Hub Height Research Articles

Wind Shear Model Considering Atmospheric Stability to Improve Accuracy of Wind Resource Assessment

An accurate wind shear model is an important prerequisite in extrapolating the wind resource from lower heights to the increasing hub height of wind turbines. Based on the 1-year dataset (collected in 2014) consisting of 15-minute intervals collected at heights of 2, 10, 50, 100, and 150 m on an anemometer tower in northern China, the present study focuses on the time-varying relationship between the wind shear coefficient (WSC) and atmospheric stability and proposes a wind shear model considering atmospheric stability. Through the relationship between Monin–Obukhov (M-O) length and gradient Richardson number, the M-O length is directly calculated by wind data, and the WSC is calculated by combining the Panofsky and Dutton (PD) models, which enhances the engineering practicability of the model. Then, the performance of the model is quantified and compared with two alternative methods: the use of annual average WSC and the use of stability change WSC extrapolation. The analysis demonstrates that the proposed model outperforms the other approaches in terms of normal root mean square error (NRMSE) and normal bias (NB). More specifically, this method reduces the NRMSE and NB by 24–29% and 76–95%, respectively. Meanwhile, it reaches the highest extrapolation accuracy under unstable and stable atmospheric conditions. The results are verified using the Weibull distribution.

Impact of meteorological data factors and material characterization method on the predictions of leading edge erosion of wind turbine blades

Leading edge erosion of wind turbine blades is a major contributor to wind farm energy yield losses and maintenance costs. Presented is a multidisciplinary framework for predicting rain erosion lifetimes of wind turbine blades. Key aim is assessing the sensitivity of lifetime predictions to: modeling aspects (material erosion model, blade aerodynamics), input data and/or their preprocessing (joint frequency distribution of wind speed and droplet size based on synchronous site-specific measurements versus frequency distribution generated with partly site-agnostic modeling standards, wind speed records of nacelle anemometer or extrapolated at hub height from met masts), and environmental conditions (UV weathering). The analyses consider a Northwest England onshore site where a utility-scale turbine is operational, focus on a reference 5 MW turbine assumed operational at the site, and use a typical leading edge coating material. It is found that the largest variations in erosion lifetime predictions are due to material erosion model (based on rain erosion test data or fundamental material properties) and wind and rain model (measurement-based joint wind speed and droplet size distribution or standard-based modeled distribution). The use of joint wind and rain distribution also enables identifying wind/rain states with highest erosion potential, knowledge paramount to deploying erosion-safe turbine control.

Future offshore wind energy evolution in the Bay of Biscay

A relevant point near the Biscay Marine Energy Platform (BiMEP) has been chosen and data from the ERA5 reanalysis and CMIP6 datasets in future SSP5-85 scenario (wind and wave data) have been used to analyse the energy potential and its trends from 2015 to 2100 for wind energy. A 15 MW wind turbine has been chosen in line with current technological state-of-the-art and needs, and its energy production has been computed considering the corresponding power curve, and the wind speed at its hub height based on the variation of sea roughness due to the sea state (wave height and period). The results, although most of them have not been relevant, have shown a slightly downward trend mostly on winter in all parameters analysed: mean wave period (Tm), significant wave height (Hs), corresponding sea roughness (z0), and wind speed at hub height (Ux). The 15MW wind turbine simulation has therefore shown a small decrease in energy production.

