Wind Turbine Hub Height Research Articles

An effective sizing study on PV-wind-battery hybrid renewable energy systems

Hybrid renewable energy system is the effective and efficient way of energy harvesting in remote or isolated areas having the advantage of harnessing of multiple renewable sources at a time. Solar, wind and battery-based hybrid system is one of the most promising alternatives in this field. Sizing of the system components depend on various technical and economical specifications. The research studied the effect of solar panel tilt angle and wind turbine hub height on sizing optimization of PV wind battery-based hybrid renewable energy system using HOMER software. Through empirical method, an optimal monthly, seasonal, yearly optimal tilt angle and wind turbine hub height have been computed. These values are used in addition with various other technical and economical specifications in order to determine the optimal sizing of hybrid system. At first, three different scenarios have been considered for the study of yearly, seasonal, and monthly optimal tilt angle with an optimized hub height for the wind turbine. Then, a comparative analysis of the sizing of hybrid system at unoptimized and optimized tilt angle and hub height have also been done. Furthermore, a detailed comparative and sensitivity analysis have also been performed across different scenarios and approaches. A detailed financial study for policy planning and implementation issues with other, traditional state of the art approaches significantly demonstrates the effectiveness of the proposed method for optimal sizing of the hybrid system.

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.

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..

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.

Optimal placement of fixed hub height wind turbines in a wind farm using twin archive guided decomposition based multi-objective evolutionary algorithm

Harnessing maximum wind energy's power output and efficiency is vital to combat environmental challenges tied to conventional fossil fuels. Wind power's cost-effectiveness and emission reduction potential underscore its significance. Efficient wind farm layout plays a pivotal role, both technically and commercially. Evolutionary algorithms show their potential while solving multi-objective wind farm layout optimization problems. However, due to the large-scale nature of the problems, existing algorithms are getting trapped into local optima and fail to explore the search space. To address this, the TAG-DMOEA algorithm is upgraded with an adaptive offspring strategy (AOG) for better exploration. The proposed algorithm is employed on a wind farm layout problem with real-time data of wind speed and direction from two different locations. Unlike mixed hub heights, fixed hub heights such as 60, 67, and 78 m are adopted to conduct the case studies at two potential locations with real-time statistical data for the investigation of improved results. The results obtained by TAG-DMOEA-AOG on six cases are compared with 10 state-of-the-art algorithms. Statistical tests such as Friedman test and Wilcoxon signed rank test along with post hoc analysis (Nemenyi test) confirmed the superiority of the TAG-DMOEA-AOG on all cases of the considered multi-objective wind farm layout optimization problem.

Ensemble Bayesian Model Averaging Projections of Wind‐Speed Extremes for Wind Energy Applications Over China Under Climate Change

AbstractWind energy has grown rapidly in recent years as a measure to control carbon emissions and mitigate climate change. Extreme wind can damage wind turbines, cause losses to wind power plants, limit economic benefits of wind energy facilities, and disrupt regional grid balance. Therefore, an accurate assessment of extreme wind speeds at wind turbine hub height and their spatiotemporal variation under climate change is critical for the planning of wind energy and for guaranteeing regional energy security. In this study, the 100‐m extreme wind speeds in China are estimated using an empirical downscaling and Bayesian model averaging ensemble method with the latest ERA5 reanalysis and 20 global climate models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6). Two shared socioeconomic pathways (SSP), that is, SSP2‐4.5 and SSP5‐8.5, are considered to account for the uncertainty in anthropogenic emissions. According to the results, the highest extreme wind speeds are primarily found in Inner Mongolia, northeast China, western Tibet, and the eastern coastal region. Extreme wind speeds in central and southeastern China are projected to increase by approximately 2% in the middle (2031–2060) and the end (2071–2100) of the 21st century relative to the baseline period (1985–2014). Summer extreme wind speeds in northwestern Tibet are expected to increase by more than 9% at the end of the century. The findings of this study indicate that it is important to take the present and projected changes in local wind extremes into account when choosing locations for wind power plants and wind energy installations.

