Energies

Editorial

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3 pages, 174 KiB

Open AccessEditorial

Computational Fluid Dynamic Models of Wind Turbine Wakes

by Antonio Crespo

Energies 2023, 16(4), 1772; https://doi.org/10.3390/en16041772 - 10 Feb 2023

Viewed by 942

Abstract

Wind energy is one of the main sources of renewable energy that does not contaminate and contributes significantly to the reduction of burning fossil fuels that originate global warming by creating greenhouse gasses; therefore, a significant part the electric energy produced presently is [...] Read more.

Wind energy is one of the main sources of renewable energy that does not contaminate and contributes significantly to the reduction of burning fossil fuels that originate global warming by creating greenhouse gasses; therefore, a significant part the electric energy produced presently is of wind origin, and this share is expected to become more important in the next years [...] Full article

(This article belongs to the Special Issue Wind Turbines, Wind Farms and Wind Energy)

3 pages, 166 KiB

Open AccessEditorial

Wakes of Wind Turbines in Yaw for Wind Farm Power Optimization

by Antonio Crespo

Energies 2022, 15(18), 6553; https://doi.org/10.3390/en15186553 - 08 Sep 2022

Cited by 2 | Viewed by 1150

Abstract

The application of wind-generated energy is increasing at a great rate, about 11% per year, with an installed capacity of 837 GW in 2021, and it is the primary non-hydro renewable technology; in many countries, it is the main source of electric energy [...] Read more.

The application of wind-generated energy is increasing at a great rate, about 11% per year, with an installed capacity of 837 GW in 2021, and it is the primary non-hydro renewable technology; in many countries, it is the main source of electric energy [...] Full article

(This article belongs to the Special Issue Wind Turbines, Wind Farms and Wind Energy)

Research

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24 pages, 3372 KiB

Open AccessArticle

Parametric Curve Comparison for Modeling Floating Offshore Wind Turbine Substructures

by Adebayo Ojo, Maurizio Collu and Andrea Coraddu

Energies 2023, 16(14), 5371; https://doi.org/10.3390/en16145371 - 14 Jul 2023

Viewed by 810

Abstract

The drive for the cost reduction of floating offshore wind turbine (FOWT) systems to the levels of fixed bottom foundation turbine systems can be achieved with creative design and analysis techniques of the platform with free-form curves to save numerical simulation time and [...] Read more.

The drive for the cost reduction of floating offshore wind turbine (FOWT) systems to the levels of fixed bottom foundation turbine systems can be achieved with creative design and analysis techniques of the platform with free-form curves to save numerical simulation time and minimize the mass of steel (cost of steel) required for design. This study aims to compare four parametric free-form curves (cubic spline, B-spline, Non-Uniform Rational B-Spline and cubic Hermite spline) within a design and optimization framework using the pattern search gradient free optimization algorithm to explore and select an optimal design from the design space. The best performance free-form curve within the framework is determined using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The TOPSIS technique shows the B-spline curve as the best performing free-form curve based on the selection criteria, amongst which are design and analysis computational time, estimated mass of platform and local shape control properties. This study shows that free-form curves like B-spline can be used to expedite the design, analysis and optimization of floating platforms and potentially advance the technology beyond the current level of fixed bottom foundations. Full article

(This article belongs to the Special Issue Wind Turbines, Wind Farms and Wind Energy)

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20 pages, 5912 KiB

Open AccessArticle

Yaw Optimisation for Wind Farm Production Maximisation Based on a Dynamic Wake Model

by Zhiwen Deng, Chang Xu, Zhihong Huo, Xingxing Han and Feifei Xue

Energies 2023, 16(9), 3932; https://doi.org/10.3390/en16093932 - 06 May 2023

Cited by 3 | Viewed by 1371

Abstract

In recent years, a major focus on wind farm wake control is to maximise the production of wind farms. To improve the power generation efficiency of wind farms through wake regulation, this study investigates yaw optimisation for wind farm production maximisation from the [...] Read more.

