Adjustment of wind farm power output through flexible turbine operation using wind farm control

When the installed capacity of wind power becomes high, the power generated by wind farms can no longer simply be that dictated by the wind speed. With sufficiently high penetration, it will be necessary for wind farms to provide assistance with supply‐demand matching. The work presented here introduces a wind farm controller that regulates the power generated by the wind farm to match the grid requirements by causing the power generated by each turbine to be adjusted. Further, benefits include fast response to reach the wind farm power demanded, flexibility, little fluctuation in the wind farm power output and provision of synthetic inertia. Copyright © 2015 John Wiley & Sons, Ltd.     

风电场微观选址及发电量预测的探讨

对风资源评估、选址地面情况和风机位置的排布等影响风电场微观选址的因素进行了分析.阐述了风电场发电量的预测方法,通过实例说明如何使用相关软件来预测风电场发电量,并根据预测结果对风电场微观选址注意事项进行了探讨.     

Optimization‐based power management of a wind farm with battery storage

This paper presents an optimization‐based control strategy for the power management of a wind farm with battery storage. The strategy seeks to minimize the error between the power delivered by the wind farm with battery storage and the power demand from an operator. In addition, the strategy attempts to maximize battery life. The control strategy has two main stages. The first stage produces a family of control solutions that minimize the power error subject to the battery constraints over an optimization horizon. These solutions are parameterized by a given value for the state of charge at the end of the optimization horizon. The second stage screens the family of control solutions to select one attaining an optimal balance between power error and battery life. The battery life model used in this stage is a weighted Amp‐hour throughput model. The control strategy is modular, allowing for more sophisticated optimization models in the first stage or more elaborate battery life models in the second stage. The strategy is implemented in real time in the framework of model predictive control. Copyright © 2012 John Wiley & Sons, Ltd.     

Contributions to wind farm power estimation considering wind direction‐dependent wake effects

This paper presents a contribution to wind farm ouput power estimation. The calculation for a single wind turbine involves the use of the power coefficient or, more directly, the power curve data sheet. Thus, if the wind speed value is given, a simple calculation or search in the data sheet will provide the generated power as a result. However, a wind farm generally comprises more than one wind turbine, which means the estimation of power generated by the wind farm as a function of the wind speed is a more complex process that depends on several factors, including the important issue of wind direction. While the concept of a wind turbine power curve for a single wind turbine is clear, it is more subject to discussion when applied to a whole wind farm. This paper provides a simplified method for the estimation of wind farm power, based on the use of an equivalent wake effect coefficient. Copyright © 2016 John Wiley & Sons, Ltd.     

Variable‐sized wind turbines are a possibility for wind farm optimization

The potential benefits associated with harnessing available momentum and reducing turbulence levels in a wind farm composed of wind turbines of alternating size are investigated through wind tunnel experiments. A variable size turbine array composed of 3 by 8 model wind turbines is placed in a boundary layer flow developed over both a smooth and rough surfaces under neutrally stratified thermal conditions. Cross‐wire anemometry is used to capture high resolution and simultaneous measurements of the streamwise and vertical velocity components at various locations along the central plane of the wind farm. A laser tachometer is employed to obtain the instantaneous angular velocity of various turbines. The results suggest that wind turbine size heterogeneity in a wind farm introduces distinctive flow interactions not possible in its homogeneous counterpart. In particular, reduced levels of turbulence around the wind turbine rotors may have positive effects on turbulent loading. The turbines also appear to perform quite uniformly along the entire wind farm, whereas surface roughness impacts the velocity recovery and the spectral content of the turbulent flow within the wind farm. Copyright © 2013 John Wiley & Sons, Ltd.     

Investigation on the integral output power model of a large-scale wind farm

The integral output power model of a large-scale wind farm is needed when estimating the wind farm’s output over a period of time in the future. The actual wind speed power model and calculation method of a wind farm made up of many wind turbine units are discussed. After analyzing the incoming wind flow characteristics and their energy distributions, and after considering the multi-effects among the wind turbine units and certain assumptions, the incoming wind flow model of multi-units is built. The calculation algorithms and steps of the integral output power model of a large-scale wind farm are provided. Finally, an actual power output of the wind farm is calculated and analyzed by using the practical measurement wind speed data. The characteristics of a large-scale wind farm are also discussed.     