Modelling wind farm effects in HARMONIE–AROME (cycle 43.2.2) – Part 1: Implementation and evaluation

Abstract. With increasing number and proximity of wind farms, it becomes crucial to consider wind farm effects (WFEs) in the numerical weather prediction (NWP) models used to forecast power production. Furthermore, these WFEs are also expected to affect other weather-related parameters at least locally. Thus, we implement the explicit wake parameterization (EWP) in the NWP model HARMONIE–AROME (hereafter HARMONIE) along-side the existing wind farm parameterization (WFP) by Fitch et al. (2012) (FITCH). We evaluate and compare the two WFPs against research flight measurements as well as against similar simulations performed with the Weather Research and Forecasting (WRF) model using case studies. The case studies include a case for WFEs above a wind farm as well as two cases for WFEs at hub height in the wake of farms. The results show that EWP and FITCH have been correctly implemented in HARMONIE. For the simulated cases, EWP underestimates the WFEs on wind speed and strongly underestimates the effect on turbulent kinetic energy (TKE). FITCH agrees better with the observations, and WFEs on TKE are particularly well captured by HARMONIE–FITCH. After this successful evaluation, simulations with all wind turbines in Europe will be performed with HARMONIE and presented in the second part of this paper series.

Repowering feasibility of Indian wind energy sector: A case study

Repowering wind farms entails upgrading or replacing old turbines with more efficient, capable, and profitable ones. This technique has the potential to boost energy production, improve grid integration, and lower operational costs. Repowering also makes use of the most recent breakthroughs in wind energy technology, ensuring that wind farms stay economically viable and contribute to the growth of renewable energy. The majority of the wind farms that are located in India were constructed in early 2000, and their capacity ratings range from 200 kW to 800 kW. The lifespans of these wind farms have finally come to an end. Repowering wind farms is a viable alternative in these regions due to the significant wind capacity that exists there. In order to find the wind potential in the site wind resource assessment is needed. This paper proposes a repowering scheme for the existing wind farm located in Kayathar, Tamil Nadu. The reduction in power loss due to the wake effect in the existing wind farm is analyzed using WAsP software and repowering scheme is proposed to increase the Annual Energy Production (AEP), Capacity Utilisation Factor (CUF). The Wind Atlas Analysis and Application Program (WAsP) is utilized in order to carry out the site’s wind resource evaluation. After the wind resource assessment, individual turbine wake loss is identified, and the viability of repowering the wind farm by raising the hub height of high wake-affected turbines was investigated. Another repowering study is also carried out by installing high-capacity turbines in place of the existing turbines.

Time-domain fatigue damage assessment for wind turbine tower bolts under yaw optimization control at offshore wind farm

Yaw optimization control is recognized as the most effective active wake control strategy for enhancing the overall power generation of wind farms. However, the potential adverse effects of yaw optimization control on the fatigue life of wind turbines remain unclear. This study examined the effect of yaw optimization control on the fatigue life of offshore wind turbines using tower bolts. Initially, the yaw misalignment of the wind turbines was optimized using the open-source FLORIS package to maximize wind farm power generation. Subsequently, the time-varying load of the wind turbines was obtained through the OpenFAST software, using the optimal yaw misalignment, wind speed, and turbulence intensity at the hub height as inputs. The fatigue life of the wind turbines was then calculated by integrating the Schmidt–Neuper engineering stress algorithm, rain flow counting method, and Miner-Palmgren fatigue damage accumulation theory. Finally, a case study of the Horns Rev I large-scale offshore wind farm, comprising 80 wind turbines, revealed that yaw optimization control could significantly enhance the annual power generation of the wind farm by approximately 1.818%. However, optimization comes at the cost of reducing the fatigue life of wind turbine tower bolts by approximately 3–9 years.

Machine learning methods to improve spatial predictions of coastal wind speed profiles and low-level jets using single-level ERA5 data

