基于SMES/BESS混合储能抑制风电功率波动的控制策略

针对风能的随机性和波动性,风力发电系统易出现功率波动的问题,采用超导磁储能(SMES)和蓄电池(BESS)混合储能的方式来平抑功率波动,提出了一种改进型混合遗传算法的变参数荷电状态(SOC)分区控制优化策略。基于自适应学习的思想对算法进行了改进,使得算法的收敛速度和精确度得以提高。将储能系统荷电状态剩余量和荷电状态分区限值作为改进后混合遗传算法的目标函数和边界条件。所得目标结果作为滤波器滤波时间常数修正值对其进行修正,从而实现功率二次分配。在Matlab/Simulink中搭建仿真模型验证了该控制策略的有效性。所提控制策略可以对任意时刻SMES和BESS出力进行最优配合,同时能减小电池充放电深度和提高对风电功率波动的平抑效果,且能有效提高混合储能系统的使用寿命。     

考虑环境成本和需求响应的风电并网优化

在能源互联网的大背景下,传统的火力发电由于对环境的破坏需要清洁能源作为代替。风电因为其经济、环保得到了广泛的应用,而日益增长的风电接入电网给电力系统的经济、稳定运行带来严峻的挑战。文中从环境成本和需求响应两方面入手,综合考虑了风电的波动性、火电环境损失、可转移负荷对于系统的影响,及需求响应在提高风电消纳、降低环境污染、平抑负荷波动等方面的作用。为求解模型,文中提出一种粒子群算法惯性权重改进策略。算例表明考虑需求响应和环境成本可以有效降低成本和弃风现象。     

超导储能蓄电池混合储能在风力发电中的应用

风力发电输出功率的波动性导致其直接并网会对电网带来不良影响,需要电力储能装置来提高并网性能,而常用的单一电池储能由于受到充放电次数的限制而易损坏。在建立用于平滑风电功率波动的超导储能和电池储能的混合储能模型基础上,设计超导储能用于平抑高频尖峰功率,电池储能用于平抑低频波动功率,并给出了两种储能装置的功率和容量确定方法。算例的仿真结果表明该方法同单一电池储能相比,可以有效地平抑风场并网的功率波动并减小电池的功率等级,减少电池的充放电次数和放电深度,从而延长了电池使用寿命。     

基于功率波动过程的风电功率短期预测及误差修正

风资源因具有较强的波动性、随机性与间断性等特点而导致风电功率预测精度不高。为减小风电功率波动对电网的冲击,提高电力系统对风电的接受与消纳能力,提出了改进的风电功率短期预测方法与基于波动的误差修正方法。首先将风电功率按不同波动过程进行聚类划分,提取不同波动的特征曲线对功率值进行修正;采用引力搜索算法优化的反向传播神经网络(GSA-BP)作为基本预测方法进行预测;分析不同波动过程下的预测误差表现,建立预测误差与综合气象指标的映射关系。针对不同波动过程建立相应的风电功率误差修正模型,提出了线性模型和GSA-BP非线性模型相结合的方式对预测误差进行修正,最后以功率预测值叠加预测误差修正值作为最终预测结果。该风电功率预测误差修正方法不仅涉及风速风向等常规因素,而且考虑到了风电功率的波动性。     

基于BAS-IMOPSO算法的风电系统储能优化配置

风力发电作为一种清洁的可再生能源,在电力系统中应用广泛,但风电出力的随机波动性会对电力系统电能质量产生不利影响。储能系统(energy storage system, ESS)因其灵活的双向调节能力被用于平抑风电接入系统带来的电压与功率波动问题。文中考虑储能接入节点与容量不同对风电出力波动的平抑效果不同,以系统电压偏差、日有功网损和ESS配置容量为目标函数建立多目标储能优化配置模型。采用一种基于天牛须搜索算法的改进多目标粒子群优化(beetle antennae search-improved multi-objective particle swarm optimization, BAS-IMOPSO)算法求解该模型,利用基于信息熵的逼近理想解排序法在Pareto解集中选取最佳储能配置方案。在IEEE 33节点系统中对模型进行仿真验证,结果表明：BAS-IMOPSO算法优化下的ESS降低配电网电压偏差与有功网损的能力较多目标粒子群优化(multi-objective particle swarm optimization, MOPSO)算法显著提高,有效改善了系统的电能质量,具有削峰...     

