Wind Energy Potential Research Articles

Data-driven met-ocean model for offshore wind energy applications

In recent years, the global transition towards green energy, driven by environmental concerns and increasing electricity demands, has remarkably reshaped the energy landscape. The transformative potential of marine wind energy is particularly critical in securing a sustainable energy future. To achieve this objective, it is essential to have an accurate understanding of wind dynamics and their interactions with ocean waves for the proper design and operation of offshore wind turbines (OWTs). The accuracy of met-ocean models depends critically on their ability to correctly capture sea-surface drag over the multiscale ocean surface—a quantity typically not directly resolved in numerical models and challenging to acquire using either field or laboratory measurements. Although skin friction drag contributes considerably to the total wind stress, especially at moderate wind speeds, it is notoriously challenging to predict using physics-based approaches. The current work introduces a novel approach based on a convolutional neural network (CNN) model to predict the spatial distributions of skin friction drag over wind-generated surface waves using wave profiles, local wave slopes, local wave phases, and the scaled wind speed. The CNN model is trained using a set of high-resolution laboratory measurements of air-side velocity fields and their respective surface viscous stresses obtained over a range of wind-wave conditions. The results demonstrate the capability of our model to accurately estimate both the instantaneous and area-aggregate viscous stresses for unseen wind-wave regimes. The proposed CNN-based wall-layer model offers a viable pathway for estimating the local and averaged skin friction drag in met-ocean simulations.

Assessment of wind energy resource in the western region of Somaliland

As the population of Somaliland continues to grow rapidly, the demand for electricity is anticipated to rise exponentially over the next few decades. The provision of reliable and cost-effective electricity service is at the core of the economic and social development of Somaliland. Wind energy might offer a sustainable solution to the exceptionally high electricity prices. In this study, a techno-economic assessment of the wind energy potential in some parts of the western region of Somaliland is performed. Measured data of wind speed and wind direction for three sites around the capital city of Hargeisa are utilized to characterize the resource using Weibull distribution functions. Technical and economic performances of several commercial wind turbines are examined. Out of the three sites, Xumba Weyne stands out as the most favorable site for wind energy harnessing with average annual power and energy densities at 80 m hub height of 317 kW/m2 and 2782 kWh/m2, respectively. Wind turbines installed in Xumba Weyne yielded the lowest levelized cost of electricity (LCOE) of not more than 0.07 $/kWh, shortest payback times (i.e., less than 7.2 years) with minimum return on investment (ROI) of approximately 150%.

Analysis of Seasonal Wind Energy Potential on Zanzibar Coastal Island

The main objectives of this research were to numerically analyze the potential for seasonal wind power (WP), assess wind direction, and select the most effective wind turbine (WT) for installation at the research site. Wind data were collected half-hourly from a branch of the Tanzania Meteorological Authority nearest to the research site. The collected data were analyzed using a double-parameter Weibull distribution (WD) model, where the standard deviation (SD) method was used to fit the WD. The results revealed that the site experienced strong winds within the range of 4.5–7 m/s between the hours of 05:00 - 20:00, with the most likely seasonal wind speed (WS) ranging from 5–7 m/s, while the mean seasonal WS was 9.07–12.14 m/s. The highest possible wind energy density (wED) of 23.3 GWh/m2 at a hub height of 10 m occurred during winter, followed by spring, autumn, and summer, with 6.39, 6.32, and 3.33 GWh/m2, respectively. The annual wED was > 13.52 GWh/m2, with a typical month-to-month energy of 1.13 GWh/m2. Finally, the study concluded that the recommended WT model (POLARIS P62-1000) was the best choice for installation at the study site due to its sustainable WS and WP potential. Based on the findings of this research, which show that the site has sustainable seasonal wind resources, it is suggested that future wind research be carried out to extend the dataset to ensure the long-term seasonal wind pattern at the site. Doi: 10.28991/HIJ-2024-05-02-08 Full Text: PDF

