Dampening variations in wind power generation—the effect of optimizing geographic location of generating sites

This paper presents a method to dampen the variations in the output of aggregated wind power through geographic allocation of wind power generation sites. The method, which is based on the sequential optimization of site localization, is applied to the Nordic countries and Germany, using meteorologic wind speed data as the input. The results show that the variability in aggregated wind power output mitigates by applying sequential optimization. For the data used in this work, the coefficient of variation (standard deviation/mean) was 0.54 for the optimized aggregation of sites, as compared with 0.91 for the present day installation. An optimal allocation of wind power generation site reduces the need for dispatch and other measures to deal with the intermittent nature of wind power. Copyright © 2013 John Wiley & Sons, Ltd.     

Cost‐optimized allocation of wind power investments: a Nordic–German perspective

Using a linear cost minimization model with a 1 h time resolution, we investigated the influence of geographic allocation of wind power on large‐scale wind power investments, taking into account wind conditions, distance to load, and the nature of the power system in place (i.e. power generation and transmission capacities). We employed a hypothetical case in which a 20% wind power share of total electricity demand is applied to the Nordic–German power system. Free, i.e. geographically unrestricted, allocation of new wind power capacity is compared with a case in which national planning frameworks impose national limitations on wind power penetration levels. Given the cost assumptions made in the present work, the prospect of increasing the wind power capacity factor from 20 to 30% could motivate investments in transmission capacity from northern Scandinavia to continental Europe. The results obtained using the model show that the distribution of wind farms between regions with favorable wind conditions is dependent upon two factors: (i) the extent to which existing lines can be used to transmit the electricity that results from the new wind power and (ii) the correlation for wind power generation between the exporting region and the wind power generation already in place. In addition, the results indicate that there is little difference, i.e. just over 1%, in total yearly cost between the free allocation of new wind power and an allocation that complies with national planning frameworks. However, on a national level, there are significant differences with respect to investments in transmission and wind power capacities and the replacement of conventional power generation. Copyright © 2012 John Wiley & Sons, Ltd.     

Characteristics of day‐ahead wind power forecast errors in Nordic countries and benefits of aggregation

The growing proportion of wind power in the Nordic power system increases day‐ahead forecasting errors, which have a link to the rising need for balancing power. However, having a large interconnected synchronous power system has its benefits, because it enables to aggregate imbalances from large geographical areas. In this paper, day‐ahead forecast errors from four Nordic countries and the impacts of wind power plant dispersion on forecast errors in areas of different sizes are studied. The forecast accuracy in different regions depends on the amount of the total wind power capacity in the region, how dispersed the capacity is and the forecast model applied. Further, there is a saturation effect involved, after which the reduction in the relative forecast error is not very large anymore. The correlations of day‐ahead forecast errors between areas decline rapidly when the distance increases. All error statistics show a strong decreasing trend up to the area sizes of 50,000 km². The average mean absolute error (MAE) in different regions is 5.7% of installed capacity. However, MAE of a smaller area can be over 8% of the capacity, but when all the Nordic regions are aggregated together, the capacity‐normalized MAE decreases to 2.5%. The average of the largest errors for different regions is 39.8% and when looking at the largest forecast errors for smaller areas, the largest errors can exceed 80% of the installed capacity, whereas at the Nordic level, the maximum forecast error is only 13.5% of the installed capacity. Copyright © 2016 John Wiley & Sons, Ltd.     

Impact of hourly wind power variations on the system operation in the Nordic countries

The variations of wind power production will increase the flexibility needed in the system when significant amounts of load are covered by wind power. When studying the incremental effects that varying wind power production imposes on the power system, it is important to study the system as a whole: only the net imbalances have to be balanced by the system. Large geographical spreading of wind power will reduce variability, increase predictability and decrease the occasions with near zero or peak output. The goal of this work was to estimate the increase in hourly load‐following reserve requirements based on real wind power production and synchronous hourly load data in the four Nordic countries. The result is an increasing effect on reserve requirements with increasing wind power penetration. At a 10% penetration level (wind power production of gross demand) this is estimated as 1·5%–4% of installed wind capacity, taking into account that load variations are more predictable than wind power variations. Copyright © 2004 John Wiley & Sons, Ltd.     

Time series modelling of power output for large‐scale wind fleets

Simulations of power systems with high wind penetration need to represent the stochastic output of the wind farms. Many studies use historic wind data directly in the simulation. However, even if historic data are used to drive the realized wind output in scheduling simulations, a model of the wind's statistical properties may be needed to inform the commitment decisions for the dispatchable units. There are very few published studies that fit models to the power output of nation‐sized wind fleets rather than the output at a single location. We fitted a time series model to hourly, time‐averaged, aggregated wind power data from New Zealand, Denmark and Germany, based on univariate, second‐order autoregressive drivers. Our model is designed to reproduce the asymptotic distribution of power output, the diurnal variation and the volatility of power output over timescales up to several hours. For the cases examined here, it was also found to provide a generally good representation of the overall distribution of power output changes and the variation of volatility with power output level, as well as an acceptable representation of the distribution of calm periods. Copyright © 2011 John Wiley & Sons, Ltd.     

