逐时风速概率分布研究

风速概率分布及其参数是体现风能资源统计特性的最重要指标之一。以山东省4个风电场测风塔和气象站测风年的逐时风速为样本,采用正态分布、指数分布、威布尔分布、伽马分布和Logistics分布对逐时风速概率分布进行研究,以Akaike信息准则判断概率分布的适用性。研究结果表明,威布尔分布、伽马分布和Logistics分布能更好的拟合小时风速的实际情况。     

矩函数在风速Weibull分布参数估计中的应用

服从Weibull分布的随机变量的各阶矩仍然服从Weibull分布,根据这一统计学性质,采用矩函数法估计Weibull分布参数.分别基于风速原点矩和风速中心矩对风速的Weibull分布参数k、c进行了估计,并给出了参数估计公式.根据所提方法解出风速概率分布后,可以根据Weibull分布计算风能特征指标.以河西某风电场为算例,分别以所提方法、均值和方差估计法对其进行Weibull分布参数估计和风能特征指标计算,同时给出了直接由风速数据统计出的各项特征指标进行对比,结果表明所提方法计算结果的整体误差较小,精度明显高于均值和方差估计法.     

风电场风速概率分布参数计算新方法

风能资源评估关系到风电的经济性和开发价值,一个地区的风速概率分布是该地区风能资源状况的最重要指标之一.在认为风电场风速服从双参数韦布尔(weibull)分布前提下,为了提高参数计算精度,从智能化的角度提出尝试采用改进的微粒群算法对Weibull双参数进行建模和优化.由此参数估算能直接反映出风能资源特性的风能特征指标,与...     

短时风速概率分布的混合威布尔逼近方法

风速概率分布特性是决定风电场功率输出的重要因素.常用的两参数威布尔函数在拟合较长周期风速时效果很好,然而在研究短周期或特殊时段风速概率分布后发现,其分布规律常为双峰或多峰分布,很难用传统方法准确逼近.对此,提出一种混合威布尔函数逼近特殊时段风速分布的方法,构造极大似然函数逼近风速实际分布并采用数论布点方法获得待求参数.结果显示,七参数混合威布尔函数显著提高了双峰型风速概率密度分布逼近的精度,而极大似然估计的加权处理,提高了关注风速段的拟合优度,达到了降低逼近误差的目的.     

采用平均风速参与因子法的区域风资源评估

目前多依据风能特征指标对单个风电场进行风资源评估,评估结果对区域能源规划所起到的参考作用比较有限。对此,提出了基于平均风速参与因子的区域风资源评估方法。按照风速观测站的位置进行地理划分,在各站风速历史数据中合理取样;在假设风速服从2参数威布尔(Weibull)分布的基础上,采用矩量法计算出各样本的风速分布参数;之后建立多维自回归模型,求得各样本风速序列的相互影响系数,应用所提平均风速参与因子方法求得区域等效风速分布参数,进行区域风资源评估。以某地10个风速观测站实测数据为样本,进行了算例分析。结果表明,所提风资源评估方法计算结果与实测数据统计结果较为接近,能有效解决区域风能能源规划中风资源评价的瓶颈问题。     

风资源评估改进方法的研究

风资源评估中以双参数的Weibull分布描述风速统计特征,并由此得出分布参数进行风能的特征指标计算和评估,因此,分布参数的估计对最后的评估结果有重要影响。最小二乘法等传统方法由风速直接计算分布参数,考虑风能与风速立方直接相关,本文首先对风速立方的矩与参数之间的关系进行考察,并给出3阶矩的计算方法,然后对经验公式的精度进行了考察。通过对某实际风场的算例与传统方法的比较,新方法在风能特征指标的计算上更接近实测结果。     

日风速随机模糊不确定模型EI北大核心CSCD

传统风速不确定模型无法兼顾其随机性和模糊性共存特征,该文考虑风速概率分布特征及其参数模糊性的随机模糊变量特征,提出一种日风速随机模糊不确定模型。考虑风速地域、季节和日特征差异,以美国NREL和我国某风电场实测风速为实例,提取多年同月日96时段和同月日同时段风速随机性特征,发现实例数据中接近或大于70%的时段服从两参数Weibull分布且同时段具有相似性,而形状参数k和尺度参数c存在模糊性;利用极大似然法挖掘k和c的模糊隶属函数特征,并在一定置信区间内定义其边界;依据不确定理论,定义风速为随机模糊变量且获取其机会测度分布函数,从而建立日风速不确定模型;最后,给出相应建模步骤及结合随机模糊模拟技术和逆变换法的日风速随机模糊模拟方法,5 000次模拟结果表明各时段风速处于历史相应时段风速上下限的概率大于94.13%,可有效用于日风速仿真。     