VAST as a generic, modular aeromechanics code for wind turbine simulation

In order to leverage the full potential wind energy, more research is needed both in the field and numerically. At DLR, the need for simulation competencies is expected to be met by the in-house developed generic and highly flexible multi-physics code VAST. While initially aimed at low- to medium-fidelity simulations of helicopter aeromechanics, the code is applicable to other problems like wind turbine computations. The goal of this paper is to present the general concept of VAST and briefly introduce the available physics models. Next to examples of successful application to helicopter simulation problems, first results in the field of wind energy are shown. The well-known IEA 15 MW reference wind turbine is used as a basis to compare VAST — both with a BEMT and a vortex theory inflow model — to other tools with similar and higher-fidelity physics modeling. For the rigid turbine, blade loads are compared with results of other tools using engineering models as well as CFD, showing good overall agreement. A more detailed evaluation of aerodynamic quantities like the angle of attack and the induced velocity field is presented for VAST and the CFD results. An outlook on the ongoing VAST developments is given.

A Multi-Flow Visual Analytics Web Platform for Detecting High Wind Energy Potential in Urban Environments

A Multi-Flow Visual Analytics Web Platform for Detecting High Wind Energy Potential in Urban Environments

Comparative analysis of Weibull parameters estimation for wind power potential assessments

There are many renewable energy sources available, especially wind energy, but it is not being fully utilized. In the wind energy industry, Wind Power Potential (WPP) is essential since it is critical to the development, operation, and optimization of wind power plants. WPP plays a significant role in the wind energy project life cycle, impacting site selection, project viability, technology choices, and ultimate success. This means that specific WPP for certain places need to be determined for development in wind industry.The goal of this study is to conduct a statistical comparison and analysis of the efficacy of various numerical methods, including the method of moments (MoM), the energy pattern factor method (EPFM), the maximum likelihood method (MLM), the energy density method (EDM), the energy pattern factor method of Sathyajith (EPFMS), Rayleigh's distribution (Rayl), and the novel energy pattern factor method (NEPFM). These methods are compared for different sites of Andhra Pradesh India. The NEPFM is considered the most effective approach for assessing the wind energy density in the regions of Visakhapatnam, Amaravati, and Tirupati. Conversely, the MLM (Modified Logarithmic Model) technique has demonstrated superior performance in evaluating the wind energy potential specifically for the Rajamahendravaram site. The Rayleigh distribution, also known as Rayl., was utilized as the primary approach for calculating the probability density of the geographical sites of Visakhapatnam, Rajamahendravaram, and Amaravati. Additionally, the energy pattern factor method was employed to analyze the site of Tirupati. The Rayleigh distribution is found to be the most suitable statistical model for estimating the cumulative density of site locations in Visakhapatnam and Amaravati. Similarly, the innovative energy pattern factor technique is recommended for analyzing the cumulative density of site locations in Rajamahendravaram and Tirupati.

Offshore wind energy potential in Shandong Sea of China revealed by ERA5 reanalysis data and remote sensing

The scale of offshore wind energy has been rapidly growing worldwide in recent years. Accurate assessment of the offshore wind energy potential and emission reduction benefits is essential to realize the ambitious targets for future installations. In this study, the Shandong Sea of China was selected as a case study, and the long-term offshore wind energy potential was assessed using ERA5 reanalysis data for the last 30 years, including the spatio-temporal variation and the technical potential of wind resources. An innovative approach to evaluate the technical potential of installed offshore wind farms based on deep learning, satellite images and ERA5 reanalysis data was proposed and applied to the Shandong Sea. The results show that (1) the highest offshore wind energy potential is observed in the northeast sea of Weihai; (2) the inter-annual variation of wind resources is relatively modest, with a pronounced monthly variability. The richest wind resources are found in the spring; (3) the electricity generation of wind resources is 12.66–30.53 GWh/year, equivalent to a reduction of 9.35–22.55 Kt of CO2 emissions; (4) as of 2023, there were 12 OWFs including 633 offshore wind turbines in Shandong Sea, generating 14,218 GWh/year of clean electricity and reducing 10,505 kt/year of CO2 emissions. The method proposed in this study is applicable to both small- and large-scale areas and allows for an accurate assessment of the electricity generation from installed offshore wind farms.