Abstract. Observations of the wind speed at heights relevant for wind power are sparse, especially offshore, but with emerging aid from advanced statistical methods, it may be possible to derive information regarding wind profiles using surface observations. In this study, two machine learning (ML) methods are developed for predictions of (1) coastal wind speed profiles and (2) low-level jets (LLJs) at three locations of high relevance to offshore wind energy deployment: the US Northeastern Atlantic Coastal Zone, the North Sea, and the Baltic Sea. The ML models are trained on multiple years of lidar profiles and utilize single-level ERA5 variables as input. The models output spatial predictions of coastal wind speed profiles and LLJ occurrence. A suite of nine ERA5 variables are considered for use in the study due to their physics-based relevance in coastal wind speed profile genesis and the possibility to observe these variables in real-time via measurements. The wind speed at 10 ma.s.l. and the surface sensible heat flux are shown to have the highest importance for both wind speed profile and LLJ predictions. Wind speed profile predictions output by the ML models exhibit similar root mean squared error (RMSE) with respect to observations as is found for ERA5 output. At typical hub heights, the ML models show lower RMSE than ERA5 indicating approximately 5 % RMSE reduction. LLJ identification scores are evaluated using the symmetric extremal dependence index (SEDI). LLJ predictions from the ML models outperform predictions from ERA5, demonstrating markedly higher SEDIs. However, optimization utilizing the SEDI results in a higher number of false alarms when compared to ERA5.

HyDesign: a tool for sizing optimization of grid-connected hybrid power plants including wind, solar photovoltaic, and lithium-ion batteries

Abstract. Hybrid renewable power plants consisting of collocated wind, solar photovoltaic (PV), and lithium-ion battery storage connected behind a single grid connection can provide additional value to the owners and society in comparison to individual technology plants, such as those that are only wind or only PV. The hybrid power plants considered in this article are connected to the grid and share electrical infrastructure costs across different generation and storing technologies. In this article, we propose a methodology for sizing hybrid power plants as a nested-optimization problem: with an outer sizing optimization and an internal operation optimization. The outer sizing optimization maximizes the net present values over capital expenditures and compares it with standard designs that minimize the levelized cost of energy. The sizing problem formulation includes turbine selection (in terms of rated power, specific power, and hub height), a wind plant wake loss surrogate, simplified wind and PV degradation models, battery degradation, and operation optimization of an internal energy management system. The problem of outer sizing optimization is solved using a new parallel “efficient global optimization” algorithm. This new algorithm is a surrogate-based optimization method that ensures a minimal number of model evaluations but ensures a global scope in the optimization. The methodology presented in this article is available in an open-source tool called HyDesign. The hybrid sizing algorithm is applied for a peak power plant use case at different locations in India where renewable energy auctions impose a monetary penalty when energy is not supplied at peak hours. We compare the hybrid power plant sizing results when using two different objective functions: the levelized cost of energy (LCoE) or the relative net present value with respect to the total capital expenditure costs (NPV/CH). Battery storage is installed only on NPV/CH-based designs, while the hybrid design, including wind, solar, and battery, only occurs on the site with good wind resources. Wind turbine selection on this site prioritizes cheaper turbines with a lower hub height and lower rated power. The number of batteries replaced changes at the different sites, ranging between two or three units over the lifetime. A significant oversizing of the generation in comparison to the grid connection occurs on all NPV/CH-based designs. As expected LCoE-based designs are a single technology with no batteries.

Relevance Of Era5 Reanalysis For Wind Energy Applications: Comparison With Sodar Observations

ERA5 reanalysis is one of the most trusted climate data sources for wind energy modeling. However, any reanalysis should be verified through comparison with observational data to detect biases before further use. For wind verification at heights close to typical wind turbine hub heights (i.e. about 100 m), it is preferable to use either in-situ measurements from meteorological towers or remote sensing data like acoustic and laser vertical profilers, which remain independent of reanalysis. In this study, we validated the wind speed data from ERA5 at a height of 100 m using data from four sodars (acoustic profilers) located in different climatic and natural vegetation zones across European Russia. The assessments revealed a systematic error at most stations; in general, ERA5 tends to overestimate wind speed over forests and underestimate it over grasslands and deserts. As anticipated, the largest errors were observed at a station on the mountain coast, where the relative wind speed error reached 45%. We performed the bias correction which reduced absolute errors and eliminated the error dependence on the daily course, which was crucial for wind energy modeling. Without bias correction, the error in the wind power capacity factor ranged from 30 to 50%. Hence, it is strongly recommended to apply correction of ERA5 for energy calculations, at least in the areas under consideration..

