Comment on wes-2022-14

This study explores the smoothing effect on the uncertainty of the wind power fluctuations that affect a power system at points of common coupling. Set pair analysis is applied to evaluate the similarity between the power fluctuations of a single wind turbine and those of all aggregated wind turbines, based on which a quantitative index describing the wind power smoothing effect is proposed. The smoothing effect characteristics of a wind farm cluster are investigated for different numbers of wind turbines, different wind speeds, different seasons, and multiple sampling intervals. A significant smoothing effect is usually observed at a shorter sampling interval and a higher wind speed, and the smoothing effect index increases with an increase in the numbers of wind turbines and farms. Additionally, the correlation between the smoothing effect of aggregated wind farms and the forecast accuracy for the corresponding aggregated power output is examined. The experimental results indicate that the wind power forecast accuracy varies with the smoothing effect index, which is influenced by the number of wind farms. Furthermore, the aggregated output from a wind farm cluster with a higher smoothing effect index exhibits better forecasting performance than that from a single wind farm, showing that the trend of the wind power series becomes smoother due to the smoothing effect, thus enabling one-step-ahead wind power forecasting with higher accuracy.

Wind power is also known as junk. This is because wind power fluctuations affect the security and stability operation. Wind power wind turbines created is mainly concerned with the speed of wind. Because of the wind direction uncertain, intermittent, and wake effects between each unit wind farm, wind turbines cannot make that kind of power according to the demand for energy as conventional generators. Due to the lack of experimental data, assess the volatility of wind power is still a lack of effective methods. This article studies the sample in a northeast wind farm power, and based on a sliding differential algorithm, distribution fitting and quantitative calculations describe the characteristics of wind power fluctuations. This article studies the sample in a northeast wind farm power, and based on a sliding difference algorithm, through the analysis showed that wind power fluctuations obey t location scale distribution. And it is affected by factors such as spatial and temporal distribution, there is a big difference between the output power fluctuation characteristics of wind farm output power and single wind turbine. This is due to the wind turbine suffered varying differences, and wake effects between field units, making the distribution of frequent power fluctuations; relative to a single unit, the fluctuation of the whole wind farm is more gentle, that is to say with the spatial distribution increased scale, wind power fluctuations presents certain "gentle effect."

Optimal design of wind turbine placement in a wind farm is one of the most effective tools to reduce wake power losses by alleviating the wake effect in the wind farm. In comparison to the discrete grid-based wind farm design method, the continuous coordinate method has the property of continuously varying the placement of wind turbines, and hence, is far more capable of obtaining the global optimum solutions. In this paper, the coordinate method was applied to optimize the layout of a real offshore wind farm for both simplified and realistic wind conditions. A new analytical wake model (Jensen-Gaussian model) taking into account the wake velocity variation in the radial direction was employed for the optimization study. The means of handling the irregular real wind farm boundary were proposed to guarantee that the optimized wind turbine positions are feasible within the wind farm boundary, and the discretization method was applied for the evaluation of wind farm power output under Weibull distribution. By investigating the wind farm layout optimization under different wind conditions, it showed that the total wind farm power output increased linearly with an increasing number of wind turbines. Under some particular wind conditions (e.g., constant wind speed and wind direction, and Weibull distribution), almost the same power losses were obtained under the wake effect of some adjacent wind turbine numbers. A common feature of the wind turbine placements regardless of the wind conditions was that they were distributed along the wind farm boundary as much as possible in order to alleviate the wake effect.

A multiscale numerical framework coupled with control strategies for simulating a wind farm in complex terrain

With increasing installation capacity and wind farms penetration, wind power plays more important role in power systems, and the modeling of wind farms has become an interesting research topic. In this paper, a coherency-based equivalent model has been discussed for the doubly fed induction generator (DFIG). Firstly, the dynamic models of wind turbines, DFIG and the mechanisms are briefly introduced. Some existing dynamic equivalent methods such as equivalent wind model, variable speed wind turbine model, parameter identification method and modal equivalent method to be used in wind farm aggregation are discussed. Then, considering wind power fluctuations, a new equivalent model of a wind farm equipped with doubly-fed induction generators is proposed to represent the interactions of the wind farm and grid. The method proposed is based on aggregating the coherent group wind turbines into an equivalent one. Finally, the effectiveness of the equivalent model is demonstrated by comparison with the wind farm response obtained from the detailed model. The dynamic simulations show that the present model can greatly reduce the computation time and model complexity.

During recent years, wind energy has moved from an emerging technology to a nearly competitive technology. This fact, coupled with an increasing global focus on environmental concern and a political desire of a certain level of diversification in the energy supply, ensures wind energy an important role in the future electricity market. For this challenge to be met in a cost-efficient way, a substantial part of new wind turbine installations is foreseen to be erected in big onshore or offshore wind farms. This fact makes the production, loading and reliability of turbines operating under such conditions of particular interest.