Novel exploration of hub heights on economics and Weibull distribution methods for wind power potential in Indian sites

Wind energy is a clean and practical way to create electricity. It necessitates the assessment of Wind Power Potential (WPP) and its economic analysis at different heights. In this context, this study examines WPP assessment for 62 different locations of 12 states in India from 10 m to 150 m height using six methods. The effectiveness of each method was performed through the computation of Relative Power Density Error (RPDE). The results suggested that the best method to estimate the WPP is the Novel Energy Pattern Factor Method (NEPFM) followed by the Empirical Method of Mabchour (EMM), the Empirical Method of Justus (EMJ), and the Empirical Method of Lysen (EML). A technical assessment is also made using six different wind turbine Models, through the computation of their respective capacity factors, annual power, and energy outputs. Furthermore the economic feasibility of these wind turbines gave Cost of Energy (COE) variation from 0.28 to 15.31 $/kWh at 10 m hub height of wind turbine and 150 m hub height of wind turbine COE varies from 0.10 to 3.53 $/kWh. This study is useful for industry.

Investigation of wind veer characteristics on complex terrain using ground-based lidar

The wind direction shift with height significantly influences wind turbine performance, particularly in relation to terrain conditions. In this work, wind conditions at 12 measurement heights ranging from 40 m to 200 m using a ground lidar, Windcube V2, installed on a 16 m tall building were analysed to examine the characteristics of wind veer angles in complex terrain. The measurement campaign was carried out from January 1st to December 31st, 2022, in the southeastern part of South Korea. The terrain complexity around the ground lidar system was evaluated using the ruggedness index (RIX), whose result was 14.06 percent corresponding to complex terrain. The ground lidar measurements were compared with mesoscale data, EMD-WRF South Korea, for the data accuracy check. Wind veer frequencies and wind roses were derived to identify directional shifts with height. Furthermore, diurnal, monthly, and seasonal variations of wind veer characteristics were analysed. Wind shear exponent factor (WSE) and turbulence kinetic energy (TKE) were calculated, and wind veer profiles were constructed based on these parameters. The relative errors of wind speeds were analysed for rotor equivalent wind speed (REWS) and hub height wind speed (HHWS), with REWS with wind veer correction, REWSveer, as a reference. Additionally, atmospheric stability conditions were classified using WSE and TKE, and the vertical changes in wind veering were analysed according to the stability conditions. The findings reveal lower wind speeds exhibited larger wind veer values and fluctuations. The relative errors for the REWS and the HHWS were 0.04 % and 0.20 % on average, respectively. The study demonstrates that terrain conditions significantly impacted wind veer angles at heights below 100 m, whereas the influence diminished with increasing height above 100 m. The results could be helpful for wind farm developers to make decisions on the siting as well as the hub height of wind turbines on complex terrain

Techno-economic feasibility of stand-alone hybrid energy system with battery storage in educational buildings: A case study of Uttara University

Stand-alone hybrid energy systems (HES) have the potential to significantly reduce pollutant emissions and alleviate strain on the national grid. The selection and sizing of stand-alone HES for buildings can serve as a methodological approach toward establishing a resilient and clean electrical energy system in urban areas of developing countries. To explore this further, a case study was conducted using Hybrid Optimization of Multiple Energy Resources (HOMER) to assess the feasibility of implementing a HES at Uttara University in Dhaka, Bangladesh. This study examined the technical, economic, environmental, social, and reliability aspects of a stand-alone HES. The findings of this study suggested that the PV-WT-DG-BT is the most cost-effective solution for the University when compared to other hybrid configurations. Additionally, this HES benefits the surrounding communities by creating employment opportunities and contributing to human development. The sensitivity analysis showed that the COE and NPC of the optimal HES are sensitive to fuel price changes. A PV slope angle of 23.88° was cost-effective, and a 20-meter wind turbine hub height was optimal. It was observed that increased solar radiation decreases COE. Further, sensitivity analysis showed that fuel costs, CO2 emissions, and NPC will decrease if average daily load demand declines. It was also found that 1 kWh of Li-Ion-based HES performed the best compared to other batteries. Finally, the results of the genetic algorithm (GA), and grey wolf optimizer (GWO) showed that they provide the same optimum configuration as HOMER but with the lowest NPC. The findings of this study can serve as a foundational model for designing HESs in other large educational buildings located in low and middle-income countries with environmental conditions and socio-economic characteristics similar to Bangladesh.