测风数据不同时间尺度对风电场产能评估的影响

风资源状况模拟的准确度主要体现在测风数据时间间隔上。以淮安某风电场测风塔一年及以上每10min的测风数据为基准,对不同时间间隔(30min、1h、2h)所记录的测风数据分别应用最小二乘法、平均风速和标准差估计法及极大似然估计法求解风速概率分布进行产能评估,对不同时间间隔下计算所得的发电量进行误差对比。结果指出,风电场发电量估计误差随着时间尺度增大而增大,采取30min时间间隔测风数据计算出的发电量与10min实测数据计算结果更为接近,当风电场选址区域的测风数据完整性较差时,研究结果具有一定指导作用。     

基于证据理论的风速不确定性建模

风速决定了风电场的输出功率,风速模型是研究含风电系统运行与规划的重要基础。提出一种基于证据理论的风速不确定建模方法。采用证据理论中基本可信度分配的概念描述风速;提出依据实测历史数据确定基本可信度分配的焦元和信任函数的实现方法,并设计等概率区间和等取值区间2种建模策略。以某风电场实测风速为例对基于所建模型和基于概率分布及区间分布的模型进行仿真比较,结果表明所提模型能确定风速的似然累积概率分布和信任累积概率分布,能更有效地描述和处理风速不确定性信息。     

新疆风电出力波动特性的概率建模

掌握风电出力的波动性规律是解决大规模风电并网运行难题的基础,分析了新疆某地区风电场出力的大量实测数据,提出了加权高斯混合分布概率模型模拟风电场及集群风电场功率变化特性,提出了反映风电出力波动特性的评价指标,采用极大似然估计法估算模型参数,并用多种概率分布模型模拟了新疆该地区风电场及集群风电场出力波动特性。仿真结果表明,加权高斯混合分布概率模型模拟精度最高,从而验证了加权高斯混合概率模型的可行性和有效性。     

最小误差逼近算法在风电场风能资源特性分析中的应用

文章采用最小误差逼近算法计算韦布尔分布特征参数和Ａ,和Ｋ,近似拟事风电场实际风速频率分布的韦布尔分布。根据比分布了解风电场的风速特性,通过选择适当的风力发电机组,可以比较准确地预测风电场年输出电能。     

风电场发电容量可信度研究

风力发电对电力系统发电可靠性具有一定贡献，如何衡量风电场的发电容量可信度是电力系统规划和评估风电场所必须解决的关键问题之一。文中建立了基于蒙特卡罗仿真的风电发电容量可信度的评估框架。风力模型、风机模型以及风电场模型构成了该体系的基础，其优点在于可以考虑不同风电场之间的内在关联特性。在此基础上利用可靠性指标，以风电场可替代的常规发电机组容量与风电场额定容量的比值作为定义考察了风电场的发电容量可信度，提出了多风电场系统风电容量可信度的计算方法。可靠性标准算例的仿真计算结果证明了所提出的评估体系的正确性和实用性。     

基于风速随机性的风储系统并网优化模型

针对储能系统能够对能量起到时间迁移作用,风储联合运行系统得到了广泛的关注。基于风电功率概率分布特征,建立了一个以风储系统的收益最大化为目标函数的概率并网模型。利用风速Weibull函数,推导出了储能系统功率在不同概率参数和不同并网方式下的概率分布函数。根据概率论区间估计方法,对风储系统中储能系统容量进行了计算和确定。最后,通过仿真软件,对储能容量确定方法和系统概率并网模型进行仿真,分析了不同概率参数和不同并网方式对储能容量和系统模型的影响,同时,验证了该模型和方法的可行性和正确性。     

Probabilistic power quality indices for electric grids with increased penetration level of wind power generation

In future electric grids it is expected that the share of power produced by renewable energy systems will increase to supply large deficits in power demand. Wind energy is one of the most important sources of renewable energy generation systems. With increased penetration level of wind energy conversion systems in modern electric grid, the quality of power will inevitably be affected. Power quality (PQ) indices are used to quantify the quality of the power. They serve as the basis for comparing negative impacts of different disturbances on power networks. Previous research of PQ indices with electric grids including wind energy sources was mainly based on fixed wind speed. Therefore, the PQ indices were calculated as instantaneous values that do not reflect the overall power quality impact of the grid connected wind energy systems. The main objective of this paper is to propose new probabilistic PQ indices for electric grids including wind energy systems. The proposed PQ indices combine both the probabilistic nature of wind speed using discrete Markov analysis and the electric grid behavior. The main PQ indices proposed are those concerning harmonics, flicker, and voltage sag. The developed indices are suitable for electric grids that include high penetration level of wind based power sources. The method used is general and can be applied to other power quality indices or power system performance indices.     

