Assessment Of Wind Energy Potential Research Articles

High-resolution assessment of wind energy potential in the Hami region of Northwestern China

Abstract Wind energy plays a pivotal role in the global effort to mitigate climate change, with China emerging as a leader in renewable energy adoption. The Hami region in northwestern China stands out as a crucial area for wind power development, given its substantial wind resources and strategic importance in China’s energy landscape. However, existing studies on wind energy potential vary widely and involve large uncertainties due to sparse measurements and coarse resolution, highlighting the need for more precise assessments to guide policy decisions and optimize energy utilization. This study leverages high-resolution ERA5 reanalysis data and advanced wind turbine technology to assess wind energy potential in the Hami region, taking into account factors including wind speed patterns, turbine heights, and geographical constraints. The comparison with in-situ data demonstrated that high-resolution ERA5 reanalyzed wind speeds enable to capture multi-year wind speed variations in this region. We find substantial potential for wind energy in Hami, with energy densities exceeding 200 W/m² in the high-potential wind zones. Importantly, this study identifies a new high-potential area in eastern Hami, termed the East Wind Zone. Our high-resolution assessment of wind energy potential at different heights over the past two decades reveals long-term trends and seasonal variations. Increasing the hub height from 95 m to 140 m raises the average wind power generation potential across Hami by 31.3 GWh/year. Our findings highlight the importance of strategic wind farm placement to maximize renewable energy output and provide insights for policy and industry, supporting China's renewable energy goals.

Wind power plant site selection problem solution using GIS and resource assessment and analysis of wind energy potential by estimating Weibull distribution function for sustainable energy production: The case of Bitlis/Turkey

Wind energy, being a sustainable energy source, is a benign, dependable, limitless, and cost-effective source of energy. This study involved the creation of a wind power plant site selection map at a provincial scale using a GIS application. The map took into account both environmental risks and environmental criteria. A wind energy measurement station of the PCE-FWS 20 type, equipped with a touch screen, was installed in the Rahva campus of Bitlis Eren University. Over the course of one year, data on wind direction, wind speed, temperature, relative humidity, and air pressure were collected at 5-s intervals and averaged over 20-min periods. The purpose of this study was to assess the suitability of the measured region for wind energy potential. A comprehensive statistical analysis was conducted using all the data collected during this period to assess the wind energy potential in the studied region. The Weibull distribution was employed to analyse the wind energy potential of this particular region. We employed graphical techniques to ascertain the Weibull distribution. The wind data analysis and simulation were conducted using the Matlab application. Analysed were the monthly statistical wind energy potential of the region, wherein the average wind speed and power density values were determined and presented through graphs at 20-min intervals. Upon analyzing the map generated using the Geographical Information Systems (GIS) technique, it has been ascertained that Bitlis Eren University Rahva campus is located in favorable areas.

Assessment of Wind Energy Potential and Optimal Site Selection for Wind Energy Plant Installations in Igdir/Turkey

Wind energy is an eco-friendly, renewable, domestic, and infinite resource. These factors render the construction of wind turbines appealing to nations, prompting numerous governments to implement incentives to augment their installed capacity of wind turbines. Alongside augmenting the installed capacity of wind turbines, identifying suitable locations for their installation is crucial for optimizing turbine performance. This study aims to evaluate potential sites for wind power plant installation via a GIS, a mapping technique. The Analytic Hierarchy Process (AHP) was employed to assess the locations, including both quantitative and qualitative aspects that significantly impact the wind farm suitability map. Utilizing the GIS methodology, all datasets were examined through height and raster transformations of land surface temperature, plant density index, air pressure, humidity, wind speed, air temperature, land cover, solar radiation, aspect, slope, and topographical characteristics, resulting in the creation of a wind farm map. The correlation between the five-year meteorological data and environmental parameters (wind direction, daily wind speed, daily maximum and minimum air temperatures, daily relative humidity, daily average air temperature, solar radiation duration, daily cloud cover, air humidity, and air pressure) influencing the wind power plant in Iğdır province, including Iğdır Airport, Karakoyunlu, Aralık, and Tuzluca districts, was analyzed. If wind energy towers are installed at 1 km intervals across an area of roughly 858,180 hectares in Igdir province, an estimated 858,180 GWh of wind energy can be generated. The GIS-derived wind power plant map indicates that the installation sites for wind power plants are located in regions susceptible to wind erosion.