Interference effect on flow features and aerodynamic mechanism over the roof between two tall buildings

Interference effect of an upwind building on flow features and wind power potential over roof of principal building was investigated through numerical simulations. Flow features were examined at different wind directions and spacing ratios (d/B = 2, 3, 4, 5, and 6), and associated aerodynamic mechanisms were analyzed. Turbulence intensity was less affected by interfering building than mean velocity. d/B exerted a more important impact on mean velocity at θ = 0° and 180° than those at oblique wind directions. The isolated building had large power potential at leeward locations due to strong flow separation at θ = 0° compared with principal building. The largest skew angle βmax fluctuated greatly with roof locations (from −15° to 80°) of isolated building at θ = 0°. In comparison, βmax over principal building changed slightly with roof locations (from −16° to 3°) due to smooth flow separation and reattachment at θ = 0°. Effect of d/B on the largest wind energy and hub height of roof-mounted wind turbines depended on θ. Recommendations for the siting of HAWTs on roofs were finally provided.

Low impact siting for wind power facilities in the Southeast United States

AbstractAlthough installed wind power generation capacity in the United States reached 132 GW in 2021, more than quadruple the capacity in 2008, a noticeable void exists in the Southeast. Scant wind power development in this region is due to relatively poorer wind resources, other competitive energy sources, and political opposition. However, the dramatic increases in wind turbine hub height, which allow harvesting the faster wind speeds that occur farther from the ground, combined with a growing sense of urgency to develop renewable energy, point to a near future with significant wind development everywhere, including the Southeast. Nevertheless, the enthusiasm for replacing fossil fuels with renewable sources is tempered by fears that the vast land requirements of utility‐scale wind farms may disrupt valuable ecosystems. In this paper, we identify the areas where installed wind power capacity is least likely to disrupt wildlife and sensitive natural areas in the southeastern United States. The generated maps exclude geographic areas unsuitable for wind power development due to environmental concerns or technical considerations corresponding to five categories. The resulting geospatial product suggests that even after removing sizable areas from consideration, there is significant land for wind development to meet the Southeast's energy needs and clean energy goals.

Geographical, technical, economic, and environmental potential for wind to hydrogen production in Algeria: GIS-based approach

In this paper, wind resource availability was determined by analyzing the meteorological wind energy potential at the hub height of wind turbines. The wind resource potential was identified in specific geographic locations. Technical factors, such as siting requirements and resource availability, were taken into account in the investigation. The economic potential was calculated for each area of Algeria, considering overall losses. Subsequently, the potential for reducing fossil fuel usage was determined through wind to hydrogen estimation. The main components utilized in this work were a Vestas-90 wind turbine (V90–2.0 MW) and an alkaline electrolyzer. The analysis employed a Geographical Information System (GIS) tool. The results demonstrate Algeria's significant potential for wind to hydrogen production, estimated at 1.093 Gt/year without constraints, and reduced to 1.066 Gt/year when considering constraints. The levelized cost of wind to hydrogen production in Algeria varies between 1.51 and 15.37 US $/kg, depending on the chosen wind turbine. The wind to hydrogen production potential identified has the capacity to replace the entire current fossil fuel consumption in the transportation sector, which amounts to 5.28 Mt H2/year. The utilization of wind to hydrogen in the transport sector results in a significant reduction of CO2 emissions, with a calculated avoidance of 45.69 Mt compared to the use of gasoline and diesel. Hydrogen produced from wind energy resources also could be used to support other sectors, including residential, agricultural, and industry in Algeria.

