Sensitivity of southern California wind energy to turbine characteristics

Using output from a high‐resolution meteorological simulation, we evaluate the sensitivity of southern California wind energy generation to variations in key characteristics of current wind turbines. These characteristics include hub height, rotor diameter and rated power, and depend on turbine make and model. They shape the turbine's power curve and thus have large implications for the energy generation capacity of wind farms. For each characteristic, we find complex and substantial geographical variations in the sensitivity of energy generation. However, the sensitivity associated with each characteristic can be predicted by a single corresponding climate statistic, greatly simplifying understanding of the relationship between climate and turbine optimization for energy production. In the case of the sensitivity to rotor diameter, the change in energy output per unit change in rotor diameter at any location is directly proportional to the weighted average wind speed between the cut‐in speed and the rated speed. The sensitivity to rated power variations is likewise captured by the percent of the wind speed distribution between the turbines rated and cut‐out speeds. Finally, the sensitivity to hub height is proportional to lower atmospheric wind shear. Using a wind turbine component cost model, we also evaluate energy output increase per dollar investment in each turbine characteristic. We find that rotor diameter increases typically provide a much larger wind energy boost per dollar invested, although there are some zones where investment in the other two characteristics is competitive. Our study underscores the need for joint analysis of regional climate, turbine engineering and economic modeling to optimize wind energy production. Copyright © 2012 John Wiley & Sons, Ltd.     

Global experience curves for wind farms

In order to forecast the technological development and cost of wind turbines and the production costs of wind electricity, frequent use is made of the so-called experience curve concept. Experience curves of wind turbines are generally based on data describing the development of national markets, which cause a number of problems when applied for global assessments. To analyze global wind energy price development more adequately, we compose a global experience curve. First, underlying factors for past and potential future price reductions of wind turbines are analyzed. Also possible implications and pitfalls when applying the experience curve methodology are assessed. Second, we present and discuss a new approach of establishing a global experience curve and thus a global progress ratio for the investment cost of wind farms. Results show that global progress ratios for wind farms may lie between 77% and 85% (with an average of 81%), which is significantly more optimistic than progress ratios applied in most current scenario studies and integrated assessment models. While the findings are based on a limited amount of data, they may indicate faster price reduction opportunities than so far assumed. With this global experience curve we aim to improve the reliability of describing the speed with which global costs of wind power may decline.     

Large eddy simulations of the effect of vertical staggering in large wind farms

In order to study the effect of vertical staggering in large wind farms, large eddy simulations (LES) of large wind farms with a regular turbine layout aligned with the given wind direction were conducted. In the simulations, we varied the hub heights of consecutive downstream rows to create vertically staggered wind farms. We analysed the effect of streamwise and spanwise turbine spacing, the wind farm layout, the turbine rotor diameter, and hub height difference between consecutive downstream turbine rows on the average power output. We find that vertical staggering significantly increases the power production in the entrance region of large wind farms and is more effective when the streamwise turbine spacing and turbine diameter are smaller. Surprisingly, vertical staggering does not significantly improve the power production in the fully developed regime of the wind farm. The reason is that the downward vertical kinetic energy flux, which brings high velocity fluid from above the wind farm towards the hub height plane, does not increase due to vertical staggering. Thus, the shorter wind turbines are effectively sheltered from the atmospheric flow above the wind farm that supplies the energy, which limits the benefit of vertical staggering. In some cases, a vertically staggered wind farm even produced less power than the corresponding non vertically staggered reference wind farm. In such cases, the production of shorter turbines is significantly negatively impacted while the production of the taller turbine is only increased marginally.     

Dependence of optimal wind turbine spacing on wind farm length

Recent large eddy simulations have led to improved parameterizations of the effective roughness height of wind farms. This effective roughness height can be used to predict the wind velocity at hub‐height as function of the geometric mean of the spanwise and streamwise turbine spacings and the turbine loading factors. Recently, Meyers and Meneveau used these parameterizations to make predictions for the optimal wind turbine spacing in infinitely large wind farms. They found that for a realistic cost ratio between the turbines and the used land surface, the optimal turbine spacing may be considerably larger than that used in conventional wind farms. Here, we extend this analysis by taking the length of the wind farm, i.e. the number of rows in the downstream direction into account and show that the optimal turbine spacing strongly depends on the wind farm length. For small to moderately sized wind farms, the model predictions are consistent with spacings found in operational wind farms. For much larger wind farms, the extended optimal spacing found for infinite wind farms is confirmed. Copyright © 2015 John Wiley & Sons, Ltd.     

