Sebastiano Stipa, Arjun Ajay, D. Allaerts, J. Brinkerhoff
Abstract. The growing number and growing size of wind energy projects coupled with the rapid growth in high-performance computing technology are driving researchers toward conducting large-scale simulations of the flow field surrounding entire wind farms. This requires highly parallel-efficient tools, given the large number of degrees of freedom involved in such simulations, and yields valuable insights into farm-scale physical phenomena, such as gravity wave interaction with the wind farm and farm–farm wake interactions. In the current study, we introduce the open-source, finite-volume, large-eddy simulation (LES) code TOSCA (Toolbox fOr Stratified Convective Atmospheres) and demonstrate its capabilities by simulating the flow around a finite-size wind farm immersed in a shallow, conventionally neutral boundary layer (CNBL), ultimately assessing gravity-wave-induced blockage effects. Turbulent inflow conditions are generated using a new hybrid off-line–concurrent-precursor method. Velocity is forced with a novel pressure controller that allows us to prescribe a desired average hub-height wind speed while avoiding inertial oscillations above the atmospheric boundary layer (ABL) caused by the Coriolis force, a known problem in wind farm LES studies. Moreover, to eliminate the dependency of the potential-temperature profile evolution on the code architecture observed in previous studies, we introduce a method that allows us to maintain the mean potential-temperature profile constant throughout the precursor simulation. Furthermore, we highlight that different codes do not predict the same velocity inside the boundary layer under geostrophic forcing owing to their intrinsically different numerical dissipation. The proposed methodology allows us to reduce such spread by ensuring that inflow conditions produced from different codes feature the same hub wind and thermal stratification, regardless of the adopted precursor run time. Finally, validation of actuator line and disk models, CNBL evolution, and velocity profiles inside a periodic wind farm is also presented to assess TOSCA’s ability to model large-scale wind farm flows accurately and with high parallel efficiency.
摘要风能项目的数量和规模不断增加，加上高性能计算技术的快速发展，促使研究人员对整个风电场周围的流场进行大规模模拟。由于此类模拟涉及大量自由度，因此需要高度并行高效的工具，并能对风电场规模的物理现象（如重力波与风电场的相互作用以及风电场与风电场之间的尾流相互作用）提出有价值的见解。在当前的研究中，我们介绍了开源、有限体积、大涡模拟（LES）代码 TOSCA（Toolbox fOr Stratified Convective Atmospheres，分层对流大气工具箱），并通过模拟浸没在浅层常规中性边界层（CNBL）中的有限尺寸风电场周围的流动来展示其功能，最终评估重力波引起的阻塞效应。湍流流入条件是通过一种新的混合离线-共流-前导方法产生的。速度是通过新型压力控制器强制产生的，该控制器允许我们设定所需的轮毂高度平均风速，同时避免科里奥利力引起的大气边界层（ABL）上方的惯性振荡，这是风电场 LES 研究中的一个已知问题。此外，为了消除以往研究中观察到的势温剖面演变对代码结构的依赖性，我们引入了一种方法，使我们能够在整个前兆模拟过程中保持平均势温剖面不变。此外，我们还强调，不同的代码由于其内在的数值耗散不同，在地营强迫下预测的边界层内速度也不相同。所提出的方法使我们能够减少这种差异，确保不同代码产生的流入条件具有相同的枢纽风和热分层，而不管所采用的前兆运行时间。最后，我们还介绍了对推杆线和盘模型、CNBL 演变以及周期性风电场内部速度剖面的验证，以评估 TOSCA 以高并行效率准确模拟大规模风电场流动的能力。
{"title":"TOSCA – an open-source, finite-volume, large-eddy simulation (LES) environment for wind farm flows","authors":"Sebastiano Stipa, Arjun Ajay, D. Allaerts, J. Brinkerhoff","doi":"10.5194/wes-9-297-2024","DOIUrl":"https://doi.org/10.5194/wes-9-297-2024","url":null,"abstract":"Abstract. The growing number and growing size of wind energy projects coupled with the rapid growth in high-performance computing technology are driving researchers toward conducting large-scale simulations of the flow field surrounding entire wind farms. This requires highly parallel-efficient tools, given the large number of degrees of freedom involved in such simulations, and yields valuable insights into farm-scale physical phenomena, such as gravity wave interaction with the wind farm and farm–farm wake interactions. In the current study, we introduce the open-source, finite-volume, large-eddy simulation (LES) code TOSCA (Toolbox fOr Stratified Convective Atmospheres) and demonstrate its capabilities by simulating the flow around a finite-size wind farm immersed in a shallow, conventionally neutral boundary layer (CNBL), ultimately assessing gravity-wave-induced blockage effects. Turbulent inflow conditions are generated using a new hybrid off-line–concurrent-precursor method. Velocity is forced with a novel pressure controller that allows us to prescribe a desired average hub-height wind speed while avoiding inertial oscillations above the atmospheric boundary layer (ABL) caused by the Coriolis