Wind Energy Science最新文献

{"title":"Investigating the physical mechanisms that modify wind plant blockage in stable boundary layers","authors":"M. Sanchez Gomez, J. Lundquist, J. Mirocha, R. Arthur","doi":"10.5194/wes-8-1049-2023","DOIUrl":"https://doi.org/10.5194/wes-8-1049-2023","url":null,"abstract":"Abstract. Wind plants slow down the approaching wind, a phenomenon known as blockage. Wind plant blockage undermines turbine performance for front-row turbines and potentially for turbines deeper into the array. We use large-eddy simulations to characterize blockage upstream of a finite-size wind plant in flat terrain for different atmospheric stability conditions and investigate the physical mechanisms modifying the flow upstream of the turbines. To examine the influence of atmospheric stability, we compare simulations of two stably stratified boundary layers using the Weather Research and Forecasting model in large-eddy simulation mode, representing wind turbines using the generalized actuator disk approach. For a wind plant, a faster cooling rate at the surface, which produces stronger stably stratified flow in the boundary layer, amplifies blockage. As a novelty, we investigate the physical mechanisms amplifying blockage by evaluating the different terms in the momentum conservation equation within the turbine rotor layer. The velocity deceleration upstream of a wind plant is caused by an adverse pressure gradient and momentum advection out of the turbine rotor layer. The cumulative deceleration of the flow upstream of the front-row turbines instigates vertical motions. The horizontal flow is diverted vertically, reducing momentum availability in the turbine rotor layer. Although the adverse pressure gradient upstream of the wind plant remains unchanged with atmospheric stability, vertical advection of horizontal momentum is amplified in the more strongly stable boundary layer, mainly by larger shear of the horizontal velocity, thus increasing the blockage effect.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49320755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The eco-conscious wind turbine: design beyond purely economic metrics","authors":"H. Canet, A. Guilloré, C. Bottasso","doi":"10.5194/wes-8-1029-2023","DOIUrl":"https://doi.org/10.5194/wes-8-1029-2023","url":null,"abstract":"Abstract. Wind turbines are designed to minimize the economic cost of energy, a metric aimed at making wind competitive with other energy-producing technologies. However, now that wind energy is competitive, how can we increase its value for the environment and for society? And how much would environmental and societal gains cost other stakeholders, such as investors or consumers? This paper tries to answer these questions, limitedly to climate-related environmental impacts, from the perspective of wind turbine design. Although wind turbines produce green renewable energy, they also have\u0000various impacts on the environment, as do all human endeavors. Among all\u0000impacts, the present work adopts the environmental effects produced by a\u0000turbine over its entire life cycle, expressed in terms of CO2-equivalent\u0000emissions. A new approach to design is proposed, whereby Pareto fronts of\u0000solutions are computed to define optimal trade-offs between economic and\u0000environmental goals. The new proposed methodology is demonstrated on the redesign of a baseline 3 MW wind turbine at two locations in Germany, differing for typical wind speeds but within the same energy market. Among other results, it is found that, in these conditions, a 1 % increase in the cost of energy can buy about a 5 % decrease in the environmental impact of the turbine. Additionally, it is also observed that in the specific case of Germany, very low-specific-power designs are typically favored, because they produce more energy at low wind speeds, where both the economic and environmental values of wind are higher. Furthermore, it is found that the CO2-equivalent emissions displaced by a wind turbine are 1 order of magnitude larger than the produced emissions. Although limited to the sole optimization of wind-generating assets at two\u0000different locations, these results suggest the existence of new\u0000opportunities for the future development of wind energy where, by shifting\u0000the focus slightly away from a purely cost-driven short-term perspective, longer-term benefits for the environment (and, in turn, for society) may be obtained.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46445985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generalized analytical body force model for actuator disc computations of wind turbines","authors":"J. Sørensen","doi":"10.5194/wes-8-1017-2023","DOIUrl":"https://doi.org/10.5194/wes-8-1017-2023","url":null,"abstract":"Abstract. A new generalized analytical model for representing body forces in numerical actuator disc models of wind turbines is proposed and compared to results from a blade element momentum (BEM) model. The model is an extension of a previously developed load model, which was based on the rotor disc being subject to a constant circulation, modified for tip and root effects, corresponding to an optimum design case. By adding a parabolic circulation distribution, corresponding to a solid-body approach of the flow in the near wake, it is possible to take into account losses associated with off-design cases, corresponding to pitch regulation at high wind speeds. The advantage of the model is that it does not depend on any detailed knowledge concerning the actual wind turbine being analysed but only requires information about the thrust coefficient and tip-speed ratio. The model is validated for different wind turbines operating under a wide range of operating conditions. The comparisons show generally an excellent agreement with the BEM model even at very small thrust coefficients and tip-speed ratios.