Energy Production Of Wind Farm Research Articles

Evaluation and Improvement of the Accuracy of Reanalysis and Analysis Datasets for Wind Resource Assessment in Sudan

Wind speed datasets are used to evaluate wind resources and energy production of wind farms. In locations where measured data are not available, reanalysis and analysis datasets can be used as an alternative to assess wind resources. This study evaluated the accuracy of wind speed data collected from reanalysis and analysis datasets against mast-measured data between 1975 and 1985 in Sudan, using monthly statistical analyses. Three bias correction methods, based on Measure-Correlate-Predict (MCP) and Linear Adaptation (LA1 and LA2), were applied to determine the original wind speed. The results indicate that LA1 outperformed MCP and LA2. Furthermore, the Weibull distribution function was employed to analyze the wind speed characteristics. In addition, wind power density was calculated using data from different sources. The findings show that although the wind power potential of the chosen locations is not suitable for large wind turbines, wind power can still be exploited with small wind turbines. Consequently, this study introduces a wind energy roadmap to attract investors in clean energy for sustainable development in Sudan, address energy problems, and meet domestic demands. The study also identifies the most important grid datasets for assessing the country's wind potential, enhancing the accuracy of assessments for investors and policymakers.

Aerodynamic interference caused by wake effects of repowered wind farms on the annual energy production in neighboring wind farms

Worldwide, wind farms are updated due to technological advances and the end of life from different aspects: social, environmental, technical, and economical. In Brazil, by 2030, around 50 wind farms will reach the age of 20 years of operation, representing more than 600 wind turbines and a total power of around 1,000 MW. This study aims to develop a technical and economic analysis of the unaddressed topic of interference of repowering in neighboring wind farms by analyzing four wind farms in Brazil. Technical analysis of the wake is based on NOJ and BastanKhah models using the software PyWake, which is also employed to calculate the energy production of wind farms. Three wind turbine models with different hub heights and rated power were also considered. Results showed that the repowered wind turbine characteristics, micrositing, and distance between wind farms greatly influence the energy production of neighboring wind farms. The energy deficit in the existing wind farms varied from 433 MWh to 5391 MWh, indicating that, although repowering is a viable solution for wind farms reaching their end-of-life, careful technical analysis regarding the micrositing and wind turbine model should be considered to mitigate energy losses in neighboring existing wind farms.

An optimization framework for wind farm layout design using CFD-based Kriging model

Wind farm layout optimization (WFLO) seeks to alleviate the wake loss and maximize wind farm power output efficiency, and is a crucial process in the design and planning of wind energy projects. Since the optimization algorithms typically require thousands of numerical evaluations of the wake effects, conventional WFLO studies are usually carried out with the low-fidelity analytical wake models, while the higher-fidelity computational-fluid-dynamics-based (CFD-based) methods are seldom used due to the excessive computational cost. In this paper, we develop a self-adaptive optimization framework for wind farm layout design using CFD-based Kriging model to maximize the annual energy production (AEP) of wind farms. This surrogate-based optimization (SBO) framework uses latin hypercube sampling to generate a group of wind farm layout samples, based on which CFD simulations with the turbines modeled as actuator disks are carried out to obtain the corresponding AEPs. This initial wind farm layout dataset is used to train the Kriging model, which is then integrated with an optimizer based on genetic algorithm (GA). As the optimization progresses, the intermediate optimal layout designs are again fed into the dataset to re-train the Kriging model. Such adaptive update of wind farm layout dataset continues until the algorithm converges to the optimal layout design. To evaluate the performance of the proposed SBO framework, we apply it to four representative wind farm cases. Compared to the conventional staggered layout along the dominant wind direction, the optimized wind farm produces significantly higher total AEP, which is more evenly distributed among the turbines. In particular, the SBO framework requires significantly smaller number of CFD calls to yield the optimal layouts that generates almost the same AEP with the direct CFD-GA method. Further analysis on the velocity fields show that the optimization framework always attempts to locate the downstream turbines away from the wakes of upstream ones along the dominant wind directions. The proposed CFD-based surrogate model provides a more accurate and flexible alternative to the conventional analytical-wake-model-based methods in WFLO tasks, and has the potential to be used for designing efficient wind farm projects.