Using nacelle‐based wind speed observations to improve power curve modeling for wind power forecasting

Wind power forecasting for projection times of 0–48 h can have a particular value in facilitating the integration of wind power into power systems. Accurate observations of the wind speed received by wind turbines are important inputs for some of the most useful methods for making such forecasts. In particular, they are used to derive power curves relating wind speeds to wind power production. By using power curve modeling, this paper compares two types of wind speed observations typically available at wind farms: the wind speed and wind direction measurements at the nacelles of the wind turbines and those at one or more on‐site meteorological masts (met masts). For the three Australian wind farms studied in this project, the results favor the nacelle‐based observations despite the inherent interference from the nacelle and the blades and despite calibration corrections to the met mast observations. This trend was found to be stronger for wind farm sites with more complex terrain. In addition, a numerical weather prediction (NWP) system was used to show that, for the wind farms studied, smaller single time‐series forecast errors can be achieved with the average wind speed from the nacelle‐based observations. This suggests that the nacelle‐average observations are more representative of the wind behavior predicted by an NWP system than the met mast observations. Also, when using an NWP system to predict wind farm power production, it suggests the use of a wind farm power curve based on nacelle‐average observations instead of met mast observations. Further, it suggests that historical and real‐time nacelle‐average observations should be calculated for large wind farms and used in wind power forecasting. Copyright © 2011 John Wiley & Sons, Ltd.     

Optimal placement of wind turbines within a wind farm considering multi-directional wind speed using two-stage genetic algorithm

Most wind turbines within wind farms are set up to face a pre-determined wind direction. However, wind directions are intermittent in nature, leading to less electricity production capacity. This paper proposes an algorithm to solve the wind farm layout optimization problem considering multi-angular (MA) wind direction with the aim of maximizing the total power generated on wind farms and minimizing the cost of installation. A two-stage genetic algorithm (GA) equipped with complementary sampling and uniform crossover is used to evolve a MA layout that will yield optimal output regardless of the wind direction. In the first stage, the optimal wind turbine layouts for 8 different major wind directions were determined while the second stage allows each of the previously determined layouts to compete and inter-breed so as to evolve an optimal MA wind farm layout. The proposed MA wind farm layout is thereafter compared to other layouts whose turbines have focused site specific wind turbine orientation. The results reveal that the proposed wind farm layout improves wind power production capacity with minimum cost of installation compared to the layouts with site specific wind turbine layouts. This paper will find application at the planning stage of wind farm.     

Difference between grid connections of large-scale wind power and conventional synchronous generation

In China, regions with abundant wind energy resources are generally located at the end of power grids. The power grid architecture in these regions is typically not sufficiently strong, and the energy structure is relatively simple. Thus, connecting large-capacity wind power units complicates the peak load regulation and stable operation of the power grids in these regions. Most wind turbines use power electronic converter technology, which affects the safety and stability of the power grid differently compared with conventional synchronous generators. Furthermore, fluctuations in wind power cause fluctuations in the output of wind farms, making it difficult to create and implement suitable power generation plans for wind farms. The generation technology and grid connection scheme for wind power and conventional thermal power generation differ considerably. Moreover, the active and reactive power control abilities of wind turbines are weaker than those of thermal power units, necessitating additional equipment to control wind turbines. Hence, to address the aforementioned issues with large-scale wind power generation, this study analyzes the differences between the grid connection and collection strategies for wind power bases and thermal power plants. Based on this analysis, the differences in the power control modes of wind power and thermal power are further investigated. Finally, the stability of different control modes is analyzed through simulation. The findings can be beneficial for the planning and development of large-scale wind power generation farms.     