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42228282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From shear to veer: theory, statistics, and practical application","authors":"M. Kelly, M. P. van der Laan","doi":"10.5194/wes-8-975-2023","DOIUrl":"https://doi.org/10.5194/wes-8-975-2023","url":null,"abstract":"Abstract. In the past several years, wind veer – sometimes called “directional shear” – has begun to attract attention due to its effects on wind turbines and their production, particularly as the length of manufactured turbine blades has increased. Meanwhile, applicable meteorological theory has not progressed significantly beyond idealized cases for decades, though veer's effect on the wind speed profile has been recently revisited.\u0000On the other hand the shear exponent (α) is commonly used in wind energy for vertical extrapolation of mean wind speeds, as well as being a key parameter for wind turbine load calculations and design standards. In this work we connect the oft-used shear exponent with veer, both theoretically and for practical use. We derive relations for wind veer from the equations of motion, finding the veer to be composed of separate contributions from shear and vertical gradients of crosswind stress.\u0000Following from the theoretical derivations, which are neither limited to the surface layer nor constrained by assumptions about mixing length or turbulent diffusivities, we establish simplified relations between the wind veer and shear exponent for practical use in wind energy. We also elucidate the source of commonly observed stress–shear misalignment and its contribution to veer, noting that our new forms allow for such misalignment. The connection between shear and veer is further explored through analysis of one-dimensional (single-column) Reynolds-averaged Navier–Stokes solutions, where we confirm our theoretical derivations as well as the dependence of mean shear and veer on surface roughness and atmospheric boundary layer depth in terms of respective Rossby numbers. Finally we investigate the observed behavior of shear and veer across different sites and flow regimes (including forested, offshore, and hilly terrain cases) over heights corresponding to multi-megawatt wind turbine rotors, also considering the effects of atmospheric stability. From this we find empirical forms for the probability distribution of veer during high-veer (stable) conditions and for the variability in veer conditioned on wind speed. Analyzing observed joint probability distributions of α and veer, we compare the two simplified forms we derived earlier and adapt them to ultimately arrive at more universally applicable equations to predict the mean veer in terms of observed (i.e., conditioned on) shear exponent; lastly, the limitations, applicability, and behavior of these forms are discussed along with their use and further developments for both meteorology and wind energy.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44219466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Grand challenges in the digitalisation of wind energy","authors":"A. Clifton, S. Barber, Andrew Bray, P. Enevoldsen, Jason M. Fields, A. Sempreviva, Lindy Williams, J. Quick, M. Purdue, Philip Totaro, Yu-Shan Ding","doi":"10.5194/wes-8-947-2023","DOIUrl":"https://doi.org/10.5194/wes-8-947-2023","url":null,"abstract":"Abstract. The availability of large amounts of data is starting to impact how the\u0000wind energy community works. From turbine design to plant layout,\u0000construction, commissioning, and maintenance and operations, new\u0000processes and business models are springing up. This is the process of\u0000digitalisation, and it promises improved efficiency and greater insight,\u0000ultimately leading to increased energy capture and significant savings\u0000for wind plant operators, thus reducing the levelised cost of energy.\u0000Digitalisation is also impacting research, where it is both easing and\u0000speeding up collaboration, as well as making research results more\u0000accessible. This is the basis for innovations that can be taken up by\u0000end users. But digitalisation faces barriers. This paper uses a\u0000literature survey and the results from an expert elicitation to identify\u0000three common industry-wide barriers to the digitalisation of wind\u0000energy. Comparison with other networked industries and past and ongoing\u0000initiatives to foster digitalisation show that these barriers can only\u0000be overcome by wide-reaching strategic efforts, and so we see these as\u0000“grand challenges” in the digitalisation of wind energy. They are,\u0000first, creating FAIR data frameworks; secondly, connecting people and data to foster innovation; and finally, enabling collaboration and competition between organisations. The grand challenges in the digitalisation of wind energy thus include a mix of technical, cultural, and business aspects that\u0000will need collaboration between businesses, academia, and government to\u0000solve. Working to mitigate them is the beginning of a dynamic process\u0000that will position wind energy as an essential part of a global clean\u0000energy future.