基于Elman神经网络的短期风电功率预测

为提高风电场输出功率预测精度,提出一种动态基于神经网络的功率预测方法。根据实际运行的风电场相关风速、相关风向和风电功率的历史数据,建立了基于Elman神经元网络的短期风电功率预测模型。运用多层Elman神经网络模型对西北某风电场实际1h和24h的风电输出功率预测,与BP神经网络模型对比,经仿真分析证明前者具有预测精度高的特点,三隐含层Elman神经网络模型预测效果最佳。这表明利用Elman回归神经网络建模对风电功率进行预测是可行的,能有效提高功率预测精度。     

基于人工神经网络的风电场短期功率预测

基于人工神经网络法(ANN)对内蒙古某风电场短期输出功率进行了预测研究,给出了较详细的实现过程,并比较了单一ANN预测方法和基于物理方法与统计方法的混合ANN预测方法的预测精度.计算结果表明,单—ANN预测方法能快速给出预测结果,但预测精度较低,均方根误差为10.67％;而混合ANN预测方法步骤较多且较费时,但预测精度较高,均方根误差为2.01％,不到单一ANN法的1/5.同时,针对预测过程中小于5m/s的小风速段和大于15m/s的大风速段所呈现的预测误差较小的原因进行了深入分析.     

混合储能提高风出力预测精度研究

风气象信息精细化程度不够造成风电场风出力预测精度低,给电网调度增加了难度,采用储能装置可提高预测精度,但是合理又经济地配置储能容量较困难。文章提出在风电场中配置蓄电池和超级电容器混合储能系统,提高风电场日前预报精度;通过高通滤波器得到误差变化较快的成分,由超级电容器来弥补;由蓄电池来弥补剩余变化较慢的误差成分。综合考虑这些误差变化特点及储能充放电功率发生概率特点,合理选取储能额定容量,并且分别搭建了超级电容器和蓄电池储能系统模糊控制规则库,根据各自荷电状态SOC优化分配混合储能充放电功率。最后对新疆某风电场并入混合储能进行了仿真分析,结果表明:采用模糊控制策略的混合储能系统能够更显著有效地提高风出力短期预测精度。     

提高风出力预测精度的储能系统模糊控制策略

风气象信息的精细化程度不够高会造成风电场风出力预测精度的降低,给电网调度增加了难度。在风电场中配置锌溴电池储能系统是提高风电场日前预报精度的有效措施,对储能装置控制是其关键问题。文章采用模糊控制方法,搭建了储能系统模糊控制规则库,根据电池储能系统荷电状态SOC的变化来控制储能充放电功率;并将所提出的控制策略在新疆达坂城风电场-储能联合发电系统中进行了仿真验证,与传统控制策略进行了对比分析。研究结果表明,采用模糊控制策略的储能系统能够更有效地提高风出力短期预测精度,85%的预测值达到了国家电网要求。     

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。     

基于小波PLS的风电功率预测

风电场输出功率预测对接入大量风电的电力系统运行具有重要意义。针对风电的间歇性和波动性,以及传统物理方法预测精度低,提出了物理方法和小波PLS相结合的风电功率预测算法。该方法可较好地实现数据去噪和样本预处理,并可对输入因素进行成分提取,对因变量有较好的解释能力,且有利于提高风电功率的预测精度。最后通过某风场的实际运行数据对该算法进行了验证,将其他预测模型的预测结果与物理方法和小波PLS的组合预测结果进行了对比,结果表明文章预测法方精度更高,效果更好。     

含风电场的交直流互联电网协作式分布式模型预测控制

针对风电场输出功率的强波动性对交直流互联电网调频的影响,提出了基于协作式分布式模型预测控制的交直流互联电网调频控制方法,以提高风电经交直流通道外送互联系统的频率稳定性,克服集中式模型控制方法扩展性差的缺点,该控制策略具有全局最优解。文章建立了含风电场的交直流互联电网调频控制模型,构建了基于协作式分布式模型预测控制的含风电场交直流互联电网的经济性调频控制策略。以3区域交直流互联电网调频控制模型进行仿真,结果表明,文章所提出的协作式分布式模型预测控制方法,能够有效地改善风电场输出功率随机波动的频率稳定性,使系统频率及区域间交换功率在较小的范围内变化,其控制效果明显优于传统的分布式模型预测控制和分散式模型预测控制。     

基于组合预测方法的风电场短期风速预测

基于持续法、人工神经网络法(ANN)和支持向量机(SVM)3种不同预测模型对内蒙古某风电场短期风速进行了预测研究,比较了不同单一预测模型的预测精度,并进行了4种不同预测模型的组合预测。计算结果表明,单一预测模型中支持向量机方法精度最高,而组合预测中3种方法组合的预测精度最高,并且组合预测精度均高于单一预测方法的精度。同时发现,当单一模型预测误差之间存在较强的负相关关系时,组合预测精度提高明显;而当单一模型预测误差之间存在较强的正相关关系时,则组合预测精度改进有限。     