Output power dispatch control for a wind farm in a small power system

Nowadays, a wind turbine generator (WTG) is required to provide control capabilities as the output power of WTG fluctuates. Under this scenario, this paper proposes an output power control method of a wind farm (WF) connected to a small power system using pitch angle control. In this control approach, the WF output power control is achieved by two control levels: central and local. In the central control, the WF output power command is determined by considering the frequency deviations and wind speeds using a fuzzy function. Then, the local output power commands for each of the WTGs are based on the proposed dispatch control. In the proposed dispatch control, the output commands of each WTG are determined by considering wind conditions for each of the WTGs. The simulation results by using an actual detailed model for the wind power system show the effectiveness of the proposed method. Copyright © 2010 John Wiley & Sons, Ltd.     

The effect of timescales on wind farm power variability with nonlinear model predictive control

Model predictive control techniques enable operators to balance multiple objectives in large wind farms, but the controller design depends on modeling effects that propagate at different timescales. This paper uses nonlinear model predictive control to investigate how wind farm power variability can be reduced both by varying ratios of three timescales impacting the system control and by inclusion of a power variability minimization measure in the controller objective function. Tests were conducted to assess how different timescale ratios affect the average farm power and power variability. Power variability measures are shown to be sensitive to the ratio of the incident wind period and the turbine time delay, particularly for cases with dominant incident wind frequencies. The average farm power increases in a series of steps as the controller time horizon increases, which corresponds to time horizon values required for wakes disturbances to propagate to downstream turbines. A second set of tests was conducted in which various measures of power variability were incorporated into the controller objective function and shown to yield significant reductions in farm power variability without significant reductions in farm power output. The controller was found to utilize two different approaches for achieving power variability reduction depending on the formulation of the controller objective function. These results have important implications for the design and operation of wind power plants, including the importance of considering the frequency components of wind during turbine siting and the potential to reduce power variability through the use of farm‐level coordinated control. Copyright © 2017 John Wiley & Sons, Ltd.     

基于风电机组无功裕度预测的风电场无功分层控制策略

针对风电电压波动的问题,文章基于风电机组无功裕度预测,提出了一种风电场无功分层控制策略.该策略首先以并网点电压偏差和线路有功损耗最小为目标,使用二次规划算法在线实时求解最优并网电压,进而求解风电场无功参考值;其次,采用EWT-LSSVM预测算法进行风电功率预测,并提出预测功率校正方法实时修正预测功率,精确求解风电机组的...     

Modelling and measurements of power losses and turbulence intensity in wind turbine wakes at Middelgrunden offshore wind farm

Understanding of power losses and turbulence increase due to wind turbine wake interactions in large offshore wind farms is crucial to optimizing wind farm design. Power losses and turbulence increase due to wakes are quantified based on observations from Middelgrunden and state‐of‐the‐art models. Observed power losses due solely to wakes are approximately 10% on average. These are relatively high for a single line of wind turbines due in part to the close spacing of the wind farm. The wind farm model Wind Analysis and Application Program (WAsP) is shown to capture wake losses despite operating beyond its specifications for turbine spacing. The paper describes two methods of estimating turbulence intensity: one based on the mean and standard deviation (SD) of wind speed from the nacelle anemometer, the other from mean power output and its SD. Observations from the nacelle anemometer indicate turbulence intensity which is around 9% higher in absolute terms than those derived from the power measurements. For comparison, turbulence intensity is also derived from wind speed and SD from a meteorological mast at the same site prior to wind farm construction. Despite differences in the measurement height and period, overall agreement is better between the turbulence intensity derived from power measurements and the meteorological mast than with those derived from data from the nacelle anemometers. The turbulence in wind farm model indicates turbulence increase of the order 20% in absolute terms for flow directly along the row which is in good agreement with the observations. Copyright © 2007 John Wiley & Sons, Ltd.     

Grid support of a wind farm with active stall wind turbines and AC grid connection

One of the main concerns in the grid integration of large wind farms is their ability to behave as active controllable components in the power system. This article presents the design of a new integrated power control system for a wind farm made up exclusively of active stall wind turbines with AC grid connection. The designed control system has the task of enabling such a wind farm to provide the best grid support. It is based on two control levels: a supervisory control level, which controls the power production of the whole farm by sending out reference signals to each individual wind turbine, and a local control level, which ensures that the reference power signals at the wind turbine level are reached. The ability of active stall wind farms with AC grid connection to control the power production to the reference power ordered by the operators is assessed and discussed by means of simulations. Copyright © 2005 John Wiley & Sons, Ltd.     