Estimation of variability and predictability of large‐scale wind energy in The Netherlands

This paper presents a data‐driven approach for estimating the degree of variability and predictability associated with large‐scale wind energy production for a planned integration in a given geographical area, with an application to The Netherlands. A new method is presented for generating realistic time series of aggregated wind power realizations and forecasts. To this end, simultaneous wind speed time series—both actual and predicted—at planned wind farm locations are needed, but not always available. A 1‐year data set of 10‐min averaged wind speeds measured at several weather stations is used. The measurements are first transformed from sensor height to hub height, then spatially interpolated using multivariate normal theory, and finally averaged over the market resolution time interval. Day‐ahead wind speed forecast time series are created from the atmospheric model HiRLAM (High Resolution Limited Area Model). Actual and forecasted wind speeds are passed through multi‐turbine power curves and summed up to create time series of actual and forecasted wind power. Two insights are derived from the developed data set: the degree of long‐term variability and the degree of predictability when Dutch wind energy production is aggregated at the national or at the market participant level. For a 7.8 GW installed wind power scenario, at the system level, the imbalance energy requirements due to wind variations across 15‐min intervals are ±14% of the total installed capacity, while the imbalance due to forecast errors vary between 53% for down‐ and 56% for up‐regulation. When aggregating at the market participant level, the balancing energy requirements are 2–3% higher. Copyright © 2008 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.     

Effect of energy storage on variations in wind power

Irregularities in power output are characteristic of intermittent energy, sources such as wind energy, affecting both the power quality and planning of the energy system. In this work the effects of energy storage to reduce wind power fluctuations are investigated. Integration of the energy storage with wind power is modelled using a filter approach in which a time constant corresponds to the energy storage capacity. The analyses show that already a relatively small energy storage capacity of 3 kWh (storage) per MW wind would reduce the short‐term power fluctuations of an individual wind turbine by 10%. Smoothing out the power fluctuation of the wind turbine on a yearly level would necessitate large storage, e.g. a 10% reduction requires 2–3 MWh per MW wind. Copyright © 2005 John Wiley & Sons, Ltd.     

Hourly wind power variations in the Nordic countries

Studies of the effects that wind power production imposes on the power system involve assessing the variations of large‐scale wind power production over the whole power system area. Large geographical spreading of wind power will reduce variability, increase predictability and decrease the occasions with near zero or peak output. In this article the patterns and statistical properties of large‐scale wind power production data are studied using the data sets available for the Nordic countries. The existing data from Denmark give the basis against which the data collected from the other Nordic countries are benchmarked. The main goal is to determine the statistical parameters describing the reduction of variability in the time series for the different areas in question. The hourly variations of large‐scale wind power stay 91%–94% of the time within ±5% of installed capacity in one country, and for the whole of the Nordic area 98% of the time. For the Nordic time series studied, the best indicator of reduced variability in the time series was the standard deviation of the hourly variations. According to the Danish data, it is reduced to less than 3% from a single site value of 10% of capacity. Copyright © 2004 John Wiley & Sons, Ltd.     

Variability in large‐scale wind power generation

The paper demonstrates the characteristics of wind power variability and net load variability in multiple power systems based on real data from multiple years. Demonstrated characteristics include probability distribution for different ramp durations, seasonal and diurnal variability and low net load events. The comparison shows regions with low variability (Sweden, Spain and Germany), medium variability (Portugal, Ireland, Finland and Denmark) and regions with higher variability (Quebec, Bonneville Power Administration and Electric Reliability Council of Texas in North America; Gansu, Jilin and Liaoning in China; and Norway and offshore wind power in Denmark). For regions with low variability, the maximum 1 h wind ramps are below 10% of nominal capacity, and for regions with high variability, they may be close to 30%. Wind power variability is mainly explained by the extent of geographical spread, but also higher capacity factor causes higher variability. It was also shown how wind power ramps are autocorrelated and dependent on the operating output level. When wind power was concentrated in smaller area, there were outliers with high changes in wind output, which were not present in large areas with well‐dispersed wind power. Copyright © 2015 John Wiley & Sons, Ltd.     

陕西省金润河北镇风电场风能资源开发评估

对陕西省宝鸡市陇县金润河北镇风电场气象条件、风功率密度、平均风速、主导风向等风能参数进行分析评价。结果表明,测风塔100 m高度月平均风速、月平均风功率密度最大均出现在4月,最小均出现在8月;测风塔100 m高度主导风向为SSW(南西南),主要风能方向为SSW(南西南),风电场风功率密度等级为1级。风电场安装20台2500 kW的风电机组,装机容量50 MW,年设计发电量1.33485×108 kW·h,年出厂电量9.5426×107 kW·h。结果可为其他风电场选址和发电量估算提供参考。     

Adjustment of wind farm power output through flexible turbine operation using wind farm control

When the installed capacity of wind power becomes high, the power generated by wind farms can no longer simply be that dictated by the wind speed. With sufficiently high penetration, it will be necessary for wind farms to provide assistance with supply‐demand matching. The work presented here introduces a wind farm controller that regulates the power generated by the wind farm to match the grid requirements by causing the power generated by each turbine to be adjusted. Further, benefits include fast response to reach the wind farm power demanded, flexibility, little fluctuation in the wind farm power output and provision of synthetic inertia. Copyright © 2015 John Wiley & Sons, Ltd.     