用于风功率平抑的SOEC系统功率控制策略

为了平抑风电场输出功率波动,考虑到固体氧化物电解池(SOEC)电解水制氢的高效率特性,基于实际数据构建千瓦级SOEC电堆模型并验证了模型精度。针对SOEC系统的非线性和时滞特性,提出一种基于模型预测的功率控制策略,用于平抑风电场输出功率波动。根据某15 MW风电场运行数据,采用集合经验模态分解方法,提出一种风电场输出功率分解方法,获得SOEC系统的充电功率指令并进行仿真验证。结果表明：基于模型预测的SOEC系统功率控制效果很好,可以实现充电功率指令的跟随变化,且平均绝对百分比误差为1.674%,平抑风功率波动效果的准确性较高。     

南方电网风电功率预测系统应用扩展研究

相比北方大型风电大多建于较为平坦内陆区域,南方电网区域内很多风电场则建设在相对复杂的地形环境,其中较有代表性的是高山、丘陵和海岸风电场,给风电功率预测系统的运行提出较大挑战。风电功率预测系统应用扩展性研究目的是使预测系统能在复杂而多变的实际现场应用场景下保持一定精度水平,满足系统用户需求。研究了南方电网区域内典型风资源特性和风电出力特性,根据这些特性,围绕当前预测系统功能流程中的三个主要技术环节——多输入数据源、功率预测建模和预测结果展现,提出应用扩展性研究框架。该项工作可以用于在数值天气预报精度有限的条件下改善预测精度,并且指导预测系统各技术环节中子技术选项的灵活组合方案,提高预测系统在工程现场的应用价值。基于南方电网区域内某风电场历史数据,论证了所提方法的有效性。     

Variability and uncertainty of wind power in the California electric power system

The California generation fleet manages the existing variability and uncertainty in the demand for electric power (load). When wind power is added, the dispatchable generators manage the variability and uncertainty of the net load (load minus wind power). The variability and uncertainty of the load and the net load are compared when 8790 MW of wind power are added to the California power system, a level expected when California achieves its 33% renewable portfolio standard, using a data set of 26,296 h of synchronous historic load and modeled historic wind power output. Variability was calculated as the rate of change in power generated by wind farms or consumed by the load from 1 h to the next (MW/h). Uncertainty was calculated as the 1 h ahead forecast error [MW] of the wind power or of the load. The data show that wind power adds no additional variability than is already present in the load variability. However, wind power adds additional uncertainty through increased forecast errors in the net load compared with the load. Forecast errors in the net load increase 18.7% for negative forecast errors (actual less than forecast) and 5.4% for positive forecast errors (actual greater than forecast). The increase in negative forecast errors occurs only during the afternoon hours when negative load forecasts and positive wind forecasts are strongly correlated. Managing the integration of wind power in the California power system should focus on reducing wind power forecast uncertainty for wind ramp ups during the afternoon hours. Copyright © 2013 John Wiley & Sons, Ltd.     

Application of mesoscale ensemble forecast method for prediction of wind speed ramps

Sudden changes in wind speed, so‐called wind speed ramps, are a major concern for wind power system operators. The present study applies the mesoscale ensemble forecast method for the prediction of wind speed ramps at wind farms in Japan and evaluates the ability and utility of this method. The mesoscale ensemble forecast in this study (ENS21) consists of 21 members with a horizontal resolution of 10 km for a 5‐year period. The simulated results show that ENS21 produces better accuracy than the deterministic forecast with a horizontal resolution of 10 km (DET_L). On the other hand, the deterministic forecast with a horizontal resolution of 5 km (DET_H) also produces better accuracy than DET_L. From a practical perspective, however, the ENS21 is computationally expensive. Thus, the eight‐member mesoscale ensemble forecast (ENS8) with as same computational cost as a deterministic forecast with a horizontal resolution of 5 km (DET_H) is also evaluated. The simulated results show that ENS8 has almost same accuracy as ENS21 and DET_H in wind speed ramp forecasts. ENS8 has advantages over ENS21 and DET_H because ENS8 is computationally efficient and is able to benefit wind power operators with flexibility in the selection of probability thresholds for decision processes compared with a single. It can be concluded that the mesoscale ensemble forecast method is more useful for prediction of the wind speed ramp than the single deterministic forecast method with the same computational cost if the ensemble members are successfully selected.     

Impact of wind farm integration on electricity market prices

Wind generation is considered one of the most rapidly increasing resources among other distributed generation technologies. Recently, wind farms with considerable output power rating are installed. The variability of the wind output power, and the forecast inaccuracy could have an impact on electricity market prices. These issues have been addressed by developing a single auction market model to determine the close to real-time electricity market prices. The market-clearing price was determined by formulating an optimal power flow problem while considering different operational strategies. Inaccurate power prediction can result in either underestimated or overestimated market prices, which would lead to either savings to customers or additional revenue for generator suppliers.     