In recent years, a major focus on wind farm wake control is to maximise the production of wind farms. To improve the power generation efficiency of wind farms through wake regulation, this study investigates yaw optimisation for wind farm production maximisation from the perspective of time-varying wakes. To this end, we first deduce a simplified dynamic wake model according to the momentum conservation theory and backward difference method. The accuracy of the proposed model is verified by simulation comparisons. Then, the time lag of wake propagation and its impact on wind farm production maximisation through wake meandering is analysed. On this basis, a yaw optimisation method for increasing wind farm energy capture is presented. This optimisation method uses the proposed dynamic wake model for wind farm prediction. The results indicate that the optimisation period is critical to the effect of the optimisation method on wind farm energy capture. Full article

(This article belongs to the Special Issue Wind Turbines, Wind Farms and Wind Energy)

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18 pages, 4441 KiB

Open AccessArticle

Utilizing the Random Forest Method for Short-Term Wind Speed Forecasting in the Coastal Area of Central Taiwan

by Cheng-Yu Ho, Ke-Sheng Cheng and Chi-Hang Ang

Energies 2023, 16(3), 1374; https://doi.org/10.3390/en16031374 - 29 Jan 2023

Cited by 4 | Viewed by 1411

Abstract

The Taiwan Strait contains a vast potential for wind energy. However, the power grid balance is challenging due to wind energy’s uncertainty and intermittent nature. Wind speed forecasting reduces this risk, increasing the penetration rate. Machine learning (ML) models are adopted in this [...] Read more.

The Taiwan Strait contains a vast potential for wind energy. However, the power grid balance is challenging due to wind energy’s uncertainty and intermittent nature. Wind speed forecasting reduces this risk, increasing the penetration rate. Machine learning (ML) models are adopted in this study for the short-term prediction of wind speed based on the complex nonlinear relationships among wind speed, terrain, air pressure, air temperature, and other weather conditions. Feature selection is crucial for ML modeling. Finding more valuable features in observations is the key to improving the accuracy of prediction models. The random forest method was selected because of its stability, interpretability, low computational cost, and immunity to noise, which helps maintain focus on investigating the essential features from vast data. In this study, several new exogenous features were found on the basis of physics and the spatiotemporal correlation of surrounding data. Apart from the conventional input features used for wind speed prediction, such as wind speed, wind direction, air pressure, and air temperature, new features were identified through the feature importance of the random forest method, including wave height, air pressure difference, air-sea temperature difference, and hours and months, representing the periodic components of time series analysis. The air–sea temperature difference is proposed to replace the wind speed difference to represent atmosphere stability due to the availability and adequate accuracy of the data. A random forest and an artificial neural network model were created to investigate the effectiveness and generality of these new features. Both models are superior to persistence models and models using only conventional features. The random forest model outperformed all models. We believe that time-consuming and tune-required sophisticated models may also benefit from these new features. Full article

(This article belongs to the Special Issue Wind Turbines, Wind Farms and Wind Energy)

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16 pages, 3203 KiB

Open AccessFeature PaperArticle

A Novel Hybrid Machine Learning Model for Wind Speed Probabilistic Forecasting

by Guanjun Liu, Chao Wang, Hui Qin, Jialong Fu and Qin Shen

Energies 2022, 15(19), 6942; https://doi.org/10.3390/en15196942 - 22 Sep 2022

Cited by 8 | Viewed by 1416

Abstract

Accurately capturing wind speed fluctuations and quantifying the uncertainties has important implications for energy planning and management. This paper proposes a novel hybrid machine learning model to solve the problem of probabilistic prediction of wind speed. The model couples the light gradient boosting [...] Read more.