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。     

A review on scale and siting of wind farms in China

Using a wind power information system, which is based on the geographic information system (GIS), this paper presents a statistical analysis of built and approved wind farms to the end of 2008. The average capacity of wind turbine generators (WTGs) and the scale of wind farms are investigated. The geographical distribution of wind farms and the main factors affecting wind farm siting are analysed. The results show: (i) The average capacity of WTGs is increasing; MW scale WTGs have become the mainstream product in the China market. (ii) Because of the national wind project approval policy, most of the wind farms have installed capacities of just under 50 MW. (iii) When siting a wind farm, wind resource is still the predominant factor being considered, while grid connection is still the main constraint in this stage. (iv) Concerning land use, among the wind farms built by 2008, 40% use grasslands, 31% use agricultural land, 17% use desert or bare lands, while 12% use farmlands. (v) Statistics show 88.6% of the built wind farms have elevation change of less than 300 m, and 90% of the built wind farms have GIS slopes of less than 10°. (vi) The geographical distribution of wind farms reveals a highly concentrated deployment tendency, 58.7% of all the newly built wind farms in 2008 are within 10 km of existing wind farms. Copyright © 2010 John Wiley & Sons, Ltd.     

风电场中抽水蓄能电站的优化配置

为解决电力系统接入风电容量超过一定比例后引起的调峰及弃风问题,改善风电场功率输出特性,可采用与风电场配套建设抽水蓄能电站的模式。在建立风电场及抽水蓄能电站运行模型的基础上,以风电—抽水蓄能电站经济效益为目标,采用一种新的自适应遗传算法对风电—抽水蓄能电站的最佳容量配比进行求解。通过对一实际算例的仿真,表明配置适当容量的抽水蓄能电站可提高风电场的综合效益。     

疆电与河西风电外送对西北电网网损影响

新疆与西北750 kV联网工程、甘肃千万kW级风电一期外送工程建成投运以及直流外送能力的大幅提升,对西北电网跨区跨省交易网损分摊机制提出了新的要求。对国内外电力市场及西北电网的网损分摊机制进行了归纳研究;计算分析了新疆电力外送及河西风电大规模上网对西北电网网损的影响;定义了交易功率的额外平均网损系数,验证了此系数应用于转运网损分摊的合理性;对西北电网网损管理在疆电与河西风电外送情况下面临的新问题提出了有益的补充办法。     

A spatial and temporal correlation analysis of aggregate wind power in an ideally interconnected Europe

Studies have shown that a large geographic spread of installed capacity can reduce wind power variability and smooth production. This could be achieved by using electricity interconnections and storage systems. However, interconnections and storage are not totally flexible, so it is essential to understand the wind power correlation in order to address power system constraints in systems with large and growing wind power penetrations. In this study, the spatial and temporal correlation of wind power generation across several European Union countries was examined to understand how wind ‘travels’ across Europe. Three years of historical hourly wind power generation data from 10 countries were analysed. The results of the analysis were then compared with two other studies focused on the Nordic region and the USA. The findings show that similar general correlation characteristics do exist between European country pairs. This is of particular importance when planning and operating interconnector flows, storage optimization and cross‐border power trading. Copyright © 2017 The Authors Wind Energy Published by John Wiley & Sons Ltd     

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.     

一种并网型风光互补发电系统的建模与仿真

分析了风光互补发电系统的技术优势,设计了基于固态变压器结构的并网型风光互补发电系统。分别建立了光伏系统,风力发电系统,超级电容和蓄电池的模型,并分析各环节的控制策略,提出了基于平均功率的储能设备容量配置方法。仿真结果表明,该系统能模拟风光互补系统在不同模式下的运行特性,可以有效降低功率波动和维持电压稳定,并能在低光照强度、低风速等情况下为系统提供短时能量支撑。     

计及风火系统聚合特性的虚拟电厂经济调度研究

提出一个VPP(虚拟发电厂)聚合出力特性的含新能源电网的两阶段经济调度模型。第一阶段基于包含若干风场与火电机组的VPP聚合出力特性决策VPP的总出力;第二阶段将总出力在VPP内部各发电单元之间进行分配。上述两个阶段都采用线性规划方法求解。基于IEEE—6母线电网的数值算例的计算结果表明,与传统经济调度方法相比,本研究提出的模型能降低风电弃风量近50%。