基于飞轮储能的并网风电场有功功率及频率控制方法研究

为平缓风电场输出,实现大规模风电并网并参与系统的频率控制,设计了一个基于永磁同步电机的飞轮储能系统,利用飞轮储能系统的快速充放电能力实现了风电场并网有功功率与一次频率控制.研究了飞轮储能系统的参考功率计算方法,根据该参考功率的变化实现储能系统充放电运行状态的转化.在Matlab/Simulink中建立了该储能系统模型,进行了风电场并网的有功功率及频率控制仿真,仿真结果表明该方法可以有效地平缓风电场的有功功率输出,参与系统的一次调频.     

基于函数系数自回归模型的风速时间序列预测

准确预测风电场风速是解决风能对电力系统所造成的安全、稳定运行和电能质量等问题的有效途径之一.风速的难以预测是由于它的高度随机和非线性.基于一种非参数的非线性自回归随机模型来预测风速,模型的自回归系数随模型依赖变量的变化而变化,因而它有灵活的非线性结构.数值实验和比较结果表明了这种函数系数自回归模型在风电场风速预测中的有效性.     

服务型制造模式下的风电场维护服务调度及服务成本研究

针对风电设备制造企业向服务型制造转型的问题,提出风电设备制造企业联合组建风电场维修服务基地,为风电场提供专业维修服务。在考虑了维修时间、运输时间、服务能力约束下,构建了数学模型优化维修队的调度方式,使维修成本最小。用遗传算法求解该模型,提出了基于风电场和维修队的混合非负整数分段编码方式,避免了遗传操作过程中非法解的产生,并用MATLAB编程进行实例求解,得到了满意的结果。     

基于信度模型的超短期风电预测

信度理论是利用信度因子来保证调整后的保险费接近于真实的风险水平的一种工具.超短期风速预测可以为风电在线优化调度,储能系统实时抑制风电波动和电力系统EMS实时优化运行等方面提供决策依据,而信度模型具有能对数据量较小的样本建模,并且能够一次性预测该电厂所有风机的未来风速的特点.因此考虑将信度模型应用于预测未来超短期内的风速,并且采用系统聚类法对风机进行合理的分类,以此提高预测精度.     

风电场运行状况分析及优化

假设风速服从威布尔分布。1)从平均风速、平均风功率密度、年有效风小时数、理论发电量这4个指标对风电场风能资源进行了评估,得出该风电场风能资源属于"丰富区",但发电效率只有19.3%。2)一期和二期风机的平均容量系数在0.19~0.28之间,说明风机与风频匹配程度较差,若选用机型Ⅲ更好。3)通过建立随机优化模型和多目标规划模型,构建了风电场所有风机每年2次的维护方案和维修人员的值班方案,该方案较好地体现了发电经济性和值班均衡性。     

系统安全运行约束下的动态环境经济调度

文章研究风电入网情况下系统安全运行的动态环境经济调度问题.基于风电预测误差,权衡效益和系统运行风险,建立了购买可中断负荷(interruptible load,IL)的经济调度优化模型.新模型的目标函数考虑了传统火电机组能耗成本、环境成本、阀点效应成本和可中断负荷补偿成本.系统运行约束上,采用条件风险价值(conditional value-at-risk,CVaR)刻画因风电间歇性和随机性导致的系统不安全运行风险,结合火电机组出力和多时间段爬坡限制建立了系统的安全运行约束.模型的求解上,采用罚函数方法、光滑化技术和样本平均方法相结合,提出了一类新的随机优化算法;IEEE-30节点系统测试了模型和算法的有效性.     

基于瑞利分布的大型风电场风机维护方案优化

针对大型风电场风能利用率低下的问题,从优化风机维护方案的角度出发,在假设风速服从瑞利分布的前提下,建立了两阶段多目标0-1规划模型,确定了所有风机每年2次的维护方案,方案以风电场风能利用率最大为目标,兼顾了维修人员的值班任务和维护任务的均衡,具有操作简单、可行性强的优势.     

考虑输电约束下区域电网风储能联合调度鲁棒优化模型

随着我国可再生能源大规模的建设,引入到电力系统中,对能源改革及节能减排具有重要意义.构建了含有风电、储能设备以及火电机组10机组30节点区域电网系统中,利用鲁棒优化方法,将风电出力作为随机变量.以发电系统中的发电成本、排污成本及风电惩罚成本作为目标函数,运用强化学习算法对目标函数求解.通过对算例模拟得到,分时电价与储能设备引入电网系统,可有效的平抑风电出力随机性、波动性,并降低区域电网的发电成本及排污成本.     