Accurately capturing wind speed fluctuations and quantifying the uncertainties has important implications for energy planning and management. This paper proposes a novel hybrid machine learning model to solve the problem of probabilistic prediction of wind speed. The model couples the light gradient boosting machine (LGB) model with the Gaussian process regression (GPR) model, where the LGB model can provide high-precision deterministic wind speed prediction results, and the GPR model can provide reliable probabilistic prediction results. The proposed model was applied to predict wind speeds for a real wind farm in the United States. The eight contrasting models are compared in terms of deterministic prediction and probabilistic prediction, respectively. The experimental results show that the LGB-GPR model improves the point forecast accuracy (RMSE) by up to 20.0% and improves the probabilistic forecast reliability (CRPS) by up to 21.5% compared to a single GPR model. This research is of great significance for improving the reliability of wind speed, probabilistic predictions, and the sustainable development of new energy. Full article

(This article belongs to the Special Issue Wind Turbines, Wind Farms and Wind Energy)

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15 pages, 5826 KiB

Open AccessArticle

A Comparative Study on Wind Energy Assessment Distribution Models: A Case Study on Weibull Distribution

by Hanifa Teimourian, Mahmoud Abubakar, Melih Yildiz and Amir Teimourian

Energies 2022, 15(15), 5684; https://doi.org/10.3390/en15155684 - 05 Aug 2022

Cited by 16 | Viewed by 2009

Abstract

Wind power generation highly depends on the determination of wind power potential, which drives the design and feasibility of the wind energy production investment. This gives an important role to wind power estimation, which creates the need for an accurate wind data analysis [...] Read more.

Wind power generation highly depends on the determination of wind power potential, which drives the design and feasibility of the wind energy production investment. This gives an important role to wind power estimation, which creates the need for an accurate wind data analysis and wind energy potential assessments for a given location. Such assessments require the implementation of an accurate and suitable wind distribution model. Therefore, in the quest for a well-fitted model, eight methods for estimating the Weibull parameters are investigated in this paper. The methods were then investigated by employing statistical tools, and their performances have been discussed in terms of various error indicators such as root mean squared error (RMSE), regression error (R2), chi-square (X2), and mean absolute error (MAE). Meteorological data for diverse terrain from 14 provinces with 30 sites scattered across Iran were employed to examine the performance of the investigated methods. The results demonstrated that the empirical method has superiority over the investigated technique in terms of errors. Full article

(This article belongs to the Special Issue Wind Turbines, Wind Farms and Wind Energy)

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22 pages, 2950 KiB

Open AccessArticle

An Efficient Estimation of Wind Turbine Output Power Using Neural Networks

by Muhammad Yaqoob Javed, Iqbal Ahmed Khurshid, Aamer Bilal Asghar, Syed Tahir Hussain Rizvi, Kamal Shahid and Krzysztof Ejsmont

Energies 2022, 15(14), 5210; https://doi.org/10.3390/en15145210 - 18 Jul 2022

Cited by 2 | Viewed by 1549

Abstract

Wind energy is a valuable source of electric power as its motion can be converted into mechanical energy, and ultimately electricity. The significant variability of wind speed calls for highly robust estimation methods. In this study, the mechanical power of wind turbines (WTs) [...] Read more.

Wind energy is a valuable source of electric power as its motion can be converted into mechanical energy, and ultimately electricity. The significant variability of wind speed calls for highly robust estimation methods. In this study, the mechanical power of wind turbines (WTs) is successfully estimated using input variables such as wind speed, angular speed of WT rotor, blade pitch, and power coefficient (Cp). The feed-forward backpropagation neural networks (FFBPNNs) and recurrent neural networks (RNNs) are incorporated to perform the estimations of wind turbine output power. The estimations are performed based on diverse parameters including the number of hidden layers, learning rates, and activation functions. The networks are trained using a scaled conjugate gradient (SCG) algorithm and evaluated in terms of the root mean square error (RMSE) and mean absolute percentage error (MAPE) indices. FFBPNN shows better results in terms of RMSE (0.49%) and MAPE (1.33%) using two and three hidden layers, respectively. The study indicates the significance of optimal selection of input parameters and effects of changing several hidden layers, activation functions, and learning rates to achieve the best performance of FFBPNN and RNN. Full article

(This article belongs to the Special Issue Wind Turbines, Wind Farms and Wind Energy)

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27 pages, 9411 KiB

Open AccessArticle

Improvement of Airflow Simulation by Refining the Inflow Wind Direction and Applying Atmospheric Stability for Onshore and Offshore Wind Farms Affected by Topography

by Susumu Takakuwa and Takanori Uchida

Energies 2022, 15(14), 5050; https://doi.org/10.3390/en15145050 - 11 Jul 2022

Viewed by 1298

Abstract

For this study, the annual frequency of atmospheric stability and the effects of topography were investigated, using ERA5 data and data from wind observation masts installed at four locations on an island for a site under development, where bidding will soon begin. As [...] Read more.