force, a known problem in wind farm LES studies. Moreover, to eliminate the dependency of the potential-temperature profile evolution on the code architecture observed in previous studies, we introduce a method that allows us to maintain the mean potential-temperature profile constant throughout the precursor simulation. Furthermore, we highlight that different codes do not predict the same velocity inside the boundary layer under geostrophic forcing owing to their intrinsically different numerical dissipation. The proposed methodology allows us to reduce such spread by ensuring that inflow conditions produced from different codes feature the same hub wind and thermal stratification, regardless of the adopted precursor run time. Finally, validation of actuator line and disk models, CNBL evolution, and velocity profiles inside a periodic wind farm is also presented to assess TOSCA’s ability to model large-scale wind farm flows accurately and with high parallel efficiency.\u0000","PeriodicalId":509667,"journal":{"name":"Wind Energy Science","volume":"32 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139864043","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}
Sebastiano Stipa, Arjun Ajay, D. Allaerts, J. Brinkerhoff
Abstract. The growing number and growing size of wind energy projects coupled with the rapid growth in high-performance computing technology are driving researchers toward conducting large-scale simulations of the flow field surrounding entire wind farms. This requires highly parallel-efficient tools, given the large number of degrees of freedom involved in such simulations, and yields valuable insights into farm-scale physical phenomena, such as gravity wave interaction with the wind farm and farm–farm wake interactions. In the current study, we introduce the open-source, finite-volume, large-eddy simulation (LES) code TOSCA (Toolbox fOr Stratified Convective Atmospheres) and demonstrate its capabilities by simulating the flow around a finite-size wind farm immersed in a shallow, conventionally neutral boundary layer (CNBL), ultimately assessing gravity-wave-induced blockage effects. Turbulent inflow conditions are generated using a new hybrid off-line–concurrent-precursor method. Velocity is forced with a novel pressure controller that allows us to prescribe a desired average hub-height wind speed while avoiding inertial oscillations above the atmospheric boundary layer (ABL) caused by the Coriolis force, a known problem in wind farm LES studies. Moreover, to eliminate the dependency of the potential-temperature profile evolution on the code architecture observed in previous studies, we introduce a method that allows us to maintain the mean potential-temperature profile constant throughout the precursor simulation. Furthermore, we highlight that different codes do not predict the same velocity inside the boundary layer under geostrophic forcing owing to their intrinsically different numerical dissipation. The proposed methodology allows us to reduce such spread by ensuring that inflow conditions produced from different codes feature the same hub wind and thermal stratification, regardless of the adopted precursor run time. Finally, validation of actuator line and disk models, CNBL evolution, and velocity profiles inside a periodic wind farm is also presented to assess TOSCA’s ability to model large-scale wind farm flows accurately and with high parallel efficiency.
摘要风能项目的数量和规模不断增加，加上高性能计算技术的快速发展，促使研究人员对整个风电场周围的流场进行大规模模拟。由于此类模拟涉及大量自由度，因此需要高度并行高效的工具，并能对风电场规模的物理现象（如重力波与风电场的相互作用以及风电场与风电场之间的尾流相互作用）提出有价值的见解。在当前的研究中，我们介绍了开源、有限体积、大涡模拟（LES）代码 TOSCA（Toolbox fOr Stratified Convective Atmospheres，分层对流大气工具箱），并通过模拟浸没在浅层常规中性边界层（CNBL）中的有限尺寸风电场周围的流动来展示其功能，最终评估重力波引起的阻塞效应。湍流流入条件是通过一种新的混合离线-共流-前导方法产生的。速度是通过新型压力控制器强制产生的，该控制器允许我们设定所需的轮毂高度平均风速，同时避免科里奥利力引起的大气边界层（ABL）上方的惯性振荡，这是风电场 LES 研究中的一个已知问题。此外，为了消除以往研究中观察到的势温剖面演变对代码结构的依赖性，我们引入了一种方法，使我们能够在整个前兆模拟过程中保持平均势温剖面不变。此外，我们还强调，不同的代码由于其内在的数值耗散不同，在地营强迫下预测的边界层内速度也不相同。所提出的方法使我们能够减少这种差异，确保不同代码产生的流入条件具有相同的枢纽风和热分层，而不管所采用的前兆运行时间。最后，我们还介绍了对推杆线和盘模型、CNBL 演变以及周期性风电场内部速度剖面的验证，以评估 TOSCA 以高并行效率准确模拟大规模风电场流动的能力。