基于空间统计模型的区域风电场效率影响因素挖掘

影响风电场效率的因素众多,导致同一区域风电场效率存在较大差异.为了探究影响风电场效率的关键因素,以消纳风险作为风电场效率评价指标,对蒙西地区风电场消纳风险进行测度,结合各风电场消纳风险值进行空间相关分析并制作Moran散点图,Moran散点图将样本主要分为HH、HL、LL、LH四类,进而分析各个因素对消纳风险的影响程度.研究结果表明,虽然蒙西地区整体消纳风险呈下降趋势,但高消纳风险风场集聚且HH类风场整体消纳风险呈上升趋势;资源禀赋、接入电压等级以及电力市场交易规模是蒙西地区风电场效率的主要影响因素.本文提出的方法能够直观反映区域各个风电场效率及关键影响因素,可为区域风电产业发展及规划提供参考.     

PMSG风电系统最大风能捕获自适应模糊滑模控制器设计

在分析了永磁同步发电机数学模型的基础上,以实现额定风速下风能的最大捕获为目标,采用滑模控制和模糊控制相结合的方法,提出了一种新的自适应模糊切换控制方法。该方法使得风电系统在额定风速以下时,能够根据风速的变化实时地改变电机转速,从而获得最佳叶尖速比,实现最大功率追踪的目标。仿真结果表明,自适应模糊切换控制方法可以有效实现额定风速下的最大风能捕获,同时弥补非线性给系统带来的影响,提高了系统的鲁棒性和稳定性。     

Scheduling electric power production at a wind farm

We present a model for scheduling power generation at a wind farm, and introduce a particle swarm optimization algorithm with a small world network structure to solve the model. The solution generated by the algorithm defines the operational status of wind turbines for a scheduling horizon selected by a decision maker. Different operational scenarios are constructed based on time series data of electricity price, grid demand, and wind speed. The computational results provide insights into management of a wind farm.     

A dynamic wind farm aggregate model for the simulation of power fluctuations due to wind turbulence

An important aspect related to wind energy integration into the electrical power system is the fluctuation of the generated power due to the stochastic variations of the wind speed across the area where wind turbines are installed. Simulation models are useful tools to evaluate the impact of the wind power on the power system stability and on the power quality. Aggregate models reduce the simulation time required by detailed dynamic models of multiturbine systems.In this paper, a new behavioral model representing the aggregate contribution of several variable-speed-pitch-controlled wind turbines is introduced. It is particularly suitable for the simulation of short term power fluctuations due to wind turbulence, where steady-state models are not applicable.The model relies on the output rescaling of a single turbine dynamic model. The single turbine output is divided into its steady state and dynamic components, which are then multiplied by different scaling factors. The smoothing effect due to wind incoherence at different locations inside a wind farm is taken into account by filtering the steady state power curve by means of a Gaussian filter as well as applying a proper damping on the dynamic part.The model has been developed to be one of the building-blocks of a model of a large electrical system, therefore a significant reduction of simulation time has been pursued. Comparison against a full model obtained by repeating a detailed single turbine model, shows that a proper trade-off between accuracy and computational speed has been achieved.     

农村小型风力发电机的控制与并网仿真

设计了双馈风力发电机组的定子磁链矢量控制系统,构造了容量为9MW的风力发电系统;通过MATLAB软件,分别在变风速和恒定风速两种情况时,对风力发电系统并网进行仿真.仿真结果表明,利用矢量变换技术的控制方法,风力发电系统可以实现有功功率和无功功率的快速解耦并且动态响应快,跟随性能好.     

Toward net-zero carbon manufacturing operations: an onsite renewables solution

A growing number of manufacturing firms are striving to achieve eco-friendly operations through onsite wind or solar generation. This paper proposes a zero-carbon power supply model to guide the integration of onsite renewable energy into manufacturing facilities. We intend to address two fundamental questions: (1) Is it cost-effective to deploy onsite wind turbines and solar photovoltaics (PVs) systems to achieve net-zero carbon environmental performance? (2) Is the renewable generation system able to meet the electricity demand despite the power intermittency? To answer these questions, we formulate a stochastic optimization model to minimize the levelized cost of onsite renewable energy. The goal is achieved by optimizing the sizing of wind and solar generating units. The proposed energy solution is tested in ten cities around the world under diverse climatic conditions. While PV is still expensive, we conclude that manufacturers could realize zero-carbon emissions at affordable cost provided the local wind speed is above 5 m/s.     

Multi-objective algorithm for the design of prediction intervals for wind power forecasting model

A composite forecasting framework is designed and implemented successfully to estimate the prediction intervals of wind speed time series simultaneously through machine learning method embedding a newly proposed optimization method (multi-objective salp swarm algorithm). In this study, data pre-process strategy based on feature extraction is served for reducing the fluctuations of wind power generation and select appropriate input forms of wind speed datasets for the sake of improving the overall performance. Besides, fuzzy set theory selection technique is used to determine the best compromise solutions from Pareto front set deriving from the optimization phase. To test the effectiveness of the proposed composite forecasting framework, several case studies based on different time-scale wind speed datasets are conducted. The corresponding results present that the proposed framework significantly outperforms other benchmark methods, and it can provide very satisfactory results in both goals between high coverage and small width.     