基于EWT-FC方法的氢-超级电容混合储能功率分配

为平抑风电功率波动,弥补单一储能的不足,引入氢和超级电容构成的混合储能装置平滑风电功率波动。针对传统功率分配方法难以建模,容易出现模态混叠现象等问题,以及为了提高储能系统可靠性,提出了一种经验小波变换EWT（empirical wavelet transform）-模糊控制FC（fuzzy control）策略方法。首先,为了验证EWT算法能实现储能设备间功率精确分配,将EWT算法与经验模态分解EMD（empirical mode decomposition）和小波变换WT（wavelet transform）算法进行对比;其次,采用EWT算法分解重构风电原始功率得到并网功率和混合储能平抑功率;最后,考虑超级电容荷电状态SOC（state-of-charge）和储氢罐压力对系统安全稳定运行的影响,采用模糊控制修正储能元件的充放电功率。仿真结果表明, EWT算法避免了模态混叠,实现了储能设备间功率精确分配。该控制策略能有效平抑风电波动,保证了SOC和储氢罐压力在合理范围内,延长了设备寿命,实现系统稳定运行。     

Output power control of wind turbine generator by pitch angle control using minimum variance control

Effective utilization of renewable energies such as wind energy as a replacement for fossil fuels is highly desirable. Wind energy is not constant and wind generator output is proportional to the cube of the wind speed, which causes the power output of wind turbine generators (WTGs) to fluctuate. In order to reduce output power fluctuations of wind farms, this paper presents an output power leveling control strategy for wind farms based on both the mean and the standard deviation of wind farm output power, a cooperative control strategy for WTGs, and a pitch angle control method using a generalized predictive controller (GPC) intended for all operating regions of WTGs. Simulation results using an actual detailed model for wind farm systems show the effectiveness of the proposed method. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn, 154(2): 10–18, 2006; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20247     

Forecasting wind power in the Mai Liao Wind Farm based on the multi-layer perceptron artificial neural network model with improved simplified swarm optimization

Of the many kinds of renewable energy, wind power is low in cost and non-polluting, so it is especially well-suited to Taiwan. The Mai Liao Wind Farm is the most important wind farm in Taiwan, and forecasting the wind power output for national sustainable development continues to be a challenging research feature. In this study, we attempt to forecast the wind power data collected from the Mai Liao Wind Farm. Our forecast model is based on a Multi-Layer Perceptron Artificial Neural Network (MLP) model using the data collected at the Mai Liao Wind Farm over a period of five years from September 2002 to August 2007. We proposed a new algorithm, namely improved Simplified Swarm Optimization (iSSO), which improves Simplified Swarm Optimization (SSO) by justifying the weights and bias in training the MLP. The proposed iSSO combines Principal Component Analysis (PCA), Autocorrelation Function (AF) and Partial Autocorrelation Function (PAF) for the selection of features which increases the efficiency of the proposed model. The experimental results demonstrate that the performance of iSSO outperforms the other six most popular algorithms.     

含风电场电力系统潮流计算的交替求解方法

风电在电网能源中所占的比例越来越大,使电网的正常运行状况发生改变,潮流计算是分析风电并入电网后电网运行状况的一种基本方法。分析了异步发电机和双馈感应发电机的数学模型,提出了一种将主电网和风电场交替进行求解的潮流算法。主电网计算时,将风电场作为一个PQ节点处理,风电场潮流计算时以主电网计算结果中风电场出口母线电压为输入条件。采用华北某地的实际风电场替换IEEE9测试系统中3号发电机进行了仿真验证,结果表明,该算法可行。     

储能协助风电机组参与电网调频控制策略研究

风机通过电力电子设备连接至电网,当转子动能与系统频率解耦,无法为电网频率变化提供惯性支撑,随着系统中风电比例的增加,系统频率稳定受到严峻挑战。文中提出一种变系数综合惯性控制方法,风机能够根据频率的扰动灵活调节输出功率;在此基础上,提出结合桨距角备用控制协同调频方法,通过对风速的分段处理,使风电机组参与电网调频具有针对性;为进一步优化风电机组调频性能,风电并网系统增加了储能装置,通过对风储系统惯性进行详细分析,提出了一种风储系统联合调频控制策略,采用模糊控制策略对中高风速区间风储出力分配制定相应的规则,实时调节储能出力系数。最后对风储调频策略进行仿真验证,结果表明,所提方法能有效改善风电机组调频效果,保证高比例风电并网的频率稳定。     