{"title":"TOSCA – an open-source, finite-volume, large-eddy simulation (LES) environment for wind farm flows","authors":"Sebastiano Stipa, Arjun Ajay, D. Allaerts, J. Brinkerhoff","doi":"10.5194/wes-9-297-2024","DOIUrl":"https://doi.org/10.5194/wes-9-297-2024","url":null,"abstract":"Abstract. The growing number and growing size of wind energy projects coupled with the rapid growth in high-performance computing technology are driving researchers toward conducting large-scale simulations of the flow field surrounding entire wind farms. This requires highly parallel-efficient tools, given the large number of degrees of freedom involved in such simulations, and yields valuable insights into farm-scale physical phenomena, such as gravity wave interaction with the wind farm and farm–farm wake interactions. In the current study, we introduce the open-source, finite-volume, large-eddy simulation (LES) code TOSCA (Toolbox fOr Stratified Convective Atmospheres) and demonstrate its capabilities by simulating the flow around a finite-size wind farm immersed in a shallow, conventionally neutral boundary layer (CNBL), ultimately assessing gravity-wave-induced blockage effects. Turbulent inflow conditions are generated using a new hybrid off-line–concurrent-precursor method. Velocity is forced with a novel pressure controller that allows us to prescribe a desired average hub-height wind speed while avoiding inertial oscillations above the atmospheric boundary layer (ABL) caused by the Coriolis force, a known problem in wind farm LES studies. Moreover, to eliminate the dependency of the potential-temperature profile evolution on the code architecture observed in previous studies, we introduce a method that allows us to maintain the mean potential-temperature profile constant throughout the precursor simulation. Furthermore, we highlight that different codes do not predict the same velocity inside the boundary layer under geostrophic forcing owing to their intrinsically different numerical dissipation. The proposed methodology allows us to reduce such spread by ensuring that inflow conditions produced from different codes feature the same hub wind and thermal stratification, regardless of the adopted precursor run time. Finally, validation of actuator line and disk models, CNBL evolution, and velocity profiles inside a periodic wind farm is also presented to assess TOSCA’s ability to model large-scale wind farm flows accurately and with high parallel efficiency.\u0000","PeriodicalId":509667,"journal":{"name":"Wind Energy Science","volume":"11 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139804032","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}
Thorsten Reichartz, Georg Jacobs, Tom Rathmes, Lucas Blickwedel, R. Schelenz
Abstract. Storing energy is a major challenge in achieving a 100 % renewable energy system. One promising approach is the production of green hydrogen from wind power. This work proposes a method for optimizing the design of wind–hydrogen systems for existing onshore wind farms in order to achieve the lowest possible levelized cost of hydrogen (LCoH). This is done by the application of a novel Python-based optimization model that iteratively determines the optimal electrolyzer position and distribution mode of hydrogen for given wind farm layouts. The model includes the costs of all required infrastructure components. It considers peripheral factors such as existing and new roads, necessary power cables and pipelines, wage and fuel costs for truck transportation, and the distance to the point of demand (POD). Based on the results, a decision can be made whether to distribute the hydrogen to the POD by truck or pipeline. For a 23.4 MW onshore wind farm in Germany, a minimal LCoH of EUR 4.58 kgH2-1 at an annual hydrogen production of 241.4 tH2a-1 is computed. These results are significantly affected by the position of the electrolyzer, the distribution mode, varying wind farm and electrolyzer sizes, and the distance to the POD. The influence of the ratio of electrolyzer power to wind farm power is also investigated. The ideal ratio between the rated power of the electrolyzer and the wind farm lies at around 10 %, with a resulting capacity factor of 78 % for the given case. The new model can be used by system planners and researchers to improve and accelerate the planning process for wind–hydrogen systems. Additionally, the economic efficiency, hence competitiveness, of wind–hydrogen systems is increased, which contributes to an urgently needed accelerated expansion of electrolyzers. The results of the influencing parameters on the LCoH will help to set development goals and indicate a path towards a cost-competitive green wind–hydrogen system.