Potential Wind Energy Analysis in Maluku Region with Savonius Turbine using CFD Approach

Potential Wind Energy Analysis in Maluku Region with Savonius Turbine using CFD Approach

GREENER IS CHEAPER: AN EXAMPLE FROM OFFSHORE WIND FARMS

Abstract Offshore wind farms (OWF) are now in operation and increasingly under construction as scalable, sustainable energy sources. In fact OWFs are currently the cheapest form of new energy projects in Europe. The levelized cost of energy (LCOE) for OWF has fallen drastically due to decades of innovation facilitated by both taxpayer and private sector funding. This emerging industry is experiencing massive worldwide growth with the potential to accelerate the decarbonization of regional and the global economy as well as bring a reliable source of green hydrogen into commercial use, all with minimal disruption to ecosystems and impacts on biodiversity. This paper provides a historical perspective of wind energy harnessing and shows that wind turbines are the oldest, largest and one of the smartest machines. We also highlight the potential of offshore wind energy to provide new solutions to (a) meet clean energy demand for a growing world population, (b) improve energy security of nations through other downstream technologies such as production and storage of dispatchable fuel (such as green hydrogen battery storage) and (c) through supply complementarity improve resilience of nuclear power plants in high-seismic-activity areas. Offshore wind industry can also become a gold standard for future industries, and the paper provides insights into the new green economics and jobs and factories for the future. We show that environment-friendly regulation is driving innovations even further to enhance sustainability of OWF. Examples include material recycling, landfill ban on blade disposal and ecofriendly low-noise offshore construction to protect biodiversity.

Influence of air flow features on alpine wind energy potential

Wind energy is one of the potential options to fill the gap in renewable energy production in Switzerland during the winter season when the energy demand exceeds local production capacities. With likely further rising energy consumption in the future, the winter energy deficit may further increase. However, a reliable assessment of wind energy potential in complex terrain remains challenging. To obtain such information, numerical simulations are performed using a combination of the “Consortium for Small-scale Modeling” and “Weather Research and Forecasting” (COSMO-WRF) models initialized and driven by COSMO-1E model, which allows us to simulate the influence of topography at a horizontal resolution of 300 m. Two LiDAR measurement campaigns were conducted in the regions of Lukmanier Pass and Les Diablerets, Switzerland. Observational LiDAR data and measurements from nearby wind sensor networks are used to validate the COSMO-WRF simulations. The simulations show an improved representation of wind speed and direction near the ground compared to COSMO-1E. However, with increasing height and less effect of the terrain, COSMO-WRF tends to overestimate the wind speeds, following the bias that is already present in COSMO-1E. We investigate two characteristic mountain–terrain flow features, namely waves and Foehn. The effect of mountain-induced waves of the flow is investigated through an event that occurred in the area of Diablerets. One-year analysis for the frequency of conditions that are favorable for mountain wave formation is estimated. The Foehn impact on wind was observed in the Lukmanier domain. We attempt quantification of the probability of occurrence using the Foehnix model. The result shows a high probability of Foehn occurrence during the winter and early spring seasons. Our study highlights the importance of incorporating complex terrain-related meteorological events into the wind energy assessment. Furthermore, for an accurate assessment of wind speed in complex terrain, our study suggests the necessity to have a better representation of the topography compared to COSMO-1E.