Wind farm layout and hub height optimization with a novel wake model

This paper comprehensively investigates the impact of wind turbine layout and hub height on power generation of wind farm. Firstly, an engineering three-dimensional (3-D) wind turbine wake model is improved by the artificial neural network (ANN) technology. The novel 3-D ANN wake model reaches more than 99% accuracy of the original wake model. It can save about 80% of the computational time when predicting the downstream wind speed. Secondly, the influence of wind turbine hub height and position on the equivalent wind speed (EWS) and power is deeply studied. Specially, when reducing the hub height of the downstream wind turbine, both the wake impact from upstream turbines and EWS will decrease, so the overall influence should be assessed according to the specific situation. Finally, the problem of wind farm layout and height optimization is investigated. According to this study, simultaneously optimizing these two factors can obtain a better result than optimizing each factor individually. If economic factor is additionally considered, the optimized hub height and power output results will be quite different. Therefore, considering more factors is important to obtain an appropriate wind farm layout.

Assessment of Wind Speed Statistics in Samaria Region and Potential Energy Production

Statistical characteristics of the wind speed in the Samaria region of Israel have been analyzed by processing 11 years of wind data provided by the Israeli Meteorological Service, recorded at a 10 m height above the ground. The cumulative mean wind speed at a measurement height was shown to be 4.53 m/s with a standard deviation of 2.32 m/s. The prevailing wind direction was shown to be characterized by a cumulative mean azimuth of 226° with a standard deviation of 79.76°. The results were extrapolated to a 70 m height in order to estimate wind characteristics at the hub height of a medium-scale wind turbine. Moreover, Weibull distribution parameters were calculated annually, monthly, and seasonally, demonstrating a good match with histogram-based statistical representations. The shape parameter of the Weibull distribution was shown to reside within a narrow range of 1.93 to 2.15, allowing us to assume a Rayleigh distribution, thus simplifying wind turbine energy yield calculations. The novelty of the current paper is related to gathering wind statistics for a certain area (Samaria), and we are not aware of any published statistics regarding wind velocity and direction in this area. These data may be interesting for potential regional wind energy development in which the obtained Weibull distribution could be used in calculations for the expected power generation of particular turbines with a known power dependence on velocity. We have given an example of these calculations for three different types of turbines and obtained their yield in terms of electric power and economic value. We also point out that the fact that realistic wind velocity statistics can be described well by an analytic formula (Weibull distribution) is not trivial, and in fact, the fit may have been poor.

Wind farm layout optimisation considering commercial wind turbines using parallel reference points, radial space division and reference vector guided EA-based approach

In this study, a new multiobjective optimisation-based approach for Wind Farm Layout Optimisation (WFLO) is developed. The proposed approach combines four distinct Evolutionary algorithms (EA) based on multiobjective optimisation methods into a more powerful parallel method. The four combined algorithms are: the Radial Space Division based EA (RSEA), the Reference Points based EA (RPEA), RVEA embedded with the reference vector regeneration strategy (RVEAa) and the Reference Vector Guided EA (RVEA). The proposed approach is PRPSVEAa, where the letter P stands for parallel, and the remaining letters are used from different combined methods. In this research, four case studies have been investigated using four different commercial wind turbines where the hub heights of Wind Turbines (WT) are kept the same in some cases and varied in some. The results indicate that the Pareto set of solutions varies from each other with different numbers of WT as they spread over a wide region. This offers the designer the most suitable layout solution based on technical and economic factors. The obtained result can also be used to plan for future extensions of the research work.