{"title":"Optimal position and distribution mode for on-site hydrogen electrolyzers in onshore wind farms for a minimal levelized cost of hydrogen (LCoH)","authors":"Thorsten Reichartz, Georg Jacobs, Tom Rathmes, Lucas Blickwedel, R. Schelenz","doi":"10.5194/wes-9-281-2024","DOIUrl":"https://doi.org/10.5194/wes-9-281-2024","url":null,"abstract":"Abstract. Storing energy is a major challenge in achieving a 100 % renewable energy system. One promising approach is the production of green hydrogen from wind power. This work proposes a method for optimizing the design of wind–hydrogen systems for existing onshore wind farms in order to achieve the lowest possible levelized cost of hydrogen (LCoH). This is done by the application of a novel Python-based optimization model that iteratively determines the optimal electrolyzer position and distribution mode of hydrogen for given wind farm layouts. The model includes the costs of all required infrastructure components. It considers peripheral factors such as existing and new roads, necessary power cables and pipelines, wage and fuel costs for truck transportation, and the distance to the point of demand (POD). Based on the results, a decision can be made whether to distribute the hydrogen to the POD by truck or pipeline. For a 23.4 MW onshore wind farm in Germany, a minimal LCoH of EUR 4.58 kgH2-1 at an annual hydrogen production of 241.4 tH2a-1 is computed. These results are significantly affected by the position of the electrolyzer, the distribution mode, varying wind farm and electrolyzer sizes, and the distance to the POD. The influence of the ratio of electrolyzer power to wind farm power is also investigated. The ideal ratio between the rated power of the electrolyzer and the wind farm lies at around 10 %, with a resulting capacity factor of 78 % for the given case. The new model can be used by system planners and researchers to improve and accelerate the planning process for wind–hydrogen systems. Additionally, the economic efficiency, hence competitiveness, of wind–hydrogen systems is increased, which contributes to an urgently needed accelerated expansion of electrolyzers. The results of the influencing parameters on the LCoH will help to set development goals and indicate a path towards a cost-competitive green wind–hydrogen system.\u0000","PeriodicalId":509667,"journal":{"name":"Wind Energy Science","volume":"28 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139869004","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}
Rebecca Foody, J. Coburn, J. Aird, R. Barthelmie, S. Pryor
Abstract. A major issue in quantifying potential power generation from prospective wind energy sites is the lack of observations from heights relevant to modern wind turbines, particularly for offshore where blade tip heights are projected to increase beyond 250 m. We present analyses of uniquely detailed data sets from lidar (light detection and ranging) deployments in New York State and on two buoys in the adjacent New York Bight to examine the relative power generation potential and power quality at these on- and offshore locations. Time series of 10 min wind power production are computed from these wind speeds using the power curve from the International Energy Agency 15 MW reference wind turbine. Given the relatively close proximity of these lidar deployments, they share a common synoptic-scale meteorology and seasonal variability with lowest wind speeds in July and August. Time series of power production from the on- and offshore location are highly spatially correlated with the Spearman rank correlation coefficient dropping below 0.4 for separation distances of approximately 350 km. Hence careful planning of on- and offshore wind farms (i.e., separation of major plants by > 350 km) can be used reduce the system-wide probability of low wind energy power production. Energy density at 150 m height at the offshore buoys is more than 40 % higher, and the Weibull scale parameter is 2 m s−1 higher than at all but one of the land sites. Analyses of power production time series indicate annual energy production is almost twice as high for the two offshore locations. Further, electrical power production quality is higher from the offshore sites that exhibit a lower amplitude of diurnal variability, plus a lower probability of wind speeds below the cut-in and of ramp events of any magnitude. Despite this and the higher resource, the estimated levelized cost of energy (LCoE) is higher from the offshore sites mainly due to the higher infrastructure costs. Nonetheless, the projected LCoE is highly competitive from all sites considered.
{"title":"Quantitative comparison of power production and power quality onshore and offshore: a case study from the eastern United States","authors":"Rebecca Foody, J. Coburn, J. Aird, R. Barthelmie, S. Pryor","doi":"10.5194/wes-9-263-2024","DOIUrl":"https://doi.org/10.5194/wes-9-263-2024","url":null,"abstract":"Abstract. A major issue in quantifying potential power generation from prospective wind energy sites is the lack of observations from heights relevant to modern wind turbines, particularly for offshore where blade tip heights are projected to increase beyond 250 m. We present analyses of uniquely detailed data sets from lidar (light detection and ranging) deployments in New York State and on two buoys in the adjacent New York Bight to examine the relative power generation potential and power quality at these on- and offshore locations. Time series of 10 min wind power production are computed from these wind speeds using the power curve from the International Energy Agency 15 MW reference wind turbine. Given the relatively close proximity of these lidar deployments, they share a common synoptic-scale meteorology and seasonal variability with lowest wind speeds in July and August. Time series of power production from the on- and offshore location are highly spatially correlated with the Spearman rank correlation coefficient dropping below 0.4 for separation distances of approximately 350 km. Hence careful planning of on- and offshore wind farms (i.e., separation of major plants by > 350 km) can be used reduce the system-wide probability of low wind energy power production. Energy density at 150 m height at the offshore buoys is more than 40 % higher, and the Weibull scale parameter is 2 m s−1 higher than at all but one of the land sites. Analyses of power production time series indicate annual energy production is almost twice as high for the two offshore locations. Further, electrical power production quality is higher from the offshore sites that exhibit a lower amplitude of diurnal variability, plus a lower probability of wind speeds below the cut-in and of ramp events of any magnitude. Despite this and the higher resource, the estimated levelized cost of energy (LCoE) is higher from the offshore sites mainly due to the higher infrastructure costs. Nonetheless, the projected LCoE is highly competitive from all sites considered.\u0000","PeriodicalId":509667,"journal":{"name":"Wind Energy Science","volume":"413 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139831797","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}
Rebecca Foody, J. Coburn, J. Aird, R. Barthelmie, S. Pryor