抑制电压波动的风电机组自抗扰控制仿真研究

由于风具有随机性和间歇性等特点,风力机的输出功率变化很大.而风力发电引起电压波动和闪变的根本原因是并网风电机组输出功率的波动.为提高风电机组控制系统的鲁棒性,本文将自抗扰控制技术(ADRC)引入到风电机组转矩控制中,将转矩尖波信号归到未知扰动中,在风速突变的情况下保持输出转矩稳定.采用非线性状态误差反馈控制率(NLSE...     

Identifying the optimum wind capacity for a power system with interconnection lines

This paper presents a general framework for identifying the optimum wind capacity to be integrated in a power system with interconnection lines. Wind generation is treated as negative demand, together with the load demand, which must be covered by the conventional power generation and the energy interchange with neighboring systems. Under the presumption that the time-varying wind and load together with the trading price with neighboring systems could be statistically modeled by specified probability distributions, a chance constrained programming (CCP) model is formulated to address the wind capacity planning problem. The objective is to maximize the yearly net profit of the entire power system subject to the operational constraints and the reliability requirement. Wind power curtailment and load shedding are allowed to improve the system flexibility but incur penalty costs. The optimization problem is solved using a hybrid intelligence algorithm incorporating Monte Carlo simulations, a neural network and a genetic algorithm. The feasibility of the proposed approach is verified by a case study on a given power system.     

用于直驱式风力发电的复合储能系统

风力发电输出功率具有波动性和间歇性的特点,这将对电网的电能质量产生较大的影响.文中提出采用超级电容器和蓄电池复合储能系统来实现快速吞吐有功功率,平抑风电的功率波动,并发挥超级电容器和蓄电池在技术性能上所具有的互补优势,提高系统的经济性能和技术性能;建立了复合储能系统的数学模型,以及基于复合储能的直驱式风力发电系统的仿真...     

含风电的电力系统鲁棒优化调度

风力发电是目前较为成熟的一种可再生能源发电技术,其出力具有随机性和间歇性,电力部门很难对其进行准确的预测。风电的不确定性给电力系统的经济调度带来重大挑战,如何最大化利用风电资源以及减小风电波动对系统供电侧的影响,是经济调度常需要解决的问题。文中建立了含风电且以最低发电成本为目标的鲁棒优化调度模型,该模型考虑机组的自动发电控制(Automatic Generation Control,AGC)响应来应对不确定性出力波动,维持系统功率平衡。然后模拟实际场景,分析该鲁棒调度方法的安全性和经济性。     

Energy storage system-based power control for grid-connected wind power farm

Wind power is characterized as intermittent with stochastic fluctuations, which can result in deviation of grid frequency and voltage when the wind power ratio is high enough. These effects have a definite impact on stability and power quality of grid operation. This paper proposes an energy storage system (ESS) based power control for a grid-connected wind power system to improve power quality and stability of the power system. Vanadium redox flow battery (VRB), as an environmentally-friendly battery provided with many advantages, is employed in the ESS. A dynamic mathematical model of VRB is built by using an equivalent circuit, and its charging and discharging characteristics are analyzed. The VRB’s stable voltage is available in a wide range (around 20–80% state of charge), which is suitable for utilization of a single-stage AC/DC converter in the VRB-based ESS. With a proposed energy storage control method, VRB-based ESS is added at the exit of the grid-connected wind farm to filter fluctuations of wind power, which ensures that smooth power will be injected into the grid and which improves power quality of the power system. Simulations and experiments are carried out to verify the proposed power control method for these grid-connected wind power systems. The grid-connected wind farm with VRB-based ESS, wind speed (characterized as gust and stochastic wind), and wind turbines are modeled in simulations. Simulation results show that the grid-injected active power from the wind farm is effectively smoothed, and reactive power support can be provided for the grid by the designed VRB-based ESS. Experimental verification is achieved with a low power bench, where a RT-LAB real-time simulation platform and a direct torque controlled induction motor simulate a real wind turbine and a wind speed model is built in the RT-LAB real-time simulation platform. The experimental results verify the proposed scheme through demonstrating a stable and smooth power flow injected into the grid though the wind power fluctuated.     