Estimating hub-height wind speed based on a machine learning algorithm: implications for wind energy assessment

Abstract. Accurate estimation of wind speed at wind turbine hub height is of significance for wind energy assessment and exploitation. Nevertheless, the traditional power law method (PLM) generally estimates the hub-height wind speed by assuming a constant exponent between surface and hub-height wind speed. This inevitably leads to significant uncertainties in estimating the wind speed profile especially under unstable conditions. To minimize the uncertainties, we here use a machine learning algorithm known as random forest (RF) to estimate the wind speed at hub heights such as at 120 m (WS120), 160 m (WS160), and 200 m (WS200). These heights go beyond the traditional wind mast limit of 100–120 m. The radar wind profiler and surface synoptic observations at the Qingdao station from May 2018 to August 2020 are used as key inputs to develop the RF model. A deep analysis of the RF model construction has been performed to ensure its applicability. Afterwards, the RF model and the PLM model are used to retrieve WS120, WS160, and WS200. The comparison analyses from both RF and PLM models are performed against radiosonde wind measurements. At 120 m, the RF model shows a relatively higher correlation coefficient R of 0.93 and a smaller RMSE of 1.09 m s−1, compared with the R of 0.89 and RMSE of 1.50 m s−1 for the PLM. Notably, the metrics used to determine the performance of the model decline sharply with height for the PLM model, as opposed to the stable variation for the RF model. This suggests the RF model exhibits advantages over the traditional PLM model. This is because the RF model considers well the factors such as surface friction and heat transfer. The diurnal and seasonal variations in WS120, WS160, and WS200 from RF are then analyzed. The hourly WS120 is large during daytime from 09:00 to 16:00 local solar time (LST) and reach a peak at 14:00 LST. The seasonal WS120 is large in spring and winter and is low in summer and autumn. The diurnal and seasonal variations in WS160 and WS200 are similar to those of WS120. Finally, we investigated the absolute percentage error (APE) of wind power density between the RF and PLM models at different heights. In the vertical direction, the APE is gradually increased as the height increases. Overall, the PLM algorithm has some limitations in estimating wind speed at hub height. The RF model, which combines more observations or auxiliary data, is more suitable for the hub-height wind speed estimation. These findings obtained here have great implications for development and utilization in the wind energy industry in the future.

Evaluation of best value of wind speed for maximum wind energy output in Nigeria using neural network

The outcome of the study, evaluation of best value of wind speed for maximum wind energy output in Nigeria using neural network was successfully achieved. Wind speed for various months and elevation data gotten from NASA were prepared in excel and imported into neural network toolbox in MATLAB for training and analysis using levenberg maquardt and scaled conjugate gradient algorithms to determine the best performance value. Furthermore, training of the data using levenberg marquardt algorithm at 70% training data, 15% test data and 15% validation data respectively revealed that the best performance level was 1.9046 m/s at 7 epochs and this matches elevation of 7. 24meters. In addition, training of the same data using scaled conjugate gradient algorithm with the stated conditions above, revealed that the best performance level was 9.0376 m/s at 182 epochs and this matches elevation of 34.35meters. Also, training fit coefficients were found to be 0.99984 and 0.99804 respectively which indicated that there is a close and positive relationship between wind speed and wind turbine hub height. Results revealed that the best value of wind speed and elevation for maximum wind energy output in Nigeria ranges from 1.9046 m/s to 9.0376 m/s and 7.2 meters to 34.35 meters respectively. The researchers made the following recommendations: Wind turbine tower and blades should be designed to have wind impact speed capacity beyond the estimated values; further research can also be done in future to establish a more accurate mathematical model between wind speed and wind energy output with other advanced program for generalization, etc.