Analysis of Wind Energy Potential in Sri Lankan Waters Based on ERA5 (ECMWF Reanalysis v5) and CCMP (Cross-Calibrated Multi-Platform)

Analysis of Wind Energy Potential in Sri Lankan Waters Based on ERA5 (ECMWF Reanalysis v5) and CCMP (Cross-Calibrated Multi-Platform)

India’s Transition Towards Becoming a Carbon Neutral Nation

The escalating impacts of climate change and the imperative to decarbonize global energy systems have spurred a concerted effort toward exploring and adopting renewable energy technologies. Among all these options, wind energy stands out as a top choice. It has a lot of potential to meet the growing need for energy and help the environment simultaneously. In this study, we comprehensively assess wind energy potential in five key cities across India: Udupi, Kollam, Surat, Visakhapatnam, and Jamnagar. Leveraging two decades of wind data sourced from reputable repositories such as the Iarc Power Project of NASA, we employ rigorous statistical analysis, including the application of the Weibull distribution, to elucidate the probability distribution of wind speeds and assess the viability of wind energy utilization in each city. We have also focused on other ways for decarbonizing India using new and advanced technologies such as Carbon Capture, Utilization and Storage (CCUS), and green hydrogen. Our analysis extends beyond theoretical considerations to encompass practical dimensions such as infrastructure requirements, cost implications, and technological advancements. Through meticulous evaluation, we aim to provide actionable insights that can inform policy formulation, infrastructure development, and investment strategies aimed at accelerating the adoption of wind energy technologies in India. Our research suggests a solution for implementing and policy framework for CCUS to make it more effective in achieving India’s carbon-neutral goals. We have also discussed green hydrogen its types, cost of implementation, and its current application. By fostering dialogue and collaboration, our research seeks to contribute to the ongoing discourse surrounding renewable energy transitions, facilitating informed decision-making towards a more sustainable energy future.

Feasibility of Wind Energy Utilization for Sustainable Power Generation in Ilorin, Kwara State, Nigeria's North-Central Region

The escalating energy demands across Nigeria, especially in remote rural areas, have outpaced the capacity of the national electricity grid, necessitating the development of independent and sustainable energy sources. Among the renewable options, wind energy stands out as a promising solution. This study focuses on assessing the potential of wind energy in Ilorin, located in Kwara State, within Nigeria's north-central region. Utilizing data collected from 2007 to 2021 by the Nigerian Meteorological Agency, the research examines monthly average wind speeds at two specific coordinates in Ilorin, considering variations in air density. The study utilizes a 15-year set of monthly average wind velocities obtained from the Nigerian Meteorological Agency (NiMet) Headquarters in Abuja, measured at a height of 10 meters above ground level. By employing the 2-coefficient Weibull statistical model and extrapolation principles across different altitudes ranging from 150 to 900 meters above ground level, the study reveals distinct seasonal patterns of wind speeds ranging from 1.1 to 5.1 m/s in Ilorin. Furthermore, wind power density values ranging from 6.7 to 39.20 W/m2 are identified, with optimal wind attributes observed at altitudes exceeding 900 meters. These findings provide valuable insights for assessing the feasibility of wind energy utilization and designing efficient systems in Nigeria's north-central regions, aiding in the sustainable energy transition.

Marshall-olkin extended inverted kumaraswamy distribution for modeling of wind speed data

Many important factors need to be taken into account when assessing the wind energy potential of a place. These include precisely determining the distribution to be used for the analysis of wind speed data and precisely estimating the model parameters. The current study has two main goals: the first is to estimate the parameters of the Marshall Olkin extended inverted Kumaraswamy (MOEIK) distribution, and the second is to use it to model wind speed data. For parameter estimation, four renowned estimation methods are used: maximum likelihood, Anderson Darling, Cramer von Misses, and ordinary least squares. A comprehensive simulation study is used to evaluate the behavior of these derived estimators, and maximum likelihood is found to efficiently estimate the parameters. For this study, wind speed data was collected from three stations in Sanghar, Tandoghulamali, and Umerkot. The results indicated that the MOIKw distribution provides efficient fitting based on the goodness-of-fit measures among competitive distributions.