风电机组输出功率平滑技术综述

风能具有高度不可控性的特点,风能的随机变化会直接导致风力发电机组的输出功率波动,波动的功率并网会引起电网频率波动及电压闪烁,甚至危及电网安全。为了满足风电机组并网要求,提高并网电能质量,风电输出功率平滑技术便成为急需解决的问题。文中介绍了直接功率控制和间接功率控制这两类功率平滑控制策略,阐述了两类中包含具体方法的原理,介绍了各种方法的研究现状,继而总结、评价了各方法的优缺点、适用前景等,最后指出了当前各类功率平滑控制技术的优化方向。     

采用NSGA-Ⅱ混合智能算法的风电场多目标电网规划

风电并网在实现节约化石能源和减少有害气体排放等效益的同时,也将对电力系统的可靠性造成一定的负面影响。为达到投资经济性、系统可靠性、环保效果的整体最优,构建了多目标风电场接入的输电线路与电网的联合优化规划模型;针对目标权重未知、人工神经网络(artificial neural network,ANN)收敛困难、无法合理决策等问题,采用方差最大化决策和分类逼近理想解的排序方法(technique for order preference by similarity to an ideal solution,TOPSIS)缩小最优解的范围,并在此基础上提出了随机模拟、神经元网络和非劣排序遗传算法Ⅱ(non-dominated sorting genetic algorithm Ⅱ,NSGA-Ⅱ)相结合的混合智能算法;对增加风电场的改进IEEE Garver-6系统进行计算分析,结果表明该方法具有较高的决策效率和计算精度,从而验证了所提出模型和方法的合理性和有效性。     

A study of the flywheel capacity of the power compensator for the wind turbine generator

Nowadays, the Wind Turbine Generator (WTG) system is remarkably increasing as a renewable energy resource in Japan. But in the WTG, the power fluctuations caused by the wind speed fluctuation are the most crucial issue in providing their energy to the utility. It was found that a power compensation system (PCS) of suitable capacity is not yet available. In this paper, the time range and standardized wind speed including fluctuations are discussed. We estimated the storage capacity of PCS at different wind profiles by standardization of wind speed and verified the validity of their capacities using a computer simulation. In this way we determined the suitable capacity of the power compensation system. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 143(4): 64–71, 2003; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10122     

基于MMC-BESS的风电场功率及电压综合调节策略

风电并网存在功率及公共耦合点(Point of Common Coupling, PCC)电压波动问题,常规方法为采用储能装置与无功补偿装置分别进行调节,但会导致系统结构复杂。文中研究了一种基于模块化多电平变换器拓扑的电池储能系统(Modular Multilevel Converter Based Battery Energy Storage System, MMC-BESS),可对风电场功率及电压进行综合调节。由于风速的随机性导致风电场输出功率波动,提出将风电场有功功率输入到一阶低通滤波器中,滤波器输出与输入的差作为MMC-BESS有功参考信号,以平滑风电场并网功率。由于风电场负荷突变导致PCC电压波动,提出将PCC电压的d轴分量与额定值作差,通过PI调节生成无功参考电流,实现PCC电压调节,在模拟风电场场景下进行实验,实验结果验证了风电场中MMC-BESS四象限功率补偿能力。     

基于改进多目标粒子群算法的含风电场电力系统优化调度

由于风电具有随机性和波动性的特点,常规电力系统分析、调度及控制方式受到了新的挑战。深入研究了含风电场的电力系统优化调度问题,建立了基于多目标粒子群算法的调度模型,在该模型中考虑发电成本、污染气体排放量及风电场输出功率短期波动引起的系统运行风险,在约束条件中加入了正负旋转备用容量,从而减小了风电波动和预测误差对系统的影响程度。算法设计上,通过引入遗传算子对多目标粒子群算法搜索机组组合的能力进行改进,提高了该模型的全局寻优能力。10机系统算例结果分析表明,所提方法正确有效,且能够减少寻优过程中不可行解、解决各优化目标之间的冲突性,使所有目标函数尽可能达到最优。