Abstract. A major issue in quantifying potential power generation from prospective wind energy sites is the lack of observations from heights relevant to modern wind turbines, particularly for offshore where blade tip heights are projected to increase beyond 250 m. We present analyses of uniquely detailed data sets from lidar (light detection and ranging) deployments in New York State and on two buoys in the adjacent New York Bight to examine the relative power generation potential and power quality at these on- and offshore locations. Time series of 10 min wind power production are computed from these wind speeds using the power curve from the International Energy Agency 15 MW reference wind turbine. Given the relatively close proximity of these lidar deployments, they share a common synoptic-scale meteorology and seasonal variability with lowest wind speeds in July and August. Time series of power production from the on- and offshore location are highly spatially correlated with the Spearman rank correlation coefficient dropping below 0.4 for separation distances of approximately 350 km. Hence careful planning of on- and offshore wind farms (i.e., separation of major plants by > 350 km) can be used reduce the system-wide probability of low wind energy power production. Energy density at 150 m height at the offshore buoys is more than 40 % higher, and the Weibull scale parameter is 2 m s−1 higher than at all but one of the land sites. Analyses of power production time series indicate annual energy production is almost twice as high for the two offshore locations. Further, electrical power production quality is higher from the offshore sites that exhibit a lower amplitude of diurnal variability, plus a lower probability of wind speeds below the cut-in and of ramp events of any magnitude. Despite this and the higher resource, the estimated levelized cost of energy (LCoE) is higher from the offshore sites mainly due to the higher infrastructure costs. Nonetheless, the projected LCoE is highly competitive from all sites considered.
{"title":"Quantitative comparison of power production and power quality onshore and offshore: a case study from the eastern United States","authors":"Rebecca Foody, J. Coburn, J. Aird, R. Barthelmie, S. Pryor","doi":"10.5194/wes-9-263-2024","DOIUrl":"https://doi.org/10.5194/wes-9-263-2024","url":null,"abstract":"Abstract. A major issue in quantifying potential power generation from prospective wind energy sites is the lack of observations from heights relevant to modern wind turbines, particularly for offshore where blade tip heights are projected to increase beyond 250 m. We present analyses of uniquely detailed data sets from lidar (light detection and ranging) deployments in New York State and on two buoys in the adjacent New York Bight to examine the relative power generation potential and power quality at these on- and offshore locations. Time series of 10 min wind power production are computed from these wind speeds using the power curve from the International Energy Agency 15 MW reference wind turbine. Given the relatively close proximity of these lidar deployments, they share a common synoptic-scale meteorology and seasonal variability with lowest wind speeds in July and August. Time series of power production from the on- and offshore location are highly spatially correlated with the Spearman rank correlation coefficient dropping below 0.4 for separation distances of approximately 350 km. Hence careful planning of on- and offshore wind farms (i.e., separation of major plants by > 350 km) can be used reduce the system-wide probability of low wind energy power production. Energy density at 150 m height at the offshore buoys is more than 40 % higher, and the Weibull scale parameter is 2 m s−1 higher than at all but one of the land sites. Analyses of power production time series indicate annual energy production is almost twice as high for the two offshore locations. Further, electrical power production quality is higher from the offshore sites that exhibit a lower amplitude of diurnal variability, plus a lower probability of wind speeds below the cut-in and of ramp events of any magnitude. Despite this and the higher resource, the estimated levelized cost of energy (LCoE) is higher from the offshore sites mainly due to the higher infrastructure costs. Nonetheless, the projected LCoE is highly competitive from all sites considered.\u0000","PeriodicalId":509667,"journal":{"name":"Wind Energy Science","volume":"29 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139891813","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}
E. Simley, D. Millstein, Seongeun Jeong, Paul Fleming
Abstract. Wind farm flow control represents a category of control strategies for achieving wind-plant-level objectives, such as increasing wind plant power production and/or reducing structural loads, by mitigating the impact of wake interactions between wind turbines. Wake steering is a wind farm flow control technology in which specific turbines are misaligned with the wind to deflect their wakes away from downstream turbines, thus increasing overall wind plant power production. In addition to promising results from simulation studies, wake steering has been shown to successfully increase energy production through several recent field trials. However, to better understand the benefits of wind farm flow control strategies such as wake steering, the value of the additional energy to the electrical grid should be evaluated – for example, by considering the price of electricity when the additional energy is produced. In this study, we investigate the potential for wake steering to increase the value of wind plant energy production by combining model predictions of power gains using the FLOw Redirection and Induction in Steady State (FLORIS) engineering wind farm flow control tool with historical electricity price data for 15 existing US wind plants in four different electricity market regions. Specifically, for each wind plant, we use FLORIS to estimate power gains from wake steering for a time series of hourly wind speeds and wind directions spanning the years 2018–2020, obtained from the ERA5 reanalysis dataset. The modeled power gains are then correlated with hourly electricity prices for the nearest transmission node. Through this process we find that wake steering increases annual energy production (AEP) between 0.4 % and 1.7 %, depending on the wind plant, with average increases in potential annual revenue (i.e., annual revenue of production, ARP) 4 % higher than the AEP gains. For most wind plants, ARP gain was found to exceed AEP gain. But the ratio between ARP gain and AEP gain is greater for wind plants in regions with high wind penetration because electricity prices tend to be relatively higher during periods with below-rated wind plant power production, when wake losses occur and wake steering is active; for wind plants in the Southwest Power Pool – the region with the highest wind penetration analyzed (31 %) – the increase in ARP from wake steering is 11 % higher than the AEP gain. Consequently, we expect the value of wake steering, and other types of wind farm flow control, to increase as wind penetration continues to grow.