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48964756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated on data from operational wind farms","authors":"Xavier Chesterman, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, Jan Helsen","doi":"10.5194/wes-8-893-2023","DOIUrl":"https://doi.org/10.5194/wes-8-893-2023","url":null,"abstract":"Abstract. Condition monitoring and failure prediction for wind turbines currently comprise a hot research topic. This follows from the fact that investments in the wind energy sector have increased dramatically due to the transition to renewable energy production. This paper reviews and implements several techniques from state-of-the-art research on condition monitoring for wind turbines using SCADA data and the normal behavior modeling framework. The first part of the paper consists of an in-depth overview of the current state of the art. In the second part, several techniques from the overview are implemented and compared using data (SCADA and failure data) from five operational wind farms. To this end, six demonstration experiments are designed. The first five experiments test different techniques for the modeling of normal behavior. The sixth experiment compares several techniques that can be used for identifying anomalous patterns in the prediction error. The selection of the tested techniques is driven by requirements from industrial partners, e.g., a limited number of training data and low training and maintenance costs of the models. The paper concludes with several directions for future work.","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":"230 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135603320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparison of eight optimization methods applied to a wind farm layout optimization problem","authors":"Jared J. Thomas, Nicholas F. Baker, P. Malisani, Erik Quaeghebeur, Sebastian Sanchez Perez-Moreno, John P. Jasa, C. Bay, F. Tilli, David Bieniek, N. Robinson, A. Stanley, Wesley Holt, A. Ning","doi":"10.5194/wes-8-865-2023","DOIUrl":"https://doi.org/10.5194/wes-8-865-2023","url":null,"abstract":"Abstract. Selecting a wind farm layout optimization method is difficult. Comparisons between optimization methods in different papers can be uncertain due to the difficulty of exactly reproducing the objective function. Comparisons by just a few authors in one paper can be uncertain if the authors do not have experience using each algorithm. In this work we provide an algorithm comparison for a wind farm layout optimization case study between eight optimization methods applied, or directed, by researchers who developed those algorithms or who had other experience using them. We provided the objective function to each researcher to avoid ambiguity about relative performance due to a difference in objective function. While these comparisons are not perfect, we try to treat each algorithm more fairly by having researchers with experience using each algorithm apply each algorithm and by having a common objective function provided for analysis. The case study is from the International Energy Association (IEA) Wind Task 37, based on the Borssele III and IV wind farms with 81 turbines. Of particular interest in this case study is the presence of disconnected boundary regions and concave boundary features. The optimization methods studied represent a wide range of approaches, including gradient-free, gradient-based, and hybrid methods; discrete and continuous problem formulations; single-run and multi-start approaches; and mathematical and heuristic algorithms. We provide descriptions and references (where applicable) for each optimization method, as well as lists of pros and cons, to help readers determine an appropriate method for their use case. All the optimization methods perform similarly, with optimized wake loss values between 15.48 % and 15.70 % as compared to 17.28 % for the unoptimized provided layout. Each of the layouts found were different, but all layouts exhibited similar characteristics. Strong similarities across all the layouts include tightly packing wind turbines along the outer borders, loosely spacing turbines in the internal regions, and allocating similar numbers of turbines to each discrete boundary region. The best layout by annual energy production (AEP) was found using a new sequential allocation method, discrete exploration-based optimization (DEBO). Based on the results in this study, it appears that using an optimization algorithm can significantly improve wind farm performance, but there are many optimization methods that can perform well on the wind farm layout optimization problem, given that they are applied correctly.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42725979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The dynamic coupling between the pulse wake mixing strategy and floating wind turbines","authors":"Daniël Van Den Berg, D. De Tavernier, J. van Wingerden","doi":"10.5194/wes-8-849-2023","DOIUrl":"https://doi.org/10.5194/wes-8-849-2023","url":null,"abstract":"Abstract. In recent years, control techniques such as dynamic induction control (often referred to as “the pulse”) have shown great potential in increasing wake mixing, with the goal of minimising turbine-to-turbine interaction within a wind farm. Dynamic induction control disturbs the wake by varying the thrust of the turbine over time, which results in a time-varying induction zone. If applied to a floating wind turbine, this time-varying thrust force will, besides changing the wake, change the motion of the platform. In light of the expected movement, this work investigates if applying the pulse to a floating wind turbine