“Assessment of combined wind and wave energy in European coastal waters using satellite altimetry.”

In this study, the combined wind and wave energy potential assessment is presented for various locations in European coastal waters. The objective is to investigate the feasibility of satellite altimetry-based assessments of combined wind/wave renewable energy potential on the European shelf. The study is motivated by the potential reduction in energy supply variability by combining wind and wave. The method consists of using a homogenized multi-mission altimeter database available by the European Space Agency Sea State Climate Change Initiative (Sea_State_cci) that comprises 26 years of data, from January 1991 to December 2018, that allows extending the time range and spatial coverage to estimate site wind and wave power densities. An empirical model is used to estimate the wave energy period from the altimeter Ku band significant wave height and radar backscatter coefficient required to compute the wave power density. The results show that wind/wave energy is relatively correlated in the Mediterranean but not in the North Atlantic sites studied. Thus, the Western North Atlantic sites are the most adequate places for wind and wave farms, from the point of view of combined exploitation.The different characteristics of the studied sites show some correspondence between variability and mean wave power, which is an essential input to a marine renewable energy strategy in any jurisdiction. The level of overall variability decreases with an increase in mean wave power, related to the higher power swell waves are not highly correlated with the local wind.

Assessment of Wind Energy Potential for achieving Sustainable Development Goal 7 in the Rural Region of Jeje, Nigeria

Assessment of Wind Energy Potential for achieving Sustainable Development Goal 7 in the Rural Region of Jeje, Nigeria

THE COMPARISON SOME MODELS DAILY WIND SPEED DATA IN PEKANBARU CITY USING TWO, THREE AND FOUR PARAMETERS GAMMA DISTRIBUTIONS

Accurate wind speed modeling is critical in estimating wind energy potential for harnessing wind power effectively. One of the bases for assessment of wind energy potential for a specified region is the probability distribution of wind speed, therefore wind speed data is needed to produce statistical modeling, especially in determining the best probability distribution. For this purpose, several modified distributions will be used and tested to determine the best model to describe wind speed in Pekanbaru. Six continuous probability density function such as Two Parameters Gamma (GM), Three Parameters Slashed Quasi-Gamma (SQG), Three Parameters Quasi Gamma (QG), Three Parameters Generalized Gamma (GG), Four Parameters Poly Weighted Exponentiated Gamma (PWEG) and Four Parameters Exponentiated Gamma Exponential (EGE) distributions were fitted for these tasks using the maximum likelihood technique. To determine the model’s fit to the daily wind speed data, several goodness-of-fit tests were applied, including the graphical methos test (pdf plot) and numerical criteria method test (AIC). The QG and GG distributions are found to be the best-fitted probability distribution based on goodness-of-fit tests for the daily wind speed data at the Pekanbaru station.

Assessment of Wind Energy Potential at Three Prime Locations in Saudi Arabia: Analysis of Sharma, Qurayyat and Sakaka Sites