Short-term prediction of wind power using EMD and chaotic theory

Due to the strong non-linear, complexity and non-stationary characteristics of wind farm power, a hybrid prediction model with empirical mode decomposition (EMD), chaotic theory, and grey theory is constructed. The EMD is used to decompose the wind farm power into several intrinsic mode function (IMF) components and one residual component. The grey forecasting model is used to predict the residual component. For the IMF components, identify their characteristics, if it is chaotic time series use largest Lyapunov exponent prediction method to predict. If not, use grey forecasting model to predict. Prediction results of residual component and all IMF components are aggregated to produce the ultimate predicted result for wind farm power. The ultimate predicted result shows that the proposed method has good prediction accuracy, can be used for short-term prediction of wind farm power.     

Wind energy prediction using a two-hidden layer neural network

The power generated by wind turbines changes rapidly because of the continuous fluctuation of wind speed and air density. As a consequence, it can be important to predict the energy production, starting from some basic input parameters. The aim of this paper is to show that a two-hidden layer neural network can represent a useful tool to carefully predict the wind energy output. By using proper experimental data (collected from three wind farm in Southern Italy) in combination with a back propagation learning algorithm, a suitable neural architecture is found, characterized by the hyperbolic tangent transfer function in the first hidden layer and the logarithmic sigmoid transfer function in the second hidden layer. Simulation results are reported, showing that the estimated wind energy values (predicted by the proposed network) are in good agreement with the experimental measured values.     

A stochastic model for the daily coordination of pumped storage hydro plants and wind power plants

We propose a stochastic model for the daily operation scheduling of a generation system including pumped storage hydro plants and wind power plants, where the uncertainty is represented by the hourly wind power production. In order to assess the value of the stochastic modeling, we discuss two case studies: in the former the scenario tree is built so as to include both low and high wind power production scenarios, in the latter the scenario tree is built on historical wind speed data covering a time span of one and a half year. The Value of the Stochastic Solution, computed by a modified new procedure, shows that in scenarios with low wind power production the stochastic solution allows the producer to obtain a profit which is greater than the one associated to the deterministic solution. In-sample stability of the optimal function values for increasing number of scenarios is reported.     

Weekly self-scheduling,forward contracting,and pool involvement for an electricity producer. An adaptive robust optimization approach

This paper addresses the optimization under uncertainty of the self-scheduling, forward contracting, and pool involvement of an electricity producer operating a mixed power generation station, which combines thermal, hydro and wind sources, and uses a two stage adaptive robust optimization approach. In this problem the wind power production and the electricity pool price are considered to be uncertain, and are described by uncertainty convex sets. To solve this problem, two variants of a constraint generation algorithm are proposed, and their application and characteristics discussed. Both algorithms are used to solve two case studies based on two producers, each operating equivalent generation units, differing only in the thermal units’ characteristics. Their market strategies are investigated for three different scenarios, corresponding to as many instances of electricity price forecasts. The effect of the producers’ approach, whether conservative or more risk prone, is also investigated by solving each instance for multiple values of the so-called budget parameter. It was possible to conclude that this parameter influences markedly the producers’ strategy, in terms of scheduling, profit, forward contracting, and pool involvement. These findings are presented and analyzed in detail, and an attempted rationale is proposed to explain the less intuitive outcomes. Regarding the computational results, these show that for some instances, the two variants of the algorithms have a similar performance, while for a particular subset of them one variant has a clear superiority.     

Spatial dependencies of wind power and interrelations with spot price dynamics

Wind power has seen strong growth over the last decade and increasingly affects electricity spot prices. In particular, prices are more volatile due to the stochastic nature of wind, such that more generation of wind energy yields lower prices. Therefore, it is important to assess the value of wind power at different locations not only for an investor but for the electricity system as a whole. In this paper, we develop a stochastic simulation model that captures the full spatial dependence structure of wind power by using copulas, incorporated into a supply and demand based model for the electricity spot price. This model is calibrated with German data. We find that the specific location of a turbine – i.e., its spatial dependence with respect to the aggregated wind power in the system – is of high relevance for its value. Many of the locations analyzed show an upper tail dependence that adversely impacts the market value. Therefore, a model that assumes a linear dependence structure would systematically overestimate the market value of wind power in many cases. This effect becomes more important for increasing levels of wind power penetration and may render the large-scale integration into markets more difficult.