Feasibility Study of Offshore Wind Wheel Installation in South China Sea at Malaysia

As the global demand for renewable energy continues to rise, it becomes crucial to investigate the viability of offshore wind power generation. This paper presents a comprehensive feasibility study assessing the viability of the South China Sea wind turbine installation. This study will describe the current situation of wind energy across the world and specifically discusses offshore wind farm technology. The study begins with a comprehensive literature review emphasizing the significance of wind power in mitigating climate change and reducing reliance on fossil fuels. The objectives of this are to study on the availability of offshore wind resources in Malaysia and to analyze the effect of micro-siting towards wind farm energy production and performance. This study indicates the best sites to set up offshore wind farm in Malaysia. According to the data, Mersing has a favorable availability of wind resources, which makes it the best place to put the turbines. Critical review for better understanding about the importance of micro-siting of wind farms social and economic was conducted. The micro-siting technique is helpful since it provides a methodical and well-informed way of choosing a location. It helps decision-makers strike a balance between the project's, the community's, and the environment's demands by considering various elements and tailoring the analysis to the particulars of a certain project. As technology and methodology evolve, the micro-siting approach will become more important in attaining sustainable and efficient development.

Research on a random search algorithm for wind turbine layout optimization

Wind turbine layout design has an important impact on the energy production and economic benefits of wind farms. The wind resource grid data include the realistic wind distributions of the wind farm. Combined with the Jensen wake model, it can be used to calculate the net production considering the wake effect of turbines. Based on the wind resource grid data and taking net energy production as the objective function, this paper proposes a random search algorithm for wind turbine layout optimization. The algorithm couples the random function with multiple optimization parameters and optimizes the wind turbine layout by considering restriction conditions of area and minimum turbine spacings. According to the results of the case study in an actual wind farm, the optimization processes using the proposed algorithm have high calculation efficiency and stability. The sensitivity analysis of parameters indicates that the effect of optimization calculation can be effectively improved by appropriately increasing the turbine coordinate searching range or the number of random operations within one single search.

Towards sequential sensor placements on a wind farm to maximize lifetime energy and profit

The optimal design of a wind farm which maximizes energy production depends on the spatially variable wind flow field. However, due to the complexity associated with modeling atmospheric winds, especially in the presence of complex terrain, the wind field is inherently uncertain. Uncertainty in the wind field during the design stage reduces the likelihood that the constructed wind farm achieves maximum energy production for a given number of turbines. As such, contemporary wind farms are designed through a two step process. First, experimental data collected from simultaneously constructed meteorological towers are used, along with simplified engineering flow models, to estimate the flow field. Then, the estimated flow field is used to place wind turbines to maximize an objective function, such as wind farm energy production. To increase wind farm energy production and reduce its uncertainty, we propose a farm design methodology that combines both the sensor placement and wind turbine location selection steps into a single sequential decision-making process. Future sensor placements sequentially leverage information gained from previous ones. Rather than placing sensors to attempt to reduce wind field uncertainty, we select sensor locations to maximize wind farm profit. Using profit as the objective function balances the increased revenue, from the increased energy production associated with improved wind field knowledge, against the higher costs from additional sensors. In this proof-of-concept study, we use idealized numerical case studies to demonstrate that sequential wind field sensor placement, compared to concurrent placement, improves the robustness of the resulting wind farm design to imperfect initial knowledge of the wind field. The numerical results suggest a wind farm annual energy output (lifetime profit) increase of approximately 6.55% (16.78%), equaling roughly 0.7 GWh/year (0.35 million USD) per turbine, compared to a typical, two-step wind farm design process which first places sensors concurrently then designs the farm thereafter. Our results also indicate that using a far-sighted sequential method as proposed in this paper outperforms standard metrics, such as entropy, that solely focus on wind field uncertainty minimization. These proof-of-concept findings suggest that future research should be dedicated to improving the wind farm design process through sequential decision-making under uncertainty.