{"title":"The value of wake steering wind farm flow control in US energy markets","authors":"E. Simley, D. Millstein, Seongeun Jeong, Paul Fleming","doi":"10.5194/wes-9-219-2024","DOIUrl":"https://doi.org/10.5194/wes-9-219-2024","url":null,"abstract":"Abstract. Wind farm flow control represents a category of control strategies for achieving wind-plant-level objectives, such as increasing wind plant power production and/or reducing structural loads, by mitigating the impact of wake interactions between wind turbines. Wake steering is a wind farm flow control technology in which specific turbines are misaligned with the wind to deflect their wakes away from downstream turbines, thus increasing overall wind plant power production. In addition to promising results from simulation studies, wake steering has been shown to successfully increase energy production through several recent field trials. However, to better understand the benefits of wind farm flow control strategies such as wake steering, the value of the additional energy to the electrical grid should be evaluated – for example, by considering the price of electricity when the additional energy is produced. In this study, we investigate the potential for wake steering to increase the value of wind plant energy production by combining model predictions of power gains using the FLOw Redirection and Induction in Steady State (FLORIS) engineering wind farm flow control tool with historical electricity price data for 15 existing US wind plants in four different electricity market regions. Specifically, for each wind plant, we use FLORIS to estimate power gains from wake steering for a time series of hourly wind speeds and wind directions spanning the years 2018–2020, obtained from the ERA5 reanalysis dataset. The modeled power gains are then correlated with hourly electricity prices for the nearest transmission node. Through this process we find that wake steering increases annual energy production (AEP) between 0.4 % and 1.7 %, depending on the wind plant, with average increases in potential annual revenue (i.e., annual revenue of production, ARP) 4 % higher than the AEP gains. For most wind plants, ARP gain was found to exceed AEP gain. But the ratio between ARP gain and AEP gain is greater for wind plants in regions with high wind penetration because electricity prices tend to be relatively higher during periods with below-rated wind plant power production, when wake losses occur and wake steering is active; for wind plants in the Southwest Power Pool – the region with the highest wind penetration analyzed (31 %) – the increase in ARP from wake steering is 11 % higher than the AEP gain. Consequently, we expect the value of wake steering, and other types of wind farm flow control, to increase as wind penetration continues to grow.\u0000","PeriodicalId":509667,"journal":{"name":"Wind Energy Science","volume":"57 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139599374","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}
Rishabh Mishra, E. Guilmineau, I. Neunaber, C. Braud
Abstract. Wind energy systems, such as horizontal-axis wind turbines and vertical-axis wind turbines, operate within the turbulent atmospheric boundary layer, where turbulence significantly impacts their efficiency. Therefore, it is crucial to investigate the impact of turbulent inflow on the aerodynamic performance at the rotor blade scale. As field investigations are challenging, in this work, we present a framework where we combine wind tunnel measurements in turbulent flow with a digital twin of the experimental set-up. For this, first, the decay of the turbulent inflow needs to be described and simulated correctly. Here, we use Reynolds-averaged Navier–Stokes (RANS) simulations with k−ω turbulence models, where a suitable turbulence length scale is required as an inlet boundary condition. While the integral length scale is often chosen without a theoretical basis, this study derives that the Taylor micro-scale is the correct choice for simulating turbulence generated by a regular grid: the temporal decay of turbulent kinetic energy (TKE) is shown to depend on the initial value of the Taylor micro-scale by solving the differential equations given by Speziale and Bernard (1992). Further, the spatial decay of TKE and its dependence on the Taylor micro-scale at the inlet boundary are derived. With this theoretical understanding, RANS simulations with k−ω turbulence models are conducted using the Taylor micro-scale and the TKE obtained from grid experiments as the inlet boundary condition. Second, the results are validated with excellent agreement with the TKE evolution downstream of a grid obtained through hot-wire measurements in the wind tunnel. Third, the study further introduces an airfoil in both the experimental and the numerical setting where 3D simulations are performed. A very good match between force coefficients obtained from experiments and the digital twin is found. In conclusion, this study demonstrates that the Taylor micro-scale is the appropriate turbulence length scale to be used as the boundary condition and initial condition to simulate the evolution of TKE for regular-grid-generated turbulent flows. Additionally, the digital twin of the wind tunnel can accurately replicate the force coefficients obtained in the physical wind tunnel.