Comprehensive onshore wind energy assessment in Malawi based on the WRF downscaling with ERA5 reanalysis data, optimal site selection, and energy production

Malawi, despite its minimal CO2 emissions, faces huge climate impacts and a low electrification rate. Recognizing these challenges, this study presents a comprehensive and nationwide assessment of Malawi's wind energy potential, leveraging the high-resolution capabilities of the Weather Research and Forecasting (WRF) model. Unlike most site-specific studies, this study covers the entire country, factoring in different turbine hub heights. Through sensitivity analysis, the optimal configuration for the WRF model in Malawi was determined and ERA5 reanalysis data was used for initial and boundary conditions. The study's findings were validated using MERRA-2 reanalysis and actual observational datasets. A combination of GIS and the fuzzy analytic hierarchy process (AHP) was employed, integrating key factors like land use and topographical complexity to determine optimal wind farm sites. Notably, the wind suitability map uncovered significant potential for wind farm development. Furthermore, in the evaluation of a 50 MW wind farm, 80 m hub heights consistently outperformed 100 m elevations, both technically and economically. An 80 m wind farm at Site A can potentially power up to 32,931 urban and 84,475 rural households. Economically, 80 m hub heights showcased lower levelized cost of electricity (LCOE) values and faster payback periods than 100 m ones. Furthermore, these proposed wind projects can markedly reduce CO2 emissions, positioning Malawi in tandem with global sustainability aspirations. Overall, this study provides a key foundation for Malawi's renewable energy strategies, highlighting the potential for improved electrification and better climate change mitigation outcomes.

Assessing worldwide future potentials of renewable electricity generation: Installable capacity, full load hours and costs

Several studies calculate the potential of renewable energies throughout the world. However, these studies vary in the underlying assumptions and do not consider several technologies within a uniform methodology, which affects comparability. In this paper, we calculate the worldwide potentials of ground-mounted photovoltaic (PV), concentrated solar power (CSP), and wind onshore and offshore on a 6.5 by 6.5 km grid. The results consist of installable capacity, generated electricity, and specific cost of electricity generation. Our methodology combines high-resolution data for land uses, slope, and hourly weather data with a thorough analysis of different factors for wind power, like hub heights and rotor diameters, and solar power plants, like tilt angle, and temperature-related loss of efficiency. The results show that the summed-up potential capacity of ground-mounted PV surpasses the amount of CSP, wind onshore and offshore combined with 450 TW. Given the cost-potential curve, ground-mounted PV reaches 560,000 TWh at costs below 20 €/MWh, which is twice the projected worldwide primary energy supply of the World Energy Outlook 2022 for the year 2050 with 205,557 TWh. The calculated results can function as input factors for energy system models, which are an important tool to analyse the energy transition.

Ambient wind conditions impact on energy requirements of an offshore direct air capture plant

