Probabilistic Forecasts of Wind Power Generation by Stochastic Differential Equation Models

The increasing penetration of wind power has resulted in larger shares of volatile sources of supply in power systems worldwide. In order to operate such systems efficiently, methods for reliable probabilistic forecasts of future wind power production are essential. It is well known that the conditional density of wind power production is highly dependent on the level of predicted wind power and prediction horizon. This paper describes a new approach for wind power forecasting based on logistic‐type stochastic differential equations (SDEs). The SDE formulation allows us to calculate both state‐dependent conditional uncertainties as well as correlation structures. Model estimation is performed by maximizing the likelihood of a multidimensional random vector while accounting for the correlation structure defined by the SDE formulation. We use non‐parametric modelling to explore conditional correlation structures, and skewness of the predictive distributions as a function of explanatory variables. Copyright © 2015 John Wiley & Sons, Ltd.     

Adaptive modelling and forecasting of offshore wind power fluctuations with Markov‐switching autoregressive models

Wind power production data at temporal resolutions of a few minutes exhibit successive periods with fluctuations of various dynamic nature and magnitude, which cannot be explained (so far) by the evolution of some explanatory variable. Our proposal is to capture this regime‐switching behaviour with an approach relying on Markov‐switching autoregressive (MSAR) models. An appropriate parameterization of the model coefficients is introduced, along with an adaptive estimation method allowing accommodation of long‐term variations in the process characteristics. The objective criterion to be recursively optimized is based on penalized maximum likelihood, with exponential forgetting of past observations. MSAR models are then employed for one‐step‐ahead point forecasting of 10 min resolution time series of wind power at two large offshore wind farms. They are favourably compared against persistence and autoregressive models. It is finally shown that the main interest of MSAR models lies in their ability to generate interval/density forecasts of significantly higher skill. Copyright © 2010 John Wiley & Sons, Ltd.     

基于小波阀值降噪和BP神经网络的超短期风电功率预测

针对超短期风电功率预测问题，考虑了风电场复杂的噪声背景和风电功率的波动性，提出了一种基于小波阀值降噪-BP神经网络的超短期风电功率预测方法。该方法采用近似对称光滑的紧支撑双正交小波db4（Daubechies函数）作为小波基，通过多分辨分析的Mallat算法对历史时序风电功率数据进行3尺度分解。根据Donoho阀值法对各层小波系数进行软阀值降噪处理，再通过小波逆变换重构历史时序风电功率，由BP神经网络对其进行训练，预测目的风电功率序列。仿真算例将该方法与普通BP神经网络方法进行了对比，比较结果证明其预测精度优于后者，具有很好鲁棒性和降噪性能，适用噪声复杂的风电场超短期风电功率在赣预测．     

Power grid operation optimization and forecasting using a combined forecasting system

Reliable photovoltaic and wind power generation forecasts are essential for efficient power systems operations. A combined forecasting system is developed, which integrates a data preprocessing method, a sub-predictor selection rule, and a multi-objective optimization to integrate various forecasting models. The proposed system effectively aggregates the advantages of all algorithms involved, facilitating greater prediction precision and stability. Experiments indicated that the proposed system can achieve higher quality point and interval forecasting performance relative to the comparative approaches.     

智能电网基本理念阐释

由于可再生能源开发利用的快速发展,世界各国面临着可再生能源如何接入及充分利用等一系列问题,而智能电网则是应对这些挑战的有效战略措施.智能电网具有强大的功能、显著的综合效益和广阔的发展前景,是世界电网发展的趋势.但是智能电网不是一个单纯的技术问题,它涉及许多重要的基本理念.厘清这些理念对于科学高效地实施智能电网、对于技术创新和产业发展至关重要.为此本文从智能电网的特点、智能电网的总体设想和技术内涵、与智能电网相关的技术、智能电网的近期与远期目标以及在实施智能电网时需要注意的事项等方面阐释了智能电网的基本理念.     