{"title":"Developing a digital twin framework for wind tunnel testing: validation of turbulent inflow and airfoil load applications","authors":"Rishabh Mishra, E. Guilmineau, I. Neunaber, C. Braud","doi":"10.5194/wes-9-235-2024","DOIUrl":"https://doi.org/10.5194/wes-9-235-2024","url":null,"abstract":"Abstract. Wind energy systems, such as horizontal-axis wind turbines and vertical-axis wind turbines, operate within the turbulent atmospheric boundary layer, where turbulence significantly impacts their efficiency. Therefore, it is crucial to investigate the impact of turbulent inflow on the aerodynamic performance at the rotor blade scale. As field investigations are challenging, in this work, we present a framework where we combine wind tunnel measurements in turbulent flow with a digital twin of the experimental set-up. For this, first, the decay of the turbulent inflow needs to be described and simulated correctly. Here, we use Reynolds-averaged Navier–Stokes (RANS) simulations with k−ω turbulence models, where a suitable turbulence length scale is required as an inlet boundary condition. While the integral length scale is often chosen without a theoretical basis, this study derives that the Taylor micro-scale is the correct choice for simulating turbulence generated by a regular grid: the temporal decay of turbulent kinetic energy (TKE) is shown to depend on the initial value of the Taylor micro-scale by solving the differential equations given by Speziale and Bernard (1992). Further, the spatial decay of TKE and its dependence on the Taylor micro-scale at the inlet boundary are derived. With this theoretical understanding, RANS simulations with k−ω turbulence models are conducted using the Taylor micro-scale and the TKE obtained from grid experiments as the inlet boundary condition. Second, the results are validated with excellent agreement with the TKE evolution downstream of a grid obtained through hot-wire measurements in the wind tunnel. Third, the study further introduces an airfoil in both the experimental and the numerical setting where 3D simulations are performed. A very good match between force coefficients obtained from experiments and the digital twin is found. In conclusion, this study demonstrates that the Taylor micro-scale is the appropriate turbulence length scale to be used as the boundary condition and initial condition to simulate the evolution of TKE for regular-grid-generated turbulent flows. Additionally, the digital twin of the wind tunnel can accurately replicate the force coefficients obtained in the physical wind tunnel.\u0000","PeriodicalId":509667,"journal":{"name":"Wind Energy Science","volume":"39 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139601047","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}
Leo Höning, L. J. Lukassen, B. Stoevesandt, I. Herráez
Abstract. High-fidelity computational fluid dynamics (CFD) simulations of the National Renewable Energy Laboratory (NREL) 5 MW wind turbine rotor are performed, comparing the aerodynamic behavior of flexible and rigid blades with respect to local blade quantities as well as the wake properties. The main focus has been set on rotational periodic quantities of blade loading and fluid velocity magnitudes in relation with the blade tip vortex trajectories describing the development of those quantities in the near wake. The results show that the turbine loading in a quasi-steady flow field is mainly influenced by blade deflections due to gravitation. Deforming blades change the aerodynamic behavior, which in turn influences the surrounding flow field, leading to non-uniform wake characteristics with respect to speed and shape.
{"title":"Influence of rotor blade flexibility on the near-wake behavior of the NREL 5 MW wind turbine","authors":"Leo Höning, L. J. Lukassen, B. Stoevesandt, I. Herráez","doi":"10.5194/wes-9-203-2024","DOIUrl":"https://doi.org/10.5194/wes-9-203-2024","url":null,"abstract":"Abstract. High-fidelity computational fluid dynamics (CFD) simulations of the National Renewable Energy Laboratory (NREL) 5 MW wind turbine rotor are performed, comparing the aerodynamic behavior of flexible and rigid blades with respect to local blade quantities as well as the wake properties. The main focus has been set on rotational periodic quantities of blade loading and fluid velocity magnitudes in relation with the blade tip vortex trajectories describing the development of those quantities in the near wake. The results show that the turbine loading in a quasi-steady flow field is mainly influenced by blade deflections due to gravitation. Deforming blades change the aerodynamic behavior, which in turn influences the surrounding flow field, leading to non-uniform wake characteristics with respect to speed and shape.\u0000","PeriodicalId":509667,"journal":{"name":"Wind Energy Science","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139609491","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}
Abstract. Active trailing edge flap (AFlap) systems have shown promising results in reducing wind turbine (WT) loads. The design of WTs relying on AFlap load reduction requires implementing systems to detect, monitor, and quantify any potential fault or performance degradation of the flap system to avoid jeopardizing the wind turbine's safety and performance. Currently, flap fault detection or monitoring systems are yet to be developed. This paper presents two approaches based on machine learning to diagnose the health state of an AFlap system. Both approaches rely only on the sensors commonly available on commercial WTs, avoiding the need and the cost of additional measurement systems. The first approach combines manual feature engineering with a random forest classifier. The second approach relies on random convolutional kernels to create the feature vectors. The study shows that the first method is reliable in classifying all the investigated combinations of AFlap health states in the case of asymmetrical flap faults not only when the WT operates in normal power production but also before startup. Instead, the second method can identify some of the AFlap health states for both asymmetrical and symmetrical faults when the WT is in normal power production. These results contribute to developing the systems for detecting and monitoring active flap faults, which are paramount for the safe and reliable integration of active flap technology in future wind turbine design.