This study proposes an off-grid direct air (carbon) capture (DAC) plant installed on the deck of an offshore floating wind turbine. The main objective is to understand detailed flow characteristics and CO2 dispersion around air contactors when placed in close proximity to one another. A solid sorbent DAC design is implemented using a commercially deployed air contactor configuration and sorbent. The sorbent is assumed to undergo a temperature vacuum swing adsorption cycle. Computational fluid dynamics (CFD) is used to determine the local conditions entering each unit based on varying wind speed and angle. Two-dimensional (2D) simulations were used to determine the pressure drop through a detailed air contactor design considering the sorbents APDES-NFC, Tri-PE-MCM, MIL-101(Cr)-PEI-800, and Lewatit VP OC 106. Only APDES-NFC was explored for further analysis in the three dimensional (3D) CFD dispersion model. 3D simulations were used to model flow patterns and CO2 dispersion using passive scalars. A worst case scenario is analyzed for all DAC units in adsorption mode with fans running simultaneously. 2D simulations show an under utilization of contactor length, and quantify pressure loss curves for four common sorbents. One commercially deployed sorbent is considered for further analysis; a pressure drop of 390.62 Pa is experienced for a flow velocity of 0.73 m s−1 through a 1.5m×1.5m×1.5m contactor. Using 3D simulations, fan energy demands are computed based on flow velocities and applied pressure gradients. There is found to be a decrease in overall fan power demand as wind speed increases. High wind speeds can passively drive the adsorption process with fans shut off at certain wind directions. This occurs at an average contactor inlet velocity of 17.5 m s−1, correlating to a hub height (150 m) wind speed of 24 m s−1. Thermal energy demands are computed based on inlet CO2 concentrations entering downstream units. Thermodynamic work for desorption based on an assumed second law efficiency is compared to thermal energy for desorption computed using a simplified isotherm method, taking into account the impacts of humidity on CO2 adsorption. Going from 414.72 ppm to 300 ppm CO2 inlet concentration requires an additional 63.9 kWh (t- CO2)−1 using the 2nd law thermodynamic efficiency method and 25.9 kWh (t- CO2)−1 using the isotherm method. Contactor arrangement, wind angles, and wind speeds have a significant impact on flow patterns experienced, and resulting CO2 dispersion. High wind speeds assist in CO2 dispersion, resulting in higher inlet concentrations to downstream DAC units and decreased thermal energy requirement.

Foundations of onshore Wind turbines: current situation and trends

The commitments by the governments for the net zero emissions for the coming decades will be a huge challenge for the renewable sector all over the planet. Wind energy will play a significant role in this transition and scale-up, in both offshore and onshore, is a crucial step to move forward. The quick development of the new onshore wind turbine models in the race for the most powerful machine, the gradual increase in the hub height, and the unitary power of the new onshore wind turbines are producing modifications in different components of the turbines. This work presents a review of the onshore wind turbines foundations, especially about the different foundation design concepts available in the onshore business, the analysis of some of them and the trends.

Optimization of Non-Uniform Onshore Wind Farm Layout Using Modified Electric Charged Particles Optimization Algorithm Considering Different Terrain Characteristics

Designing an onshore wind farm layout poses several challenges, including the effects of terrain and landscape characteristics. An accurate model should be developed to obtain the optimal wind farm layout. This study introduces a novel metaheuristic algorithm called Modified Electric Charged Particles Optimization (MECPO) to maximize wind farms’ annual energy production (AEP) by considering the different terrain and landscape characteristics of the sites. Some non-uniform scenarios are applied to the optimization process to find the best combination of decision variables in the wind farm design. The study was initiated by a uniform wind farm layout optimization employing identical wind turbine hub heights and diameters. Following this, these parameters underwent further optimization based on some non-uniform scenarios, with the optimal layout from the initial uniform wind farm serving as the reference design. Three real onshore sites located in South Sulawesi, Indonesia, were selected to validate the performance of the proposed algorithm. The wind characteristics for each site were derived from WAsP CFD, accounting for the terrain and landscape effects. The results show that the non-uniform wind farm performs better than its uniform counterpart only when using varying hub heights. Considering the impacts of the terrain and landscape characteristics, it is observed that sites with a higher elevation, slope index, and roughness length exhibit a lower wake effect than those with lower ones. Moreover, the proposed algorithm, MECPO, consistently outperforms other algorithms, achieving the highest AEP across all simulations, with a 100% success rate in all eight instances. These results underscore the algorithm’s robustness and effectiveness in optimizing wind farm layouts, offering a promising avenue for advancing sustainable wind energy practices.