异步风力发电系统动态稳定性分析的数学模型及其应用

分析了异步风力发电系统的特点,介绍该系统各部分模型的结构,根据风速变化的异步风力发电系统动态稳定性分析的需要,建立了在一定程度上反映风电场实际风速变化的风速时空模型。由于风力机与发电机的柔性连接,使得风力机动态数学模型大为简化,取适当的惯性时间常数,风力机可用一阶惯性环节表示。     

A probabilistic model for assessing the long-term economics of wind energy

Understanding the long-term economic impact of wind energy on electricity markets is becoming more important due to the increasing penetration of wind power in the generation mix of power systems. In this paper, we evaluate the economics of wind energy by developing a probabilistic model to compute the long-term probability distribution of market clearing prices and wind farm revenues. The power system is assumed to consist of conventional generating units and wind farms. Availabilities of the generating units and the uncertainty in the wind power output are implicitly accounted for. The effect of increasing wind power penetration on the probability distribution functions of the market price and wind farm revenues is evaluated by changing the rated capacity of the wind farm. The model is illustrated by using a power system with a 32-unit and wind farms. The superiority of the proposed probabilistic model over a deterministic one is confirmed.     

Analysis of voltage stability uncertainty using stochastic response surface method related to wind farm correlation

Wind speed follows the Weibull probability distribution and wind power can have a significant influence on power system voltage stability. In order to research the influence of wind plant correlation on power system voltage stability, in this paper, the stochastic response surface method (SRSM) is applied to voltage stability analysis to establish the polynomial relationship between the random input and the output response. The Kendall rank correlation coefficient is selected to measure the correlation between wind farms, and the joint probability distribution of wind farms is calculated by Copula function. A dynamic system that includes system node voltages is established. The composite matrix spectral radius of the dynamic system is used as the output of the SRSM, whereas the wind speed is used as the input based on wind farm correlation. The proposed method is compared with the traditional Monte Carlo (MC) method, and the effectiveness and accuracy of the proposed approach is verified using the IEEE 24-bus system and the EPRI 36-bus system. The simulation results also indicate that the consideration of wind farm correlation can more accurately reflect the system stability.     

Research on multi-terminal traveling wavefault location method in complicatednetworks based on cloud computingplatform

Determination of the output power of wind generators is always associated with some uncertainties due to wind speed and other weather parameters variation, and accurate short-term forecasts are essential for their efficient operation. This can efficiently support transmission and distribution system operators and schedulers to improve the power network control and management. In this paper, we propose a double stage hierarchical adaptive neuro-fuzzy inference system (double-stage hybrid ANFIS) for short-term wind power prediction of a microgrid wind farm in Beijing, China. The approach has two hierarchical stages. The first ANFIS stage employs numerical weather prediction (NWP) meteorological parameters to forecast wind speed at the wind farm exact site and turbine hub height. The second stage models the actual wind speed and power relationships. Then, the predicted next day’s wind speed by the first stage is applied to the second stage to forecast next day’s wind power. The influence of input data dependency on prediction accuracy has also been analyzed by dividing the input data into five subsets. The presented approach has resulted in considerable forecasting accuracy enhancements. The accuracy of the proposed approach is compared with other three forecasting approaches and achieved the best accuracy enhancement than all.     

三种计算风速威布尔分布参数的比较

介绍计算威尔分布参数的累积分布函数拟合法、平均风速和标准差估计法和平均风速和最大风速估计等三种算法,并应用此算法计算了鄱阳气象站的风速威布尔分布参数。根据分布参数拟合了鄱阳县气象站的三种风速概率分布,将拟合的风速概率分布与同期的风速实际频率分布结果进行相关分析,依据相关系数判断拟合效果的好坏。通过比较得到了以下结论:平均风速和标准差估计法效果最好,累积分布函数拟合法次之,由于最大风速变化比较随机,平均风速和最大风速估计法效果波动最大,整体效果差。通过多年最大风速的平均数与平均风速计算,能减少最大风速抽样的随机性误差,结果更具代表性。     

风力发电系统动态仿真的风速模型

风力发电机组不同于火电等传统发电机组的最大之处在于其原动机功率的本质不可控,这是由风速的易变性和不可控性造成的。风速状况对风力发电系统的性能有着重要的影响,也使得风速模型成为风力发电系统仿真模型的重要部分。该文针对风速随机变化的特性,在风速统计特性研究的基础上,用自回归滑动平均(ARMA)方法建立了具有一定功率谱密度特性的风速模型。对该模型所模拟的风速序列进行了分析和验证,并与现有仿真程序中风速模型的结果进行了对比,结果表明该文提出的ARMA模型能够有效地产生动态分析和仿真所需的风速序列。