Power smoothing in large wind farms using optimal control of rotating kinetic energy reserves

In large wind farms, self‐induced turbulence levels significantly increase the variability of generated power in a range of time scales from a few seconds to several minutes. In the current study, we investigate the potential for reducing this type of variability by dynamically controlling the rotating kinetic energy reserves that are present in the farm's wind turbines. To this end, we reduce the burden of frequency regulation on remaining conventional units when they are displaced in favor of wind turbines. We focus on the development of a theoretical benchmark framework in which we explore the trade‐off between high energy extraction and low variability using optimal coordinated control of multiple turbines subject to a turbulent wind field. This wind field is obtained from a large‐eddy simulation of a fully developed wind farm boundary layer. The controls that are optimized are the electric torque and the pitch angles of the individual turbines as function of time so that turbines are accelerated or decelerated to optimally extract or store energy in the turbines' rotating inertia. Results are presented in terms of Pareto fronts (i.e., curves with optimal trade‐offs), and we find that power variations can be significantly reduced with limited loss of extracted energy. For a one‐turbine case, such an optimal control leads to large potential reductions of variability but mainly for time scales below 10 s if we limit power losses to a few percent. Variability over longer time scales (10–100 s) is reduced considerably more for coordinated control. For instance, restricting the energy‐loss incurred with smoothing to 1%, and looking at time scales of 50 s, we manage to reduce variability with a factor of 6 for a coordinated case with 24 turbines, compared with a factor of 1.4 for an uncoordinated case. Copyright © 2014 John Wiley & Sons, Ltd.     

Dynamic models of wind farms with fixed speed wind turbines

The increasing wind power penetration on power systems requires the development of adequate wind farms models for representing the dynamic behaviour of wind farms on power systems. The behaviour of a wind farm can be represented by a detailed model including the modelling of all wind turbines and the wind farm electrical network. But this detailed model presents a high order model if a wind farm with high number of wind turbines is modelled and therefore the simulation time is long. The development of equivalent wind farm models enables the model order and the computation time to be reduced when the impact of wind farms on power systems is studied. In this paper, equivalent models of wind farms with fixed speed wind turbines are proposed by aggregating wind turbines into an equivalent wind turbine that operates on an equivalent wind farm electrical network. Two equivalent wind turbines have been developed: one for aggregated wind turbines with similar winds, and another for aggregated wind turbines under any incoming wind, even with different incoming winds.The proposed equivalent models provide high accuracy for representing the dynamic response of wind farm on power system simulations with an important reduction of model order and simulation time compare to that of the complete wind farm modelled by the detailed model.     

Survey of modelling methods for wind turbine wakes and wind farms

This article provides an overview and analysis of different wake‐modelling methods which may be used as prediction and design tools for both wind turbines and wind farms. We also survey the available data concerning the measurement of wind magnitudes in both single wakes and wind farms, and of loading effects on wind turbines under single‐ and multiple‐wake conditions. The relative merits of existing wake and wind farm models and their ability to reproduce experimental results are discussed. Conclusions are provided concerning the usefulness of the different modelling approaches examined, and difficult issues which have not yet been satisfactorily treated and which require further research are discussed. Copyright © 1999 John Wiley & Sons, Ltd.     

ENDOW (efficient development of offshore wind farms): modelling wake and boundary layer interactions

While experience gained through the offshore wind energy projects currently operating is valuable, a major uncertainty in estimating power production lies in the prediction of the dynamic links between the atmosphere and wind turbines in offshore regimes. The objective of the ENDOW project was to evaluate, enhance and interface wake and boundary layer models for utilization offshore. The project resulted in a significant advance in the state of the art in both wake and marine boundary layer models, leading to improved prediction of wind speed and turbulence profiles within large offshore wind farms. Use of new databases from existing offshore wind farms and detailed wake profiles collected using sodar provided a unique opportunity to undertake the first comprehensive evaluation of wake models in the offshore environment. The results of wake model performance in different wind speed, stability and roughness conditions relative to observations provided criteria for their improvement. Mesoscale model simulations were used to evaluate the impact of thermal flows, roughness and topography on offshore wind speeds. The model hierarchy developed under ENDOW forms the basis of design tools for use by wind energy developers and turbine manufacturers to optimize power output from offshore wind farms through minimized wake effects and optimal grid connections. The design tools are being built onto existing regional‐scale models and wind farm design software which was developed with EU funding and is in use currently by wind energy developers. Copyright © 2004 John Wiley & Sons, Ltd.     