Growth in turbine size and technological development of modern commercial large scale wind turbines in Türkiye

In this study, in addition to the recent advances and tendencies in wind turbine technology around the world, the progress of commercial wind turbine technology installed in Turkey was also thoroughly examined. In this respect, several metrics for installed wind turbines including number of turbines, installed power capacity (MW), mean rated capacity (MW), mean rotor diameter (m), mean specific power capacity (W/m2), and mean hub height (m) have been obtained between the years of 2011 and 2019. According to the obtained results, the mean rated capacity of Turkey’s annual installed wind turbines advanced from 1.86 MW in 2011 to 3.52 MW in 2019. However, the mean specific power of yearly installed wind turbines declined from 423.7 W/m2 to 314.1 W/m2. Outcomes revealed that the growth in the size and the reduction in the specific power have contributed to the tendency of higher power outputs, and wind turbine capacity factor and power generation capacity have been on the rise in Turkey. In time, wind turbines with greater rotor diameters and hubs started to show up more observable on land. For that purpose, the proposed solution to regulate turbine visibility during site selection is the potential visibility model (PVM), which is meant to be used as an auxiliary variable.

Mesoscale modelling of North Sea wind resources with COSMO-CLM: model evaluation and impact assessment of future wind farm characteristics on cluster-scale wake losses

Abstract. As many coastal regions experience a rapid increase in offshore wind farm installations, inter-farm distances become smaller, with a tendency to install larger turbines at high capacity densities. It is, however, not clear how the wake losses in wind farm clusters depend on the characteristics and spacing of the individual wind farms. Here, we quantify this based on multiple COSMO-CLM simulations, each of which assumes a different, spatially invariant combination of the turbine type and capacity density in a projected, future wind farm layout in the North Sea. An evaluation of the modelled wind climate with mast and lidar data for the period 2008–2020 indicates that the frequency distributions of wind speed and wind direction at turbine hub height are skillfully modelled and the seasonal and inter-annual variations in wind speed are represented well. The wind farm simulations indicate that for a typical capacity density and for SW winds, inter-farm wakes can reduce the capacity factor at the inflow edge of wind farms from 59 % to between 54 % and 30 % depending on the proximity, size and number of the upwind farms. The efficiency losses due to intra- and inter-farm wakes become larger with increasing capacity density as the layout-integrated, annual capacity factor varies between 51.8 % and 38.2 % over the considered range of 3.5 to 10 MW km−2. Also, the simulated efficiency of the wind farm layout is greatly impacted by switching from 5 MW turbines to next-generation, 15 MW turbines, as the annual energy production increases by over 27 % at the same capacity density. In conclusion, our results show that the wake losses in future wind farm clusters are highly sensitive to the inter-farm distances and the capacity densities of the individual wind farms and that the evolution of turbine technology plays a crucial role in offsetting these wake losses.

Wind Farm Layout Optimization Subject to Cable Cost, Hub Height, and a Feasible 3D Gaussian Wake Model Implementation

We address the Wind Farm Layout Optimization (WFLO) problem and tackle the optimal placement of several turbines within a specific (wind farm) area by incorporating additional aspects of an economically driven target function. With this, we contribute three refinements for WFLO research: First, while many research contributions optimize the turbines’ locations subject to maximum energy production or energy efficiency, we instead pursue a strategy of maximizing a profit objective. This enables us to incorporate inner-farm wiring costs (underground cable installation). For this, we explore the impact of using MSTs (Minimum Spanning Trees) and adding junction (so-called “Steiner”) points to the terrain plane. Second, while most research focuses on finding optimal x and y coordinates (i.e., address two-dimensional turbine placement), we also optimize the turbines’ hub heights z. Third, we also provide a software implementation of the Gaussian wake model. The latter finds entrance to the open-source WFLO research framework that comes as package <strong>wflo</strong> for statistical software R. We find that taking wiring cost into account may lead to very different turbine placements, however, increasing overall profit significantly. Allowing the optimizer to vary the hub heights may have an ambiguous impact on the wind farm profit.