{"title":"Active trailing edge flap system fault detection via machine learning","authors":"Andrea Gamberini, Imad Abdallah","doi":"10.5194/wes-9-181-2024","DOIUrl":"https://doi.org/10.5194/wes-9-181-2024","url":null,"abstract":"Abstract. Active trailing edge flap (AFlap) systems have shown promising results in reducing wind turbine (WT) loads. The design of WTs relying on AFlap load reduction requires implementing systems to detect, monitor, and quantify any potential fault or performance degradation of the flap system to avoid jeopardizing the wind turbine's safety and performance. Currently, flap fault detection or monitoring systems are yet to be developed. This paper presents two approaches based on machine learning to diagnose the health state of an AFlap system. Both approaches rely only on the sensors commonly available on commercial WTs, avoiding the need and the cost of additional measurement systems. The first approach combines manual feature engineering with a random forest classifier. The second approach relies on random convolutional kernels to create the feature vectors. The study shows that the first method is reliable in classifying all the investigated combinations of AFlap health states in the case of asymmetrical flap faults not only when the WT operates in normal power production but also before startup. Instead, the second method can identify some of the AFlap health states for both asymmetrical and symmetrical faults when the WT is in normal power production. These results contribute to developing the systems for detecting and monitoring active flap faults, which are paramount for the safe and reliable integration of active flap technology in future wind turbine design.\u0000","PeriodicalId":509667,"journal":{"name":"Wind Energy Science","volume":"23 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139608889","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}
Abstract. This work aims to develop an analytical model for the streamwise velocity and turbulence in the wake of a wind turbine where the expansion and the meandering are taken into account independently. The velocity and turbulence breakdown equations presented in the companion paper are simplified and resolved analytically, using shape functions chosen in the moving frame of reference. This methodology allows us to propose a physically based model for the added turbulence and thus to have a better interpretation of the physical phenomena at stake, in particular when it comes to wakes in a non-neutral atmosphere. Five input parameters are used: the widths (in vertical and horizontal directions) of the non-meandering wake, the standard deviation of wake meandering (in both directions) and a modified mixing length. Two calibrations for these parameters are proposed: one if the users have access to velocity time series and the other if they do not. The results are tested on a neutral and an unstable large-eddy simulation (LES) that were both computed with Meso-NH. The model shows good results for the streamwise velocity in both directions and can accurately predict modifications due to atmospheric instability. For the axial turbulence, the model misses the maximum turbulence at the top tip in the neutral case, and the proposed calibrations lead to an overestimation in the unstable case. However, the model shows encouraging behaviour as it can predict a modification of the shape function (from bimodal to unimodal) as instability and thus meandering increases.
{"title":"Breakdown of the velocity and turbulence in the wake of a wind turbine – Part 2: Analytical modelling","authors":"Erwan Jézéquel, Frederic Blondel, Valery Masson","doi":"10.5194/wes-9-119-2024","DOIUrl":"https://doi.org/10.5194/wes-9-119-2024","url":null,"abstract":"Abstract. This work aims to develop an analytical model for the streamwise velocity and turbulence in the wake of a wind turbine where the expansion and the meandering are taken into account independently. The velocity and turbulence breakdown equations presented in the companion paper are simplified and resolved analytically, using shape functions chosen in the moving frame of reference. This methodology allows us to propose a physically based model for the added turbulence and thus to have a better interpretation of the physical phenomena at stake, in particular when it comes to wakes in a non-neutral atmosphere. Five input parameters are used: the widths (in vertical and horizontal directions) of the non-meandering wake, the standard deviation of wake meandering (in both directions) and a modified mixing length. Two calibrations for these parameters are proposed: one if the users have access to velocity time series and the other if they do not. The results are tested on a neutral and an unstable large-eddy simulation (LES) that were both computed with Meso-NH. The model shows good results for the streamwise velocity in both directions and can accurately predict modifications due to atmospheric instability. For the axial turbulence, the model misses the maximum turbulence at the top tip in the neutral case, and the proposed calibrations lead to an overestimation in the unstable case. However, the model shows encouraging behaviour as it can predict a modification of the shape function (from bimodal to unimodal) as instability and thus meandering increases.\u0000","PeriodicalId":509667,"journal":{"name":"Wind Energy Science","volume":"112 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139614173","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}
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