainty effects on the wind farm performance during wind farm planning stage and providing insights on improving the performance of the existing wind farms. In this work, the propagation of uncertainties from surface roughness and induction factor in infinite aligned wind farms modeled by a modified distributed roughness model is investigated using non‐intrusive polynomial chaos. Stochastic analysis of surface roughness indicates that 30% uncertainty can propagate such that there is up a 8% uncertainty in the power output of the wind farm by affecting the uncertainty in the position of the individual wind turbines in the vertical boundary layer profile and uncertainty in vertical momentum fluxes which replenish energy in the wake in large wind farms. Induction factor uncertainty of the wind turbines can also have a significant effect on power output. Not only does its uncertainty substantially affect the vertical boundary layer profile, but the uncertainty in turbine wake growth which affects how neighboring turbine wakes interact. We found that optimal power output in terms of reduction of uncertainty closely correlates with the Betz limit and is dependent on the mean induction factor. Copyright © 2016 John Wiley & Sons, Ltd.     

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.     

Optimal selection of time windows for preventive maintenance of offshore wind farms subject to wake losses

The maintenance of wind farms is one of the major factors affecting their profitability. During preventive maintenance, the shutdown of wind turbines causes downtime energy losses. The selection of when and which turbines to maintain can significantly impact the overall downtime energy loss. This paper leverages a wind farm power generation model to calculate downtime energy losses during preventive maintenance for an offshore wind farm. Wake effects are considered to accurately evaluate power output under specific wind conditions. In addition to wind speed and direction, the influence of wake effects is an important factor in selecting time windows for maintenance. To minimize the overall downtime energy loss of an offshore wind farm caused by preventive maintenance, a mixed-integer nonlinear optimization problem is formulated and solved by the genetic algorithm, which can select the optimal maintenance time windows of each turbine. Weather conditions are imposed as constraints to ensure the safety of maintenance personnel and transportation. Using the climatic data of Cape Cod, Massachusetts, the schedule of preventive maintenance is optimized for a simulated utility-scale offshore wind farm. The optimized schedule not only reduces the annual downtime energy loss by selecting the maintenance dates when wind speed is low but also decreases the overall influence of wake effects within the farm. The portion of downtime energy loss reduced due to consideration of wake effects each year is up to approximately 0.2% of the annual wind farm energy generation across the case studies—with other stated opportunities for further profitability improvements.     

The spectrum of power from wind turbines

The power spectral density of the output of wind turbines provides information on the character of fluctuations in turbine output. Here both 1-second and 1-hour samples are used to estimate the power spectrum of several wind farms. The measured output power is found to follow a Kolmogorov spectrum over more than four orders of magnitude, from 30 s to 2.6 days. This result is in sharp contrast to the only previous study covering long time periods, published 50 years ago. The spectrum defines the character of fill-in power that must be provided to compensate for wind's fluctuations when wind is deployed at large scale. Installing enough linear ramp rate generation (such as a gas generator) to fill in fast fluctuations with amplitudes of 1% of the maximum fluctuation would oversize the fill-in generation capacity by a factor of two for slower fluctuations, greatly increasing capital costs. A wind system that incorporates batteries, fuel cells, supercapacitors, or other fast-ramp-rate energy storage systems would match fluctuations much better, and can provide an economic route for deployment of energy storage systems when renewable portfolio standards require large amounts of intermittent renewable generating sources.     

Characterizing future large,rapid changes in aggregated wind power using Numerical Weather Prediction spatial fields

A critical limiting factor to the successful deployment of a large proportion of wind power in power systems is its predictability. Power system operators play a vital role in maintaining system security, and this task is greatly aided by useful characterizations of future system operations. A wind farm power forecast generally relies on the forecast output from a Numerical Weather Prediction (NWP) model, typically at a single grid point in the model to represent the wind farm's physical location. A key limitation of this approach is the spatial misplacement of weather features often found in NWP forecasts. This paper presents a methodology to display wind forecast information from multiple grid points at hub height around the wind farm location. If the raw forecast wind speeds at hub height at multiple grid points were to be displayed directly, they would be misleading as the NWP outputs take account of the estimated local surface roughness and terrain at each grid point. Hence, the methodology includes a transformation of the wind speed at each grid point to an equivalent value that represents the surface roughness and terrain at the chosen single grid point for the wind farm site. The chosen‐grid‐point‐equivalent wind speeds for the wind farm can then be transformed to available wind farm power. The result is a visually‐based decision support tool which can help the forecast user to assess the possibilities of large, rapid changes in available wind power from wind farms. A number of methods for displaying the field for multiple wind farms are discussed. The chosen‐grid‐point‐equivalent transformation also has other potential applications in wind power forecasting such as assessing deterministic forecast uncertainty and improving downscaling results. Copyright © 2008 John Wiley & Sons, Ltd.