A Monte Carlo simulation method for probabilistic evaluation of annual energy production of wind farm considering wind flow model and wake effect

The wind energy utilization has attracted worldwide attention. The wind farm is the most important place to convert wind energy into electric energy. The accurate estimation of annual energy production and its uncertainty involves the wind resource assessment and investment risk management for the wind farm. In order to reasonably analyze the uncertainty, a Monte Carlo simulation method is proposed to obtain the annual energy production and its uncertainty for the wind farm. The method proposed in this paper not only includes the uncertainties associated with Weibull distribution parameters, measure-correlation-prediction method, power law exponent, air density and power curve in previous studies, but also incorporates the wind flow model uncertainty and wake effect. Specifically, the method proposed in this paper extrapolates the wind speed from the meteorological mast to each wind turbine in the wind farm based on the uncertainty of the wind flow model. At the same time, the wind speed reduction on the downstream wind turbines due to the wake effect will be considered, which leads to the annual energy production and uncertainty of the whole wind farm. Also, the effectiveness of the proposed method is verified based on the numerical example. The results show that the average annual energy productions of different wind turbines in the wind farm with flat terrain are close, but there are some slight differences among turbines due to the wake effect with the maximum loss of 3.5%. It is also observed that the simulated uncertainty of annual energy production of concerned wind farm is 9.0%, which is larger than 7.48% by square root of the sum of the squares method where correlations among variables are not considered.

Wind Energy Supply Profiling and Offshore Potential in South Africa

South Africa’s energy network is under severe pressure due to low supply and overwhelming demand. With an increase in renewable energy providers, specifically wind energy, knowing how the supply can satisfy the electricity demand may relieve apprehensions. This research aims to provide insight into the wind energy supply of South Africa and question how well this supply meets the demand of South Africa. The methodology used in this work highlights the importance of access to public datasets to dispel misconceptions in the energy industry. Additionally, the work supports network planning and the arguments for increasing wind energy penetration on the South African grid. Wind profiles and the typical energy production of South African wind farms are compared to electricity demand. The geographical spacing of the operational wind farms is considered. It is observed that wind energy supply assists in the peak electricity hourly demand as well as seasonal peaks. Furthermore, South Africa’s coast is analysed to determine the offshore wind power potential, where shallow and deep waters are considered. It is observed that South Africa has a high potential for offshore wind, even after losses are applied.

Numerical Investigations on the Fluid Behavior in the Near Wake of an Experimental Wind Turbine Model in the Presence of the Nacelle

Accurate predictions of the near wake of horizontal-axis wind turbines are critical in estimating and optimizing the energy production of wind farms. Consequently, accurate aerodynamic models of an isolated wind turbine are required. In this paper, the steady-state flow around an experimental horizontal-axis wind turbine (known as the MEXICO model) is investigated using full-geometry computational fluid dynamics (CFD) simulations. The simulations are performed using Reynolds-Averaged Navier-Stokes (RANS) equations in combination with the transitional k-kl-w turbulence model. The multiple reference frame (MRF) approach is used to allow the rotation of the blades. The impacts of the nacelle and blade rotation on the induction region and near wake are highlighted. Simulation cases under attached and detached flow conditions with and without the nacelle were compared to the detailed particle image velocimetry (PIV) measurements. The axial and radial flow behaviors at the induction region have been analyzed in detail. This study attempts to highlight the nacelle effects on the near wake flow and on numerical prediction accuracy under various conditions, as well as the possible reasons for these effects. According to simulation results, the rotation of blades dominates the near wake region, and including the nacelle geometry can improve both axial and radial flow prediction accuracy by up to 15% at high wind speeds. At low wind speeds, the nacelle effects can be ignored. The presence of the nacelle has also been shown to increase flow separation at the trailing edges of the blade airfoils, increasing both root and tip vorticities. Finally, small nacelle diameters are recommended to reduce flow separation on the blades and increase the average velocity downstream of the rotor, thereby optimizing wind farm output power.