Probabilistic capacity of a grid connected wind farm based on optimization method

This paper proposes a new method to find the capacity of a future wind farm regarding several limits of the grid system: voltage stability limits, thermal limits, voltage limits, load tap changing limitation and generator power output limitation. The method combines the optimization method and a probabilistic analysis to maximize the number of the wind turbines subject to those limits. Two types of wind farms are modeled in this paper: fixed speed wind farms and variable speed wind farms. It is concluded that the proposed method is a fast and accurate method to deal with the uncertainty of wind energy in the planning of wind farm capacity.     

Optimal gain-scheduled control of fixed-speed active stall wind turbines

For a smooth integration of large wind farms into the utility grids, the individual wind turbines must be able to achieve various power control objectives. In this context, the authors focus their attention on the control of fixed-speed active stall wind turbines. This sort of turbine includes a pitch servomechanism to induce stall on the blades, thereby having control on the output power. The authors develop a methodology to design optimal gain-scheduled pitch controllers valid for the whole operating region of the wind turbine. The proposed solution uses concepts of linear parameter-varying system theory. In addition to providing a formal framework for the control design, this theory guarantees stability and performance. Further, because of the similarities with Hαcontrol, the tools developed for the controller design are very familiar to the control community. The main features of the proposed controller are assessed by means of numerical simulations obtained for realistic wind speed profiles and power production demands.     

Energy production assessment of small wind farms

The present work investigates the performance and the energy production of a grid-connected wind farm during six years of operation. The layout and the single line diagram of this wind farm are shown. A complete record of operational data for five turbines is analysed to study the performance of the wind farm. The study illustrates the variation of the energy and wind speed on annual and monthly basis for the whole examined period. On the other hand, the annual growth in the local consumption of the wind farm is shown. As an important indicator, the capacity factor is analysed for single turbines and for the whole wind farm. Finally, the study attempts to correlate the results to the external and internal factors affecting the performance of these turbines using the available database.     

Benefits of collocating vertical‐axis and horizontal‐axis wind turbines in large wind farms

In this study, we address the benefits of a vertically staggered (VS) wind farm, in which vertical‐axis and horizontal‐axis wind turbines are collocated in a large wind farm. The case study consists of 20 small vertical‐axis turbines added around each large horizontal‐axis turbine. Large‐eddy simulation is used to compare power extraction and flow properties of the VS wind farm versus a traditional wind farm with only large turbines. The VS wind farm produces up to 32% more power than the traditional one, and the power extracted by the large turbines alone is increased by 10%, caused by faster wake recovery from enhanced turbulence due to the presence of the small turbines. A theoretical analysis based on a top‐down model is performed and compared with the large‐eddy simulation. The analysis suggests a nonlinear increase of total power extraction with increase of the loading of smaller turbines, with weak sensitivity to various parameters, such as size, and type aspect ratio, and thrust coefficient of the vertical‐axis turbines. We conclude that vertical staggering can be an effective way to increase energy production in existing wind farms. Copyright © 2016 John Wiley & Sons, Ltd.     

Optimal selection of time windows for preventive maintenance of offshore wind farms subject to wake losses

The maintenance of wind farms is one of the major factors affecting their profitability. During preventive maintenance, the shutdown of wind turbines causes downtime energy losses. The selection of when and which turbines to maintain can significantly impact the overall downtime energy loss. This paper leverages a wind farm power generation model to calculate downtime energy losses during preventive maintenance for an offshore wind farm. Wake effects are considered to accurately evaluate power output under specific wind conditions. In addition to wind speed and direction, the influence of wake effects is an important factor in selecting time windows for maintenance. To minimize the overall downtime energy loss of an offshore wind farm caused by preventive maintenance, a mixed-integer nonlinear optimization problem is formulated and solved by the genetic algorithm, which can select the optimal maintenance time windows of each turbine. Weather conditions are imposed as constraints to ensure the safety of maintenance personnel and transportation. Using the climatic data of Cape Cod, Massachusetts, the schedule of preventive maintenance is optimized for a simulated utility-scale offshore wind farm. The optimized schedule not only reduces the annual downtime energy loss by selecting the maintenance dates when wind speed is low but also decreases the overall influence of wake effects within the farm. The portion of downtime energy loss reduced due to consideration of wake effects each year is up to approximately 0.2% of the annual wind farm energy generation across the case studies—with other stated opportunities for further profitability improvements.     