GIS-based multi-criteria analysis for solar, wind, and biomass energy potential: A case study of Iraq with implications for climate goals

The study utilizes a GIS-Based Multi-Criteria Analysis to evaluate the viability of solar, wind, and biomass energy in Iraq, focusing on enhancing the nation's energy independence and meeting international climate objectives. Detailed spatial analysis revealed specific zones suited for the efficient installation of energy power plants. Zones with an energy surplus factor of less than 0.2 are deemed not viable for development, whereas regions with factors less than 0.4 are ideal for harnessing solar, wind, and biomass resources. Furthermore, zones with surplus factors greater than 0.6 are highly recommended for energy projects. Notably, the results show that strategic development of these zones can cater to between 45 % and 68 % of the country total energy demand, potentially reducing the CO2 footprint by 0.5. Moreover, with an increase in the construction threshold, the CO2 reduction potential has shown a decrease to 1.29E+08 tones, particularly evident in areas with rigorous construction standards. A plateau in reduction figures is observed once the construction threshold surpasses 3.5, stabilizing between 2.5E+07 tones. As the threshold exceeds 5, a further stabilization is noted, consistently around 2.17E+07 tones, persisting even when the threshold approaches 8. The outcomes underscores the critical role of strategic zoning in maximizing renewable energy potential, highlighting pathways for the country to achieve energy self-sufficiency and make significant strides in climate goals.

Harnessing the power of wind: a comprehensive analysis of wind energy potential in Dhaka city

This study gives a thorough analysis on the wind energy potential in Dhaka, Bangladesh, utilizing data from NASA Power's remote sensing tools and weather data from the Bangladesh Meteorological Department (BMD). The wind speed data collected over a 22 year period at an altitude of 10 m. The results indicate that 3.07 m per second (ms−1) is Dhaka city's typical wind speed, while the maximum wind speeds were recorded in June and July. A Weibull distribution is used to observe the wind data, as well as to calculate the Weibull form parameter of 2.65 and the scale parameter of 3.43 ms−1. Based on these parameters, the most probable wind speed along with the wind speed carrying maximum energy were calculated 2.83 ms−1 and 4.28 ms−1, respectively. The highest density of energy has been found in the month of July with a value of 52.11 W/m2. According to the study, the south is the most prominent wind direction for Dhaka city. Moreover, the study analyzes the relations between energy density and other variables, like wind speed, humidity, dry bulb temperature, etc. Positive correlations between energy density, wind speed, and dry bulb temperature imply that the higher wind speeds and dry bulb temperatures result in greater energies. The study's conclusions offer intuitive information about Dhaka City's potential for wind energy and can support direct future efforts to pursue this green resource in alignment with the Sustainable Development Goals (SDGs) of Bangladesh.

Wind Flow Characteristics on a Vertical Farm with Potential Use of Energy Harvesting

The response to the climate emergency requires solutions that address multiple sustainability targets, which could be conducted by merging scientific research from areas that have traditionally evolved separately. This investigation presents advances in that direction by studying a building prototype designated for vertical farming, which enables the wind energy potential across built-up areas to be explored, in this case through the implementation of micro-wind turbines on the surface of the prototype. The study includes a parametric analysis consisting of varying locations of wind turbines across the building envelope, and the width of ventilation corridors. The effects of different widths of outdoor ventilation corridors, various locations, and additional wind angles on the capacity to harvest wind resources were investigated. The results showed that the 5 m wide outdoor corridor has the best ventilation effect, and the wind turbine placed on the roof has the best wind energy potential. The efficiency of wind turbines decreases significantly when multiple devices are placed at the same height on the façades, although overall, the potential for energy harvesting seems incremental.