Machine Learning and Deterministic Methods for Detection Meteorological Phenomena from Ground Measurements: Application for Low-Level Jet and Sea-Breeze Identification in Northern France

This study focused on the detection of mesoscale meteorological phenomena, such as the nocturnal low-level jet (NLLJ) and sea breeze (SB), using automatic deterministic detection wavelet technique algorithms (HWTT and SWT) and the machine learning recurrent neural network (RNN) algorithm. The developed algorithms were applied for detection of NLLJ and SB events from ultrasonic anemometer measurements, performed between January 2018 and December 2019 at a nearshore experimental site in the north of France. Both algorithms identified the SB and NLLJ days successfully. The accuracy of SB event detection by the RNN algorithm attained 95%, and we identified 67 and 78 SB days in 2018 and 2019, respectively. Additionally, a total of 192 and 168 NLLJ days were found in 2018 and 2019, respectively. To demonstrate the capability of the algorithms to detect SB and NLLJ events from near-ground ultrasonic anemometer measurements, analysis of the simultaneous wind lidar measurements available for 86 days were performed. The results show a good agreement between the RNN-based detection method and the lidar observations, detecting 88% of SB. Deterministic algorithms (HWTT and SWT) detected a similar number of NLLJ events and provided high correlation (0.98) with the wind lidar measurements. The meteorological phenomena studied can significantly affect the energy production of offshore wind farms. It was found that the maximum hourly average peak power production could be to 5 times higher than that of the reference day due to higher wind speed observed during NLLJ events. During SB events, hourly average peak power production could be up to 2.5 times higher. In this respect, the developed algorithms applied for analysis, from near-ground anemometer measurements, may be helpful for monitoring and forecasting the meteorological phenomena capable of disturbing the energy production of offshore wind turbines.

Adaptive wind data normalization to improve the performance of forecasting models

Wind speed forecasting, a time series problem, plays a vital role in estimating annual wind energy production in wind farms. Calculation of wind energy helps to maintain stability between electricity production and consumption. Deep learning models are used for predicting time series data. However, as wind speed is non-stationary and irregular, pre-processing of these data is necessary to get accurate results. In this paper, static normalization techniques like min–max, z-score, and adaptive normalization are used for pre-processing wind datasets, and further, their forecasting results are compared. Adaptive normalization increases the learning rate and gives better forecasting results than static normalization. The RMSE value was reduced by 9.18% for the NREL dataset when adaptive normalization was used instead of z-score normalization and by 23.58% for the Weather dataset. The datasets used are taken from National Renewable Energy Laboratory (NREL) and Kaggle’s Dataset.

Performance analysis of wake models on the energy production of large offshore wind farms

ABSTRACT A design process for determining the impact of the simple wake model, park (WAsP) model, and the eddy-viscosity model on the energy production of large offshore wind farms is presented in this study. This process is implemented in the system advisor model simulator. Even though wind turbine components’ electrical transients and physical stress are not covered in this simulator, four large offshore wind farms are successfully simulated. It is found that the simple wake model is more suitable for controlling wake effects on large offshore wind farms than park (WAsP) and eddy-viscosity models. This model produces higher annual energy with a better capacity factor than the other two models. Furthermore, although any generator system could test these wake models’ performance, this study uses single stage low-speed and direct-drive generators. The direct-drive generator produces higher annual energy and better capacity factor on all four offshore wind farms for all three wake models. However, this performance is for a rectangular or square layout of wind turbines on the wind farms, with row orientation 0° and every other row as offset type. This study’s findings significantly optimize wake effect control and wind turbine layout design on large offshore wind farms.

Advanced Ensemble Methods Using Machine Learning and Deep Learning for One-Day-Ahead Forecasts of Electric Energy Production in Wind Farms