Cuckoo search for wind farm optimization with auxiliary infrastructure

This paper focuses on the optimization problem of a wind farm layout. This area of research is currently receiving widespread attention, as optimal positioning of the turbines promotes the financial viability of the wind farm and enhances the competitiveness of wind projects in the energy market. In this work, cuckoo search (CS), a modern population‐based metaheuristic optimization algorithm, is used. The objective is to find the turbine layout and types that maximize the net present value of the wind farm, while constraints on the turbine positions have to be met. The following constraints are considered: Firstly, the minimum distance between turbines for safe operation; secondly, a realistic wind farm shape including forbidden zones for installation and the existing infrastructure. Furthermore, the optimization of the wind farm includes an algorithm to find the least expensive layout of the wind farm roads and the electrical collector system. The algorithm is based on Dijkstra's shortest path and Prim's minimum spanning tree algorithms. The test results indicate that the infrastructure cost has a significant effect on the optimum wind farm solution. A genetic algorithm, commonly applied to wind farm micro‐siting problems, is used to benchmark the performance of the CS. The results show that the CS is capable of consistently finding better solutions than the genetic algorithm.     

Time series modelling of power output for large‐scale wind fleets

Simulations of power systems with high wind penetration need to represent the stochastic output of the wind farms. Many studies use historic wind data directly in the simulation. However, even if historic data are used to drive the realized wind output in scheduling simulations, a model of the wind's statistical properties may be needed to inform the commitment decisions for the dispatchable units. There are very few published studies that fit models to the power output of nation‐sized wind fleets rather than the output at a single location. We fitted a time series model to hourly, time‐averaged, aggregated wind power data from New Zealand, Denmark and Germany, based on univariate, second‐order autoregressive drivers. Our model is designed to reproduce the asymptotic distribution of power output, the diurnal variation and the volatility of power output over timescales up to several hours. For the cases examined here, it was also found to provide a generally good representation of the overall distribution of power output changes and the variation of volatility with power output level, as well as an acceptable representation of the distribution of calm periods. Copyright © 2011 John Wiley & Sons, Ltd.     

Technoeconomic study for the optimization of turbine size and wind farm layouts

A technoeconomic analysis and optimization of wind turbine size and layout are performed using WAsP software. A case study of a 100‐MW wind farm located in Egypt is considered. Wind atlas for Egypt was used as the input data of the WAsP software. Two turbine models of powers 52 and 80 MW are considered for this project. The wind turbine size and distributions are selected based on the technoeconomic optimization, namely minimum wake effect, maximum annual energy production (AEP) rate, optimum cash flow, and payback period. The future worth method is adopted in economic comparison between the two alternatives, and the cash flow diagram provided the payback period and future worth after the lifetime of the plant. The results showed that (1) the AEP dramatically decreases for a wind farm area less than 15 km²; (2) the turbine spacing, spacing‐to‐diameter ratio, and the setback distances decrease and the wind turbine density and wake losses increase with decreasing the wind turbines size; (3) the total net AEP using G52 is lower than that of using G80 by about 16%; (4) the technoeconomic analysis recommended using G80 as it has higher profit than those of G52 by about $20 million.     

Large‐eddy simulations with extremum‐seeking control for individual wind turbine power optimization

Large‐eddy simulations of the flow past an array of three aligned turbines have been performed. The study is focused on below rated (Region 2) wind speeds. The turbines are controlled through the generator torque gain, as usually done in Region 2. Two operating strategies are considered: (i) preset individual optimum torque gain based on a model for the power coefficient (baseline case) and (ii) real‐time optimization of torque gain for maximizing each individual turbine power capture during operation. The real‐time optimization is carried out through a model‐free approach, namely, extremum‐seeking control. It is shown that ESC is capable of increasing the power production of the array by 6.5% relative to the baseline case. The extremum‐seeking control reduces the torque gain of the downstream turbines, thus increasing the angular speed of the blades. This results in improved aerodynamics near the tip of the blade that is the portion contributing mostly to the torque and power. In addition, an increase in angular speed leads to a larger entrainment in the wake, which also contributes to provide additional available power downstream. It is also shown that the tip speed ratio may not be a reliable performance indicator when the turbines are in waked conditions. This may be a concern when using optimal parameter settings, determined from isolated turbine models, in applications with waked turbines. Copyright © 2017 John Wiley & Sons, Ltd.     