Projecting Future Energy Production from Operating Wind Farms in North America. Part I: Dynamical Downscaling

Abstract New simulations at 12-km grid spacing with the Weather and Research Forecasting (WRF) Model nested in the MPI Earth System Model (ESM) are used to quantify possible changes in wind power generation potential as a result of global warming. Annual capacity factors (CF; measures of electrical power production) computed by applying a power curve to hourly wind speeds at wind turbine hub height from this simulation are also used to illustrate the pitfalls in seeking to infer changes in wind power generation directly from low-spatial-resolution and time-averaged ESM output. WRF-derived CF are evaluated using observed daily CF from operating wind farms. The spatial correlation coefficient between modeled and observed mean CF is 0.65, and the root-mean-square error is 5.4 percentage points. Output from the MPI-WRF Model chain also captures some of the seasonal variability and the probability distribution of daily CF at operating wind farms. Projections of mean annual CF (CFA) indicate no change to 2050 in the southern Great Plains and Northeast. Interannual variability of CFA increases in the Midwest, and CFA declines by up to 2 percentage points in the northern Great Plains. The probability of wind droughts (extended periods with anomalously low production) and wind bonus periods (high production) remains unchanged over most of the eastern United States. The probability of wind bonus periods exhibits some evidence of higher values over the Midwest in the 2040s, whereas the converse is true over the northern Great Plains. Significance Statement Wind energy is playing an increasingly important role in low-carbon-emission electricity generation. It is a “weather dependent” renewable energy source, and thus changes in the global atmosphere may cause changes in regional wind power production (PP) potential. We use PP data from operating wind farms to demonstrate that regional simulations exhibit skill in capturing actual power production. Projections to the middle of this century indicate that over most of North America east of the Rocky Mountains annual expected PP is largely unchanged, as is the probability of extended periods of anomalously high or low production. Any small declines in annual PP are of much smaller magnitude than changes due to technological innovation over the last two decades.

Intelligent digital twin – machine learning system for real-time wind turbine wind speed and power generation forecasting

Wind power is a key pillar in efforts to decarbonise energy production. However, variability in wind speed and resultant wind turbine power generation poses a challenge for power grid integration. Digital Twin (DT) technology provides intelligent service systems, combining real-time monitoring, predictive capabilities and communication technologies. Current DT research for wind turbine power generation has focused on providing wind speed and power generation predictions reliant on Supervisory Control and Data Acquisition (SCADA) sensors, with predictions often limited to the timeframe of datasets. This research looks to expand on this, utilising a novel framework for an intelligent DT system powered by k-Nearest Neighbour (kNN) regression models to upscale live wind speed forecasts to higher wind turbine hub-height and then forecast power generation. As there is no live link to a wind turbine, the framework is referred to as a “Simulated Digital Twin” (SimTwin). 2019-2020 SCADA and wind speed data are used to evaluate this, demonstrating that the method provides suitable predictions. Furthermore, full deployment of the SimTwin framework is demonstrated using live wind speed forecasts. This may prove useful for operators by reducing reliance on SCADA systems and provides a research and development tool where live data is limited.

Wind power in forested regions: Power law extrapolation vs. lidar observation

As the increasing hub height of modern wind turbine, wind shear model is generally adopted as a useful tool to extrapolate wind speed available to higher levels for wind power assessment. This paper conducts a data-driven study to examine the power law model in assessing the wind power in forested regions featured with seasonally-varied roughness. Specifically, wind speeds are observed using a laser lidar mounted in forested regions in the northeast of China, ranging from 80 m to 200 m with a constant interval of 20 m. Subsequently, comparisons are made between the wind profiles established based on the power law model and field observations. The exponents used include the empirical exponents and the fitted exponents, the former are derived from the standard codes, and the latter are fitted based on the seasonal and annual data collections. Results indicate that the power law model is unable to fully capture the vertical distribution of wind speed in forested regions, it is the exponent that plays a decisive role influencing the reliability representing the real wind profile. Compared with the empirical exponents, the seasonally-fitted exponents exhibits much better suitability for wind speed extrapolation in forested regions, as well as for the wind power assessment.