风力发电冷却技术

随着风力发电机单机容量的逐步增大，发电机内各部件的散热量也将大大增加，如何有效解决发电机的温升瓶颈，已成为风力发电机进一步发展的关键问题之一。本文首先对风力发电的原理与结构进行了综述，分析了风力发电机运行过程中热量产生的部件和原因，介绍了目前风力发电机组所采用的冷却技术，并在此基础上对下一代大功率风力发电机冷却技术进行了展望。     

集聚效应对中国风电产业发展差异影响的实证研究

风电产业的发展是清洁能源发展的热点之一。本文从产业集聚效应的角度出发,深入研究了我国风电产业链的发展情况,利用地区集中度LQ指标对2009~2012年华北、东北、西北和江苏沿海风电产业集聚情况进行了实证研究,得出我国四大区域确实存在风电产业集聚现象。同时利用产业地区集中度cR指标测度各地的市场集中度,深入分析了我国典型地区的风电产业集聚特征,得出受集聚效应和各地经济资源禀赋不同的影响,我国风电产业发展存在差异且缺少地区间的产业联动发展,并提出相应的对策建议。     

Wind power prediction based on wind speed forecast using hidden Markov model

This study examines a new approach for short-term wind speed and power forecasting based on the mixture of Gaussian hidden Markov models (MoG-HMMs). The proposed approach focuses on the characteristics of wind speed and power in the consecutive hours of previous days. The proposed method is carried out in two steps. In the first step, for the hourly prediction of wind speed, several wind speed features are employed in MoG-HMM, and in the second step, the results obtained from the first step along with their characteristics and wind power features are used to predict wind power estimation. To increase the prediction accuracy, the data used in each step are classified, and then for each class, one HMM with its specific parameters is used. The performance of the proposed approach is examined using real NREL data. The results show that the proposed method is more precise than other examined methods.     

风能技术发展中的几个问题

改善能源结构，利用可再生能源，减轻环境污染，提高人民生活质量，已成了全球能源工业关注的一个热点，风能是目前最有开发利用前景和技术最成熟的一种新能源和可再生能源之一。随着风能开发和利用的发展，风能技术也取得了显著的进步，并逐渐成为能源技术中的一个分支。本文简要介绍当前风能技术发展中的几个关注的问题。     

基于混沌-RBF神经网络的风电场风速预测

在利用风速时间序列具有混沌特性的前提下，将相空间重构和RBF神经网络结合的混合算法用于风电场风速预测。通过实例仿真计算对比表明，该混沌-RBF神经网络的混合算法可以进一步提高预测准确度。     

风力发电研究和开发的现状与展望

风力发电是世界上增长最快的可再生能源，是未来基于可持续发展和零污染电能的一线希望，其开发利用是最终解决常规能源尤其是化石能源带来的能源短缺、环境污染以及温室效应等问题的有效途径之一，是理想的替代能源。本文在综合分析世界上风力发电技术发展现状的基础上。还对风力发电的未来发展趋势进行了预测——风力发电在本世纪无疑将会得到更加广泛的应用。     

基于FLUENT的NA63A系列翼型气动特性对比分析

利用计算流体力学软件FLUENT6．3．2对在风力发电领域应用较为广泛的NA63A系列翼型的外部流场进行数值模拟，采用Spalar-S．Allmaras湍流计算模型求解该系列翼型在不同攻角下的压力、速度分布。进而对NA63A系列不同翼型在相同攻角下以及网种翼型在不同攻角下的气动特性进行对比分析并总结规律，为该类翼型的性能研究提供了一定的理论依据。     

电力网的尺度问题——从纳米到太空尺度

探讨了电力网的尺度问题,阐述了国际微电网的发展态势,并基于纳米尺度发电机、太空尺度太阳能发电站以及黑洞发电机等电力获取途径,特别论述了两种超常规的电网:纳米尺度电网和太空尺度电网.进一步地,还应用复杂网络理论分析了纳米尺度电网和太空尺度电网的固有属性,为其未来的设计规划做了一定程度的铺垫.     