Purpose: The research aims to assess the wind energy potential at three chosen sites in Saudi Arabia Sharma, Al Qurayyat, and Sakaka over a five-year period from 2019 to 2023. The main goal is to determine the most favorable locations for wind farm development based on wind speed characteristics, Weibull distribution parameters, and wind power density (WPD). Materials and Methods: The wind speed data for the chosen locations was obtained from NASA's wind website and analyzed using the Weibull distribution to determine the shape (k) and scale (c) parameters, which are indicative of wind speed distribution. Critical metrics such as mean wind speed, WPD, and the most probable wind speed were computed to evaluate the wind energy potential. The Vestas V150-4.2 MW turbine was utilized to estimate potential energy output, considering the variability and reliability of wind resources at the specified locations. The study utilized tables, figures, and wind rose diagrams to highlight important wind parameters like mean wind speed, Weibull parameters, WPD, and Vmax for each location. Flowcharts were also used to summarize the methodology and conclusions, ensuring a clear and comprehensive presentation of the data. Findings: The analysis showed that Qurayyat consistently had the highest mean wind speed and WPD, indicating excellent wind energy potential. Sharma and Sakaka also displayed significant wind resources, with high mean wind speeds and substantial WPD values. The Weibull parameters indicated that all three sites have favorable wind characteristics for energy production. The estimated energy output using the Vestas V150-4.2 MW turbine ranged from 12,809 to 17,807 MWh/yr at Site 18, 14,095 to 15,170 MWh/yr at Site 24, and 14,013 to 15,832 MWh/yr at Site 31. Implications to Theory, Practice and Policy: The study presents a thorough evaluation of the wind energy potential in the Al-Jawf and Tabuk regions of Saudi Arabia. It contributes to a deeper understanding of wind resource distribution and its significance for the development of renewable energy. The study provides valuable insights for wind farm developers and policymakers by identifying the most promising locations for investment in wind energy infrastructure. Furthermore, it emphasizes the need for detailed site-specific analyses to optimize wind energy deployment, aligning with Saudi Arabia's broader renewable energy objectives.

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.

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.

A comprehensive assessment of wind energy potential and wind farm design in a coastal industrial city

Purpose Jubail Industrial City is one of the largest industrial centers in the Middle East, offering potential opportunities for renewable energy generation. This research paper presents a comprehensive analysis of the wind resources in Jubail Industrial City and proposes the design of a smart grid-connected wind farm for this strategic location. Design/methodology/approach The study used wind data collected at three different heights above ground level – 10, 50 and 90 m – over four years from 2017 to 2020. Key parameters, such as average wind speeds (WS), predominant wind direction, Weibull shape, scale parameters and wind power density (WPD), were analyzed. The study used Windographer, an exclusive software program designed to evaluate wind resources. Findings The average WS at the respective heights were 3.07, 4.29 and 4.58 m/s. The predominant wind direction was from the north-west. The Weibull shape parameter (k) at the three heights was 1.77, 2.15 and 2.01, while the scale parameter (c) was 3.36, 4.88 and 5.33 m/s. The WPD values at different heights were 17.9, 48.8 and 59.3 W/m2, respectively. Originality/value The findings suggest that Jubail Industrial City possesses favorable wind resources for wind energy generation. The proposed smart grid-connected wind farm design demonstrates the feasibility of harnessing wind power in the region, contributing to sustainable energy production and economic benefits.

Outlook for Offshore Wind Energy Development in Mexico from WRF Simulations and CMIP6 Projections

This study presents a comprehensive assessment of the offshore wind energy potential in Mexico across 40 years (1979–2018) of numerical simulations using the Weather Research and Forecasting (WRF) model and data from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The WRF identifies three regions with moderate to good wind potential: off the north coast of Tamaulipas (Zone I), the northwest coast of Yucatan (Zone II), and the Gulf of Tehuantepec (Zone III). The analysis involves comparing 47 CMIP6 climate models with the WRF results and selecting the best performing models to obtain future projections for the short term (2040–2069) and the long term (2070–2099). Two ensemble-based strategies were implemented. The first one, which uses an intersection approach from which four CMIP6 models were considered, reveals positive percentage differences in Zone II for both future projections, especially for the long-term one. In Zones I and III, positive values are also observed near the coast, mainly for the long-term projection, but they are considerably lower compared to those in Zone II. The second ensemble strategy uses weight assignment through the Mean Absolute Percentage Error, so that a greater weight is given to the model that performed better in each particular zone, potentially providing more accurate results. The findings suggest the likelihood of increased offshore wind energy in these three zones of Mexico, for both short- and long-term future projections, with positive percentage differences of up to 10% in certain areas.