The ability to precisely forecast power generation for large wind farms is very important, since such generation is highly unstable and creates problems for Distribution and Transmission System Operators to properly prepare the power system for operation. Forecasts for the next 24 h play an important role in this process. They are also used in energy market transactions. Even a small improvement in the quality of these forecasts translates into more security of the system and savings for the economy. Using two wind farms for statistical analyses and forecasting considerably increases credibility of newly created effective prediction methods and formulated conclusions. In the first part of our study, we have analysed the available data to identify potentially useful explanatory variables for forecasting models with additional development of new input data based on the basic data set. We demonstrate that it is better to use Numerical Weather Prediction (NWP) point forecasts for hourly lags: −3, 2, −1, 0, 1, 2, 3 (original contribution) as input data than lags 0, 1 that are typically used. Also, we prove that it is better to use forecasts from two NWP models as input data. Ensemble, hybrid and single methods are used for predictions, including machine learning (ML) solutions like Gradient-Boosted Trees (GBT), Random Forest (RF), Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), K-Nearest Neighbours Regression (KNNR) and Support Vector Regression (SVR). Original ensemble methods, developed for researching specific implementations, have reduced errors of forecast energy generation for both wind farms as compared to single methods. Predictions by the original ensemble forecasting method, called “Ensemble Averaging Without Extremes” have the lowest normalized mean absolute error (nMAE) among all tested methods. A new, original “Additional Expert Correction” additionally reduces errors of energy generation forecasts for both wind farms. The proposed ensemble methods are also applicable to short-time generation forecasting for other renewable energy sources (RES), e.g., hydropower or photovoltaic (PV) systems.

Evaluation of the impact of active wake control techniques on ultimate loads for a 10 MW wind turbine

Abstract. Wind farm control is one of the solutions recently proposed to increase the overall energy production of a wind power plant. A generic wind farm control is typically synthesized so as to optimize the energy production of the entire wind farm by reducing the detrimental effects due to wake–turbine interactions. As a matter of fact, the performance of a farm control is typically measured by looking at the increase in the power production, properly weighted through the wind statistics. Sometimes, fatigue loads are also considered in the control optimization problem. However, an aspect which is rather overlooked in the literature on this subject is the evaluation of the impact that a farm control law has on the individual wind turbine in terms of maximum loads and dynamic response under extreme conditions. In this work, two promising wind farm controls, based on wake redirection (WR) and dynamic induction control (DIC) strategy, are evaluated at the level of a single front-row wind turbine. To do so, a two-pronged analysis is performed. Firstly, the control techniques are evaluated in terms of the related impact on some specific key performance indicators, with special emphasis on ultimate loads and maximum blade deflection. Secondarily, an optimal blade redesign process is performed with the goal of quantifying the modification in the structure of the blade entailed by a possible increase in ultimate values due to the presence of wind farm control. Such an analysis provides for an important piece of information for assessing the impact of the farm control on the cost-of-energy model.

Three-Dimensional LiDAR Wake Measurements in an Offshore Wind Farm and Comparison with Gaussian and AL Wake Models

Huaneng Rudong 300 MW offshore wind farm project is located in eastern China. The wake effect is one of the major concerns for wind farm operators, as all 70 units are plotted in ranks, and the sea surface roughness is low. This paper investigated the wake intensity by combining a field test and a numerical simulation. To carry out further yaw optimization, a Gaussian wake model was adopted. Firstly, a 3D Light Detection and Ranging device (LiDAR) was used to capture the features in both horizontal and vertical directions of the wake. It indicated that Gaussian wake model can precisely predict the characteristics under time average and steady state in the wind farm. The predicted annual energy production (AEP) of the whole wind farm by the Gaussian model is compared with the calculation result of the actuator line (AL)-based LES method, and the difference between the two methods is mostly under 10%.

Turbulence and Control of Wind Farms

The dynamics of the turbulent atmospheric boundary layer play a fundamental role in wind farm energy production, governing the velocity field that enters the farm as well as the turbulent mixing that regenerates energy for extraction at downstream rows. Understanding the dynamic interactions among turbines, wind farms, and the atmospheric boundary layer can therefore be beneficial in improving the efficiency of wind farm control approaches. Anticipated increases in the sizes of new wind farms to meet renewable energy targets will increase the importance of exploiting this understanding to advance wind farm control capabilities. This review discusses approaches for modeling and estimation of the wind farm flow field that have exploited such knowledge in closed-loop control, to varying degrees. We focus on power tracking as an example application that will be of critical importance as wind farms transition into their anticipated role as major suppliers of electricity. The discussion highlights the benefits of including the dynamics of the flow field in control and points to critical shortcomings of the current approaches.