Centrifuge Modeling of the Monotonic Capacity of Offshore Ring Anchors in Sand

The development of offshore wind technology has become a feasible solution to meet the increasing demands for clean and renewable energy. The United States has a total of 4250GW offshore wind energy potential; however, 65% of it is in deep water zones (Lopez et al., 2022) where wind turbines with fixed foundations typically are economically and technically unfeasible. In those situations, floating turbines supported by subsea anchors are a more competitive solution. Based on previous studies, ring anchors can be more material-efficient than piles and caissons because they require less material. Ring anchors also perform better than drag anchors due to their greater embedment depth. To further understand the behavior of ring anchors in saturated sand, a series of centrifuge load tests were performed at the University of California Davis Center for Geotechnical Modeling (CGM) at an acceleration of 70g. This test series investigated the effect of the anchor embedment depth and loading angle on the monotonic loading behavior. The ring anchor models were embedded in dense saturated sand, and then connected to an actuator using taut steel wire ropes. Sensors were used to measure the line tension, displacement, and inclination. The results indicate that the ring anchors mobilize greater capacities as their embedment depth is increased and when they are loaded at angles close to the horizontal direction, while vertical loading leads to the smallest capacity. The anchor displacement during the tests deviated slightly from the loading direction, showing a horizontal deviation at the earlier stages of the tests and a vertical one after the peak load. Furthermore, soil disturbance induced by the anchor installation was found to have a strong effect on the vertical capacity of the ring anchors. Overall, this study provides valuable information regarding the monotonic loading behavior of ring anchors which can aid in their future field deployment.

Assessment Wind Energy in the Central Region of Thailand

This study evaluates the wind energy potential in the central region of Thailand by using data from seven wind measurement stations (Ayutthaya, Bangna, Chainat, Kamphaeng Saen, Lopburi, Nakhon Sawan, and Pathum Thani) and two wind turbines, namely AN Bonus 1300/62 and Vestas Wind System A/S. The analysis was conducted using the Wind Atlas Analysis and Application Program (WAsP) to examine the collected wind speed data over three years from the seven stations. The objective was to identify the top three areas with the highest annual energy production (AEP). Additionally, the study included an economic analysis using various metrics, such as the present value of costs (PVC), benefit-cost ratio (BCR), payback period (PBP), and levelized cost of electricity (LCOE). The results indicate that the AN Bonus 1300/62 model provides higher values in areas with high wind speeds and relatively high electricity demand, whereas the Vestas V52 model is suitable for areas with low wind speeds and lower investment requirements. Based on these findings, the authors recommend prioritizing wind energy development in the provinces of Ayutthaya, Nakhon Sawan, and Lopburi using the AN Bonus 1300/62 model, as these areas have high wind speeds and relatively high electricity demand. Overall, this research provides valuable insights for policymakers and investors, enabling them to make informed decisions regarding renewable energy investments in Thailand.

The Effect of Varying Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Parameters on Wind Energy Prediction: A Comparative Study

Owing to the development of technology, the majority of nations throughout the world now rely on fossil fuels and nuclear power plants to meet their energy needs. However, as academic research on this subject has shown, it has become clear that alternative energy uses are necessary due to the gradual depletion of these fuels and their significant negative effects on the environment. In order to ensure energy diversity and end the energy shortage, the development of renewable energy sources is crucial. The prediction of wind power is crucial for effectively utilizing the potential of wind energy. In this study, an adaptive neuro-fuzzy inference system (ANFIS) and an artificial neural network (ANN) have been developed for the prediction of wind power. In this study, data sets were created by taking the daily average wind speeds of the selected wind turbine, the daily average power values it produces, and the daily average wind speed values in the Velimese region. By creating single-hidden layer and multi-hidden layer ANN models, the network was trained multiple times with different activation functions and different numbers of neurons, and wind power prediction was performed. In the ANFIS model, the number of membership functions is kept constant, and wind power prediction is performed using different membership functions. With these ANFIS and ANN models developed with different parameter combinations, it is aimed to determine the most efficient model by performing daily average wind power prediction. Parameter combinations were tested to determine the appropriate models, and as a result, the ANN and ANFIS models were compared with each other.