Mitigating wake effects between wind turbines is crucial for enhancing the overall output power of offshore wind farms. Therefore, optimizing turbine spacing and layout under turbulent conditions is essential. This study employs the NREL-5 MW wind turbine model to investigate the efficiency of a 3 × 3 wind farm. This research focuses on the influence of turbine spacing and layout on wake field distribution and output power characteristics under different turbulence intensities. A key innovation is the application of entropy production theory to quantify energy dissipation and wake recovery, providing a deeper understanding of the underlying mechanisms in energy losses. This research also introduces fatigue analysis based on the Damage Equivalent Load (DEL) method, revealing that staggered layouts significantly reduce cyclic loads and extend turbine lifespan. The results indicate that modifying the layout is a more effective strategy for enhancing the total power output of the wind farm, which proves to be more effective than altering the turbulence intensity. Specifically, staggered layout I (with a downstream stagger of 1.0 rotor diameter (D)) increases total output power by 28.76% (to 36.84 MW) and causes a 16.38% surge in power when the spacing increases to 5D. Expanding the wind turbine spacing mitigates wake interaction, resulting in a dramatic 59.84% power recovery for downstream wind turbines. The wind turbine’s lifespan is extended as a result of fatigue loads on the root bending moment being substantially reduced by the staggered layout, which alters the wake structure and stress distribution. The entropy production analysis shows that regions with high entropy production are primarily concentrated near the rotor and within the wake shear layer. The energy dissipation is substantially reduced in the case of staggered layout. These findings provide valuable guidance for the aerodynamic optimization and long-term operation design of large-scale wind farms, contributing to improved energy efficiency and reduced maintenance costs.

Multi-objective optimization of clustered wind farms based on potential game approach

Large-eddy simulation of a utility-scale wind farm in complex terrain

Researching on interaction between wind farm and power system, it usually uses simple model to describe the wind farm. The wind turbines in a wind farm always suppose as a coherent generator group, every wind turbines have identical wind speed. As wind turbine locates in the different site in the wind farm, the wind speed at each wind turbines will not change simultaneously. Wake effect is another factor that should take into consideration. So, it needs to get wind speed equivalent model. The equivalent model of wind speed in the wind farm is based on output of power. Gust wind and ramp wind are discussed. Wind farm will change gust wind into step varying wind and make ramp wind into different type wind. The arrangement of wind turbines will also affect equivalent model of wind speed. The result show that the length of wind farm will smooth the power output. It explains why the power output fluctuation of wind farm is smaller than single wind turbine. The onshore wind farm located in Dongtai Jiangsu province is proposed as an example. The equivalent wind speed model and equivalent impedance connected to grid are calculated. The results illustrate the impact of wind speed model on wind farm output.

This paper proposes a coordinated operational strategy of energy storage system (ESS) and wind farm to reduce wind power spillage and alleviate wind power fluctuation. Firstly, power curve of a wind farm, in the form of an equivalent wind turbine, is determined by least square fitting. Based on the power output characteristic of the wind farm and historic operating data, the statistical properties of wind power spillage and fluctuation can be obtained. Secondly, the target for wind farm and ESS power output is calculated. Power generation/absorption capabilities of ESS and available power output reducing range of the wind farm are quantified as well, considering the operational constraints of ESS and dispatched wind power output. Finally, a coordinated operational strategy is developed not only to adjust the combined ESS and wind farm to meet the dispatch requirement, but also to ensure ESS and the wind farm to satisfy operational constraints. Case studies are conducted on a practical wind farm with battery energy storage system installed, which demonstrate that the coordination of ESS can greatly boost the revenue and improve power quality of wind farm.

he increase in demand for electricity in the United States motivates a unique opportunity for researching alternative energy sources – the need for electricity is mounting, while traditional sources for electrical generation, such as coal, are depleting and becoming more costly to access. Exploring alternative energy sources, such as wind power, are therefore ideal, as wind is readily available and will not deplete with use. However, the optimization of wind farms is imperative to ensure that wind farm power development is as high as possible, and that prediction of wind farm performance prior to installation is accurate. To meet this goal, the modeling that is being employed within wind farm optimization schemes must be reflective of real-world wind farm conditions, using site-specific parameters to ensure that researchers can accurately predict the potential power development and costs for resulting wind farm designs.

ABSTRACTThe 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.

This article presents a method for performing noise constrained optimization of wind farms by changing the operational modes of the individual wind turbines. The optimization is performed by use of the TopFarm framework and the PyWake wind farm modeling as well as two sound propagation models: the ISO 9613-2 model and the Parabolic Equation model, WindSTAR. The two sound propagation models introduce different levels of complexity to the optimization problem with the WindSTAR model taking a broader range of parameters, like the acoustic ground impedance, the complex terrain elevation and the flow field from the noise source to the receptor, into account. Thus, as the WindSTAR model introduces a higher complexity of the sound propagation computations, it likewise introduces a higher computational time. Wind farm optimization using each of the two sound propagation models is therefore performed in different atmospheric conditions and for different source/receptor setups, and compared through this study in order to evaluate the advantage of using a more complex sound propagation model. The article focuses on artificial wind farms in flat terrain as well as arbitrarily chosen dwellings at which the noise constraints are applied. By this, the study presents the potential of an optimization algorithm focusing on the sound propagation and wind farm operation trade-off.

The risk assessment of frequency deviation in power systems caused by the wind power fluctuations is an essential issue on wind power integration, thus a more realistic wind power fluctuation simulation is necessary. To construct wind power fluctuations, not only time scale but also wind direction should be considered for the wind speed model with spatial correlation in a wind farm. In this paper, wind direction is added to the wind speed model on the basis of the second level wind speed with spatial correlation. Based on the Von Mises distribution and the Weibull distribution, the correlation of wind speed and direction is analyzed, and the sequences are obtained according to Monte Carlo Sampling, describing the correlation between wind speed and wind direction clearly. Then the wind power fluctuations with spatial correlation is available. Therefore, the frequency deviation resulted from the uncertainty of wind power fluctuations can be evaluated more accurately.