Review of the marine environmental impact assessment reports regarding offshore wind farm

The energy production of offshore wind farms plays an important role in expanding renewable energy. However, the development of offshore wind farms faces many challenges due to its incompatibility with marine environments and its social acceptability among the local community. In this study, we reviewed the marine environmental impact assessment status of offshore wind farm development projects for 2012–2019 in South Korea. A total of nine projects were selected for this study, all of which experienced considerable conflict with local fisheries resources. To appropriately respond to the underlying challenges faced by offshore wind farm development and in order to better support decision-making for future impact assessment, our findings identified: i) a need for adequate preliminary investigation and technical examination of fisheries resources; ii) a need to assess and estimate the impact of underwater noise, vibration, and electromagnetic waves on fisheries resources during wind farm construction and operation; and iii) a need for a bottom-up approach that allows for communication with local stakeholders and policy-makers to guarantee the local acceptability of the development.

Differences in wind farm energy production based on the atmospheric stability dissipation rate: Case study of a 30 MW onshore wind farm

The physical characteristics of a wind field significantly influence the energy produced by a wind farm because they are related to the available kinetic energy in the wake region created by wind turbine generators; they also affect wake recovery. To understand the correlation between the external environment and the amount of energy produced, a study based on the interaction between the power characteristics of wind turbines and atmospheric factors is required. In this study, the distribution characteristics of the turbulence dissipation rate (ε) with the regime were analyzed after classifying atmospheric stability by regimes. In addition, the normalized power of wind turbines was analyzed, and the difference in annual energy production (AEP) with the regime was presented. The AEP showed the best value in the slightly unstable regime and the lowest value in the stable regime. Finally, considering the annual mean wind speed at the study site, a difference of approximately 5%–7% was observed between the AEP under the neutral atmospheric regime and other atmospheric regimes. Further, this study highlights the impact of atmospheric stability regimes and the turbulence dissipation rate on energy production, thereby indicating the necessity of integrating these factors to predict the AEP of wind farms.

Levelised cost of energy analysis for offshore wind farms – A case study of the New York State development

In the present work, a comprehensive numerical model was developed to predict the levelised cost of energy (LCOE) for offshore wind farms. A case study is further performed based on the potential developments at the offshore area of the New York State. In the present work, some specific local limitations in the United States are considered by following in line with the present European development experience. A ten-year historical wind data set is used to evaluate the wind farm energy production. The effects of distance to shore, rated power, life span, operation height, farm capacity and seasonal operation plan on LCOE are evaluated. An optimal site giving an LCOE of 123.4 $2018/MWh is found in this paper. In addition, a novel factor named as wind farm energy density (WFED) is suggested in the present study. It shows that when considering the limited coastal area as an issue, a large capacity wind farm may not have good performance compared with a lower capacity wind farm in terms of energy production. The 508 MW wind farm has a better WFED compared with either a 330 MW wind farm or an 800 MW wind farm under the current investigation.

Wind farm cumulative induction zone effect and the impact on energy yield estimation

The cumulative induction zone effect arising from the operation of multiple wind turbine generators and their interactions within a wind farm is investigated within the framework of computational fluid dynamics. A computational fluid dynamic model is benchmarked and validated against measured lidar data from an offshore wind farm. A range of wind farm array configurations are investigated and the results are used to advise adjustments relevant to wind farm energy production assessments. Based upon the offshore wind farm array configurations investigated, the outcomes indicate that the cumulative induction zone effect results in a reduction in the induction zone wind velocity of approximately 0.3–1.6%, with a corresponding reduction in energy yield of 0.4–1.2% depending on the wind farm configuration, the inflow wind velocity and the atmospheric stability conditions. In the case of larger wind farms, investigations to date indicate that the cumulative induction zone effect is more pronounced with the appropriate energy adjustment factor predicted to be increased by a factor of up to two. Additionally, wind farm configurations and wind speed distributions differing from those considered within this work may result in cumulative induction zone effect values outside those stated.