Technical aspects of status and expected future trends for wind power in Denmark

The power system of Denmark is characterized by significant incorporation of wind power. Presently, more than 20% of the annual electricity consumption is covered by electricity‐producing wind turbines. The largest increase in grid‐incorporated wind power is expected to come from large (offshore) wind farms operating as large wind power plants with ride‐through solutions, connected to the high‐voltage transmission system and providing ancillary services to the system. In Denmark there are presently two offshore wind farms connected to the transmission system: Horns Rev A (160MW rated power in the western part of the country) and Nysted (165MW rated power at Rødsand in Eastern Denmark). The construction of two more offshore wind farms, totalling 400MW by the years 2008–2010, has been announced. This article presents the status, perspectives and technical challenges for wind power in the power system from the point of view of Energinet.dk, Transmission System Operator of Denmark. Copyright © 2006 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.     

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.     

Predicting the Wind

Due to increasing wind power penetration, the need for and usage of wind power prediction systems have increased. At the same time, much research has been done in this field, which has led to a significant increase in the prediction accuracy recently. With many ongoing research programs in the field of numerical weather prediction (NWP), as well as in the power output prediction models (transforming wind speed into electrical power output), one can expect further improvements in the future. For the time being, three measures are taken as best practices to reduce prediction errors: Combinations of different models can be done with power output forecast models as well as with NWP models (multimodel and multischeme approaches). Reductions in RMSE of up to 20% were shown with intelligent combinations. As expected, a shorter forecast horizon leads to lower prediction errors. However, the organization of the electricity market as well as the conventional generation pool has a large influence on the needed forecast horizon. The forecast error depends on the number of wind turbines and wind farms and their geographical spread. In Germany, typical forecast errors for representative wind farm forecasts are 10-15% RMSE of installed power, while the error for the control areas calculated from these representative wind farms is typically 6-7% and that for the whole of Germany only 5-6%. Whenever possible, aggregating wind power over a large area should be performed as it leads to significant reduction of forecast errors as well as short-term fluctuations. a large area should be performed as it leads to significant reduction of forecast errors as well as short-term fluctuations.     

Short‐term forecasting of categorical changes in wind power with Markov chain models

As penetrations of renewable wind energy increase, accurate short‐term predictions of wind power become crucial to utilities that must balance the load and supply of electricity. As storage of wind energy is not yet feasible on a large scale, the utility must integrate wind energy as soon as it is generated and decide at each balancing time‐step whether a change in conventional energy output is required. With high penetrations of wind energy, utilities must also plan for operating reserves to maintain stability of the electricity system when forecasts for renewable energy are inaccurate. Thus, a simple forecast of whether the wind power will increase, decrease or not change in the next time‐step will give utility operators an easy tool for assessing whether changes need to be made to the current generation mix. In this work, Markov chain models based on the change in power output at up to three locations or lags in time are presented that not only produce such an hourly forecast but also include a measure of the uncertainty of the forecast. Forecasts are greatly improved when knowledge of whether the maximum or minimum wind power is currently being produced and the intrahour trend in wind power are incorporated. These models are trained, tested and evaluated with a uniquely long set of 2 years of 10 min measurements at four meteorological stations in the Pacific Northwest and perform better than a benchmark state‐of‐the‐art wind speed forecasting model.Copyright © 2013 John Wiley & Sons, Ltd.     

Implementation of a Model Output Statistics based on meteorological variable screening for short‐term wind power forecast

A combination of physical and statistical treatments to post‐process numerical weather predictions (NWP) outputs is needed for successful short‐term wind power forecasts. One of the most promising and effective approaches for statistical treatment is the Model Output Statistics (MOS) technique. In this study, a MOS based on multiple linear regression is proposed: the model screens the most relevant NWP forecast variables and selects the best predictors to fit a regression equation that minimizes the forecast errors, utilizing wind farm power output measurements as input. The performance of the method is evaluated in two wind farms, located in different topographical areas and with different NWP grid spacing. Because of the high seasonal variability of NWP forecasts, it was considered appropriate to implement monthly stratified MOS. In both wind farms, the first predictors were always wind speeds (at different heights) or friction velocity. When friction velocity is the first predictor, the proposed MOS forecasts resulted to be highly dependent on the friction velocity–wind speed correlation. Negligible improvements were encountered when including more than two predictors in the regression equation. The proposed MOS performed well in both wind farms, and its forecasts compare positively with an actual operative model in use at Risø DTU and other MOS types, showing minimum BIAS and improving NWP power forecast of around 15% in terms of root mean square error. Further improvements could be obtained by the implementation of a more refined MOS stratification, e.g. fitting specific equations in different synoptic situations. Copyright © 2012 John Wiley & Sons, Ltd.