Optimal energy production from wind and hydroelectric power plants

All countries attach great importance to renewable energy investments with concern that future fossil-fueled energy resources could be exhausted. Thus, a very large renewable energy production potential may be predicted in not a very distant future. This study is about optimal energy production from wind and hydroelectric power plants at a small scale settlement center. A water resources system with multiple reservoirs in which wind power plants are located around the basin is described in this study. The system has three scenarios, in which wind and hydroelectric power plants are integrated, separated and no wind turbines. In the integrated scenario, by the energy generated in the wind power plants, the released flows from the reservoirs are sent to the reservoirs as a use again. In models of every scenario, optimal operational models for long-term planning are established on the system. The technique of dynamic programming with successive approximations is used in these models. The models are applied to a water resources system with multiple reservoirs presented successively on the main line of the Ceyhan River in the Ceyhan Basin. The results obtained here are evaluated in terms of three scenarios developed for energy production. As a result, it has been seen that the systems of the integrated and separated scenario are similar to energy productions and system without wind turbines produces more little energy production to other scenarios.     

A frequency domain approach to wind turbines for flicker analysis

Wind turbines may have an important impact on power quality. Flicker is a more serious issue for fixed-speed wind turbines because these turbines produce electric power following the variations of the incident wind. During continuous operation, wind variations will result in power fluctuations and consequently in voltage fluctuations. It is necessary to evaluate wind turbines flicker emission level, and traditionally time domain simulations have been used to perform the analysis. This paper presents a complete frequency domain model to study flicker produced during wind turbines continuous operation. The model includes a realistic wind speed model as observed by the wind turbine and also a frequency domain induction generator model is presented. The frequency domain model has been compared with a time domain model. The frequency domain approach, as shown in the paper, may be very useful for flicker analysis in electric networks.     

Mean and turbulent kinetic energy budgets inside and above very large wind farms under conventionally-neutral condition

In this study, large-eddy simulations (LES) is combined with a turbine model to investigate all the terms in the budgets of mean and turbulent kinetic energy (TKE) inside and above very large wind farms. Emphasis is placed on quantifying the relative contribution of the thermal stratification in the free-atmosphere and wind-turbine spacing on the energy balance. The mean kinetic energy budget through the wind farms indicates that the magnitude of the kinetic energy entrainment form the free atmosphere into the boundary layer increases by increasing the density of the farms and decreasing the static stability in the free atmosphere, leading to larger power output from the wind farms. This entrainment is the only source of kinetic energy to balance that extracted by the turbines inside very large wind farms. In addition, it is shown that the distribution of the kinetic energy flux above the wind turbines, at top-tip level, is quite heterogeneous and its magnitude just behind the wind turbines is much larger due to the strong wind shear at that level. The simulation results also show that increasing the wind-farm density leads to an increase in the boundary-layer height, the ratio of the ageostrophic to the geostrophic velocity component inside the boundary layer, and the potential temperature near the surface. Detailed analysis of the TKE budget through the wind farms reveals also an important effect of the thermal stratification and wind turbine spacing on the magnitude and spatial distribution of the shear production, dissipation rate and transport terms. In particular, the shear production and dissipation rate have a peak at the turbine-top level, where the wind shear is largest, and their magnitude increases as the static stability in the free atmosphere and the wind-turbine spacing decrease.     

Short‐term wind speed prediction in wind farms based on banks of support vector machines

Wind speed prediction is a key point in the management of wind farms because it is directly related to the power produced by each of a farm's turbines. Wind speed prediction is usually one of the most important tasks in wind farming, and companies that manage these farms invest large amounts of money to improve their prediction systems. In this paper, we propose an improvement to an existing wind speed prediction system, using banks of regression Support Vector Machines (SVMr) for a final regression step in the system. Several novel SVMr structures are proposed in this paper to manage the diversity in input data arising from the use of different global forecasting models and several parameterizations of a mesoscale model, included in the basic version of the prediction system. We show that the system implementing SVMr banks outperforms the basic system without taking into account diversity in the input data. It also performs better than a similar system using banks of multi‐layer perceptrons. All the tests are carried out using real data from several wind turbines on a wind farm in southeast Spain. Copyright © 2010 John Wiley & Sons, Ltd.