yields similar results to that of the pulse applied to bottom-fixed turbines. This is done by considering first the magnitude of motions of the floating wind turbine due to the application of a time-varying thrust force and secondly the effect of these motions on the wake mixing. A frequency response experiment shows that the movement of the floating turbine is heavily frequency dependent, as is the thrust force. Time domain simulations, using a free-wake vortex method with uniform inflow, show that the expected gain in average wind speed at a distance of 5 rotor diameters downstream is more sensitive to the excitation frequency compared to a bottom-fixed turbine with the same pulse applied. This is due to the fact that, at certain frequencies, platform motion decreases the thrust force variation and thus reduces the onset of wake mixing.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43921020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new RANS-based wind farm parameterization and inflow model for wind farm cluster modeling","authors":"M. P. van der Laan, O. García-Santiago, M. Kelly, A. M. Meyer Forsting, C. Dubreuil-Boisclair, Knut Sponheim Seim, Marc Imberger, A. Peña, N. Sørensen, P. Réthoré","doi":"10.5194/wes-8-819-2023","DOIUrl":"https://doi.org/10.5194/wes-8-819-2023","url":null,"abstract":"Abstract. Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses; hence, there is a need for numerical models that can properly simulate wind farm interaction.\u0000This work proposes a Reynolds-averaged Navier–Stokes (RANS) method to efficiently simulate the effect of neighboring wind farms on wind farm power and annual energy production. First, a novel steady-state atmospheric inflow is proposed and tested for the application of RANS simulations of large wind farms. Second, a RANS-based wind farm parameterization is introduced, the actuator wind farm (AWF) model, which represents the wind farm as a forest canopy and allows to use of coarser grids compared to modeling all wind turbines as actuator disks (ADs). When the horizontal resolution of the RANS-AWF model is increased, the model results approach the results of the RANS-AD model. A double wind farm case is simulated with RANS to show that replacing an upstream wind farm with an AWF model only causes a deviation of less than 1 % in terms of the wind farm power of the downstream wind farm. Most importantly, a reduction in CPU hours of 75.1 % is achieved, provided that the AWF inputs are known, namely, wind farm thrust and power coefficients. The reduction in CPU hours is further reduced when all wind farms are represented by AWF models, namely, 92.3 % and 99.9 % for the double wind farm case and for a wind farm cluster case consisting of three wind farms, respectively. If the wind farm thrust and power coefficient inputs are derived from RANS-AD simulations, then the CPU time reduction is still 82.7 % for the wind farm cluster case.\u0000For the double wind farm case, the RANS models predict different wind speed flow fields compared to output from simulations performed with the mesoscale Weather Research and Forecasting model, but the models are in agreement with the inflow wind speed of the downstream wind farm. The RANS-AD-AWF model is also validated with measurements in terms of wind farm wake shape; the model captures the trend of the measurements for a wide range of wind directions, although the measurements indicate more pronounced wind farm wake shapes for certain wind directions.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49102313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating energy production and wake losses of multi-gigawatt offshore wind farms with atmospheric large-eddy simulation","authors":"P. Baas, R. Verzijlbergh, Pim van Dorp, Harm J. J. Jonker","doi":"10.5194/wes-8-787-2023","DOIUrl":"https://doi.org/10.5194/wes-8-787-2023","url":null,"abstract":"Abstract. As a consequence of the rapid growth of the globally installed offshore wind energy capacity, the size of individual wind farms is increasing. This poses a challenge to models that predict energy production. For instance, the current generation of wake models has mostly been calibrated on existing wind farms of much smaller size. This work analyzes annual energy production and wake losses for future, multi-gigawatt wind farms with atmospheric large-eddy simulation. To that end, 1 year of actual weather has been simulated for a suite of hypothetical 4 GW offshore wind farm scenarios. The scenarios differ in terms of applied turbine type, installed capacity density, and layout. The results suggest that production numbers increase significantly when the rated power of the individual turbines is larger while keeping the total installed capacity the same. Even for turbine types with similar rated power but slightly different power curves, significant differences in production were found. Although wind speed was identified as the most dominant factor determining the aerodynamic losses, a clear impact of atmospheric stability and boundary layer height has been identified. By analyzing losses of the first-row turbines, the yearly average global-blockage effect is estimated to between 2 and 3 %, but it can reach levels over 10 % for stably stratified conditions and wind speeds around 8 m s−1. Using a high-fidelity modeling technique, the present work provides insights into the performance of future, multi-gigawatt wind farms for a full year of realistic weather conditions.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43505204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}