Diurnal variations in wind power density analysis for optimal wind energy integration in different Indian sites

Wind power is becoming increasingly crucial in the global transition to sustainable energy systems. It is rare to find studies based on diurnal variation of wind energy potential for different sites. Seasonal patterns could lead to overestimating the night-time and underestimating the daytime wind power density (WPD), resulting in load losses and higher generation costs. Present study includes Diurnal variations in WPD, that will estimate energy generation patterns more accurately. Current analysis uses hourly wind speed data collected from IMD for six coastal Indian sites spanning 1969–2007. Seasonal, monthly, and diurnal variations at 30 m and 60 m hub heights are analyzed using Weibull distribution model. The peak diurnal WPD of 714.53 W/m2 is observed in Tuticorin during July at 60 m hub height, and the lowest is found during December. The results show substantial differences between day and night WPD, emphasizing the importance of accounting for diurnal wind speed fluctuations. This study can be helpful for the proper sizing of energy storage systems to ensure uninterrupted energy supply throughout the day and night, installing hybrid energy storage systems. It can also help designing hybrid energy systems for power generation, reducing operational costs and environmental impacts.

The wake of a large wind turbine in stable atmospheric boundary layer flow, simulated in the EnFlo stratified-flow wind tunnel

The EnFlo stratified-flow wind tunnel is described, and the parameterization of stable atmospheric boundary layers in the context of wind turbines and stability classification is given, as are the scaling constraints for stratified-flow wind tunnel experiments. Wake measurements of mean velocity, Reynolds stresses, and turbulent heat flux were made for three mild stable states, including overlying inversion, and the neutral state. The depth of the atmospheric boundary layer was kept constant, and so the results show the effect of change in stability alone, without the change in scale that would also arise in full scale measurements, an advantage provided by wind tunnel experiments. The simulated boundary-layer wind-speed profile is the same in each case, its height slightly exceeding the blade-tip top height, and the velocity deficit at the turbine is also the same in each, implying a constant thrust coefficient. In the near wake the momentum deficit rises more rapidly in the stable cases, and stays higher further downstream where it is subsequently reduced by turbulent mixing. The wake grows less rapidly in the vertical direction in the stable cases, both above and below the hub height, the height above growing still less rapidly with an imposed inversion, while the height below is unaffected by the inversion. The wake width is largely unaffected by stability. Stability reduces the Reynolds stresses in the wake over and above the reduction in the undisturbed flow; there is not a simple superposition. In the lower part of the wake the stresses are not affected by the inversion, while they are in the upper part. Turbulent heat flux is increased in the bulk of the wake, more so with an inversion, but is reduced to the surface and unaffected by an inversion.

Sea breeze-driven effects on wind down-ramps: Implications for wind farms along the north-east coast of Brazil

This research focuses on understanding a meteorological phenomenon that is related to operational disruptions in a wind power plant on the North-East coast of Brazil. The study employed enhanced weather observations based on SODAR and LIDAR wind profilers, micrometeorological masts, ERA-5 reanalysis data, wind power production data from Brazilian power grid operators, and GOES-16 thermal and visible imagery. On August 15, 2023, Brazil experienced a widespread blackout, which was largely caused by down-ramp events that affected several wind farms at the same time. The factors leading to the phenomenon were categorized into three driven-dependent categories: i) a sharp fall in dew-point temperature, ii) the presence of a cumulus cloud line just above the coastline in the early morning, and/or iii) rainfall over the adjoining sea, providing the feasibility to nowcasting this severe wind ramp. The results derived from the conceptual model suggest that sea-breezes play a significant role in the net wind at the turbine hub-height, and often exceed the influence of large-scale flow. The net wind is considered to be caused by the interplay between trade winds and consistent breezes. It is hoped this research can provide a valuable insight into the complex interactions between local meteorological phenomena and wind energy production on the North-East coast of Brazil, and that its application has the potential to improve the operational forecasting of wind power plants.