TOPFARM: Multi‐fidelity optimization of wind farms

A wind farm layout optimization framework based on a multi‐fidelity optimization approach is applied to the offshore test case of Middelgrunden, Denmark as well as to the onshore test case of Stag Holt – Coldham wind farm, UK. While aesthetic considerations have heavily influenced the famous curved design of the Middelgrunden wind farm, this work focuses on demonstrating a method that optimizes the profit of wind farms over their lifetime based on a balance of the energy production income, the electrical grid costs, the foundations cost, and the cost of wake turbulence induced fatigue degradation of different wind turbine components. A multi‐fidelity concept is adapted, which uses cost function models of increasing complexity (and decreasing speed) to accelerate the convergence to an optimum solution. In the EU‐FP6 TOPFARM project, three levels of complexity are considered. The first level uses a simple stationary wind farm wake model to estimate the Annual Energy Production (AEP), a foundations cost model depending on the water depth and an electrical grid cost function dictated by cable length. The second level calculates the AEP and adds a wake‐induced fatigue degradation cost function on the basis of the interpolation in a database of simulations performed for various wind speeds and wake setups with the aero‐elastic code HAWC2 and the dynamic wake meandering model. The third level, not considered in this present paper, includes directly the HAWC2 and the dynamic wake meandering model in the optimization loop in order to estimate both the fatigue costs and the AEP. The novelty of this work is the implementation of the multi‐fidelity approach in the context of wind farm optimization, the inclusion of the fatigue degradation costs in the optimization framework, and its application on the optimal performance as seen through an economical perspective. Copyright © 2013 John Wiley & Sons, Ltd.     

电网故障条件下风储联合系统无功自适应控制

针对电网三相对称故障条件下风电场电压不稳定的问题,文章提出了一种基于神经元的风储联合系统无功功率自适应控制策略,该策略以风储联合系统公共耦合点(Point of Common Coupling,PCC)的电压和电流为控制器的输入,采用Hebb学习算法作为自适应律,以获得准确的无功补偿。通过动态调整控制器的参数,使储能系统协调风电达到自适应输出无功功率的效果,提高系统在电网故障下的电压稳定性和风电故障穿越能力。最后,利用Matlab/Simulink仿真验证了该控制策略的有效性和正确性,与常规PI控制策略相比,文章所提出的控制策略可使风储系统迅速提供无功功率,PCC点的电压得到明显上升。     

Fatigue distribution optimization for offshore wind farms using intelligent agent control

A novel control approach is proposed to optimize the fatigue distribution of wind turbines in a large‐scale offshore wind farm on the basis of an intelligent agent theory. In this approach, each wind turbine is considered to be an intelligent agent. The turbine at the farm boundary communicates with its neighbouring downwind turbines and organizes them adaptively into a wind delivery group along the wind direction. The agent attributes and the event structure are designed on the basis of the intelligent agent theory by using the unified modelling language. The control strategy of the intelligent agent is studied using topology models. The reference power of an individual wind turbine from the wind farm controller is re‐dispatched to balance the turbine fatigue in the power dispatch intervals. In the fatigue optimization, the goal function is to minimize the standard deviation of the fatigue coefficient for every wind turbine. The optimization is constrained such that the average fatigue for every turbine is smaller than what would be achieved by conventional dispatch and such that the total power loss of the wind farm is restricted to a few percent of the total power. This intelligent agent control approach is verified through the simulation of wind data from the Horns Rev offshore wind farm. The results illustrate that intelligent agent control is a feasible way to optimize fatigue distribution in wind farms, which may reduce the maintenance frequency and extend the service life of large‐scale wind farms. Copyright © 2012 John Wiley & Sons, Ltd.     

Current situation and development of wind power in China

The current development of wind power in China was presented in this paper. Many regions such as Xinjiang Uygur Autonomous Region, Inner Mongolia Autonomous Region and southeast coastal region, etc. in China have abundant wind energy resource. At the same time, the utilization of wind power in China has been developing quickly and its prospect is promising in spite of many some obstacles. With the implementation of the Renewable Energy Law, some previous obstacles have been or are being eliminated. Much investment and many enterprises start to enter this field. In spite of this, there still exist some financial and technological obstacles. One of the technological obstacles is the stability of local power grid owing to the increasing proportion of the wind power capacity. Because the centralized development mode of wind power was adopted, the quick fluctuation of wind speed will influence the voltage and frequency stability of local power grid. In addition, large wind farm has little dispatching ability because of the uncontrollability, randomness and fluctuation of natural incoming wind. To erase these obstacles, a novel hybrid power system combining wind farm and small gas turbine power plants is discussed.     

风电场风电机组的接地设计

较系统地介绍了风电场风电机组对接地电阻的要求、接地设计思路及方法,并提供实际工程中接地网布置图实例作为参考。