Shift-contagion in energy markets and global crisis

This paper presents an analysis of shift-contagion in energy markets, testing whether linkages between returns in energy markets increase during crisis periods. The research presented herein demonstrates how common movement between energy markets increases due to (i) shift-contagion across energy markets, reflected by structural transmission of shocks across markets and (ii) larger common shocks operating through standard cross-market interdependences. A regime-switching model was developed to detect shift-contagion across energy markets. In the approach adopted herein, the occurrence of shift-contagion is endogenously estimated rather than being exogenously assigned. The results show that shift-contagion has been a major feature of energy markets over the last decade. Evidence is presented which demonstrates that the linkages between energy markets do not appear to be stable. These results are remarkably accurate for forecasting Brent and natural gas for horizons for up to 50 days. Conversely, for WTI (West Texas Intermediate oil) and coal, the model performs well only for forecasting very short horizons (up to 20 days). For all products, the model shows significant biases for long horizons.     

Power transformation models and volatility forecasting

This paper considers the forecast accuracy of a wide range of volatility models, with particular emphasis on the use of power transformations. Where one‐period‐ahead forecasts are considered, the power autoregressive models are ranked first by a range of error metrics. Over longer forecast horizons, however, generalized autoregressive conditional heteroscedasticity models are preferred. A value‐at‐risk‐based forecast assessment indicates that, while the forecast errors are independent, they are not independent and identically distributed, although this latter result is sensitive to the choice of forecast horizon. Our results are robust across a number of different asset markets. Copyright © 2008 John Wiley & Sons, Ltd.     

基于一元线性回归由风速传感器测量值推导巷道平均风速

通过试验数据建立了风速传感器的测量值与平均风速之间的一元线性回归方程，并对拟合效果进行了评价。对参数的判断表明该方程是有效的，可以近似地把风速传感器的测量值转换成平均风速值，并在相同的巷道长度、相同的风流速度、不同断面下用Comsol模拟了风速流场分布情况。     

Short term load forecasting in electric power systems: A comparison of ARMA models and extended wiener filtering

On-line prediction of electric load in the buses of the EHV grid of a power generation and transmission system is basic information required by on-line procedures for centralized advanced dispatching of power generation. This paper presents two alternative approaches to on-line short term forecasting of the residual component of the load obtained after the removal of the base load from a time series of total load. The first approach involves the use of stochastic ARMA models with time-varying coefficients. The second consists in the use of an extension of Wiener filtering due to Zadeh and Ragazzini. Real data representing a load process measured in an area of Northern Italy and simulated data reproducing a non-stationary process with known characteristics constitute the basis of a numerical comparison allowing one to determine under which conditions each method is more appropriate.     

列车通过大跨度桥梁桥塔区域过程中的风-车-桥耦合动力分析

大跨度桥梁位于强横风环境时,桥塔的遮挡作用会使列车进出桥塔区域时车体受到的风力发生变化,这种风荷载的突变效应会对车辆的动力响应造成影响,此外车辆动力响应的改变还会影响桥梁的动力响应.本文以瓯江大跨公铁两用悬索桥为工程背景,以快速谱分析模拟风场,对桥梁子系统施加抖振风力,对桥梁子系统施加稳态风力,考虑桥塔宽度与车辆长度的关系,建立风-车-桥耦合系统运动平衡方程,以全过程迭代法求解该方程,分析计算了不同车速与风速下桥塔遮风效应对车辆桥梁动力响应的影响.结果表明风速和桥塔宽度越大,车辆的动力响应越大,而桥梁动力响应受桥塔遮风效应影响很小.     

接触网抗风可靠性研究现状及展望

介绍了接触网抗风可靠性研究的国内外现状;并分析了接触网风荷载作用下的失效形式、抗风可靠性的安全界限和破坏准则;对存在的难点及研究方向提出了几点建议。     

基于WAsP的不同风资源下风力机的选择

提出了一种确定适合于不同地区的风力机的方法。首先利用WAsP软件对当地风能资源评估，分析计算得到风谱图，建立模拟风力机站；然后拟合4种类型的400W风力机的输出功率特性曲线；最后计算不同风力机在不同地区风资源下的单位面积月发电量及其变动率。在保证满足供电负荷的条件下，单位面积月发电量的变动率最小的风力机确定为该地区风力机。