For this study, the annual frequency of atmospheric stability and the effects of topography were investigated, using ERA5 data and data from wind observation masts installed at four locations on an island for a site under development, where bidding will soon begin. As a result, we found that a variety of atmospheric stabilities appeared at the site, and that the annual average events were not neutral but, instead, unstable. Moreover, the deviation from neutral varied depending on wind direction and the wind speed varied greatly, depending on the mast position and wind direction. Additionally, it was necessary to reproduce the wind flow separation due to topography, in order to predict the wind conditions of wind turbines located close to the island. The accuracy of the airflow simulation by large eddy simulation was validated using the mast-to-mast wind speed ratio. For simulations, we used the commercial software RIAM-COMPACT, which has been widely used in Japan, as it allows the atmospheric stability to be freely set. As a result, we found that the accuracy could be improved by refining the inflow wind direction and taking the average of the results calculated under several atmospheric stability conditions. Full article

(This article belongs to the Special Issue Wind Turbines, Wind Farms and Wind Energy)

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Review

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21 pages, 2483 KiB

Open AccessReview

Assessing Wind Energy Projects Potential in Pakistan: Challenges and Way Forward

by Jamshid Ali Turi, Joanna Rosak-Szyrocka, Maryam Mansoor, Hira Asif, Ahad Nazir and Daniel Balsalobre-Lorente

Energies 2022, 15(23), 9014; https://doi.org/10.3390/en15239014 - 28 Nov 2022

Cited by 8 | Viewed by 2656

Abstract

Energy is the driver of the socioeconomic growth and development of a country. In the pursuit of available and affordable sources of energy, nations around the world have forgotten the sustainability angle and are facing an energy crisis. The developing world has initiated [...] Read more.

Energy is the driver of the socioeconomic growth and development of a country. In the pursuit of available and affordable sources of energy, nations around the world have forgotten the sustainability angle and are facing an energy crisis. The developing world has initiated development plans in an unsustainable way, causing a demand–supply gap and leading to very high energy prices. Renewable energy gives us a solution to this circular crisis. The recent world has seen significant investment in renewables, particularly in the wind energy sector. The investment was initiated as a government spending program, but is now taken up by the private sector. The current study presents a thorough analysis of the prospects for wind energy and the means and measures required to fully capacitate the sector in Pakistan. In Pakistan’s three largest provinces, there is tremendous potential for wind energy, which requires proper utilization and exploration for sustained socioeconomic development. This study is based on the mixed-methods approach. In the first phase, content analysis was caried out using the systematic literature review (SLR) technique. Relevant content analysis was performed using the PRISMA diagram. A total of two hundred and thirty-nine (239) documents were scanned; however, only eighty-two (82) were included after the removal of duplications and irrelevant documents. Moreover, short interviews were conducted with entrepreneurs, and themes have been prescribed. The study found that commercially feasible wind energy potential is particularly abundant in Pakistan’s Sindh and Balochistan regions. The country’s diverse geography makes it ideal for wind turbine installations at various sites. The renewable energy policy should be revisited to incentivize the use of wind energy to ensure the nationally determined contributions (NDCs)’ commitments are assured to achieve sustainable development by 2030. Pakistan has seen rapid development in the wind energy sector with around 4 percent of electric power being generated through wind farms in just over 13 years. In order to exploit the potential, there is a need for significant public and private joint efforts. Full article

(This article belongs to the Special Issue Wind Turbines, Wind Farms and Wind Energy)

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