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

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

Assessment of wind and wave energy potential along the Indian coast

Assessment of wind and wave energy potential along the Indian coast

Assessment of wind energy potential in Zambia

An assessment of potential for wind energy in Zambia was carried out to help address the shortage of energy due to increasing energy needs arising from energy requirements for newly opened mining industries and unreliable hydropower generation due to climate change. The assessment was carried out by collecting wind speed data of 25 sites owned by Zambia Meteorological Department. The objective of the study was to analyse wind patterns and determine areas in Zambia where production of electricity from wind can be invested. The analysis was based on the use of Weibull shape factor and yielded 3.32 m/s wind speed and 13.2 shape factor from a site with the most suitable wind speeds (Kasama Meteorological Station). A correlation study was carried out using these results and Goldwin 1.5 MW wind turbine of model GW82/1500. The study results showed that 44.46 kW power output could be obtained at 3.83 m/s wind speed by a correlation equation of this turbine power output with wind speeds. A series of similar power output results were obtained for wind speeds recorded in the years from 2013 to 2021 and compared with those obtained in the same period by using model equation due to Al Buhariri, and found that the power outputs compared very closely at low wind speeds ranging 3 to 3.2 m/s but rather widely at higher wind speeds.This study is significant in that it provides information on decision making and helps determine policy direction for wind utilization in Zambia and it can also help influence investment decision by Zambian mining companies who have financial ability to undertake large scale investments.

Wind energy potential assessment: a case study in Central India

Access to sustainable energy is important for economic development and environmental protection. The shift towards renewable energy is essential for mitigating climate change and ensuring long-term energy sustainability. Though developing nations possess significant renewable energy capacity, transitioning to renewable sources remains a challenge. India's expanding wind energy sector has the potential to lower carbon dioxide emissions and produce energy in a sustainable manner. This research uses the Windographer software to conduct a comprehensive analysis of the wind energy potential at a distinct site in Nagpur, India. The assessment includes monthly average wind speeds, wind rose diagrams that show the prevailing wind directions, and wind power density (WPD) calculations. These factors together establish the site's classification. In this study, Weibull parameters are derived using four distinct methods to estimate the probability distribution function. The average WPD is found to be 13.63 and 71.08 W/m2 at heights of 10 and 100 m above ground level, respectively. Through wind turbine output analysis, numerous turbine models are assessed for suitability at the specified location. The Windtec DD 3000-125 model, interestingly, has a maximum capacity factor of 9.27%. The results highlight comparatively low wind potential in the studied region. Nevertheless, a wind turbine at this location can produce about 2 412 379 kW of electricity annually using the aforementioned wind turbine model.

THE COMPARISON SOME EXTENDED WEIBULL DISTRIBUTION (WEIBULL (W), EXPONENTIATED WEIBULL (EW), EXPONENTIATED EXPONENTIAL WEIBULL (EEW) AND ADDITIVE WEIBULL (AW)) FOR THE WIND SPEED PROBABILITY MODELLING

Accurate wind speed modeling is critical in estimating wind energy potential for harnessing wind power effectively. One of the bases for assessment of wind energy potential for a specified region is the probability distribution of wind speed, therefore wind speed data is needed to produce statistical modeling, especially in determining the best probability distribution. For this purpose, several modified weibull distributions will be used and tested to determine the best model to describe wind speed in Pekanbaru. The main goal of this study is to find the best fitting distribution to the daily wind speed measured over Pekanbaru region for the years 1999-2020 by using the four modified weibull distributions, namely Weibull (W), Exponentiated Weibull (EW), Additive Weibull (AW) and Exponentiated Exponential Weibull (EEW). The maximum likelihood method will be used to get the estimated parameter value from the distribution used in this study. Furthermore, the graphical inspection (density-density plot and cumulative plot) and numerical criteria (Akaike’s information criterion (AIC), Bayesian Information Criteria (BIC), - log likelihood (- l) ) were used to determine the best fit model. In most cases, the results produced by the graphical inspection were similar, and differed from the numerical criteria . The best fit result was chosen as the distribution with the lowest values of AIC, BIC and - l. In general, the Exponentiated Exponential Weibull (EEW) distribution has been selected as the best model.

A geospatial integrated multi-criteria approach for assessment of solar and wind energy potentials with economic and environmental analysis

India has made an international commitment to generate about 50% of its total energy needs from renewable energy sources by 2030. Here, to meet such a commitment, this study developed high-resolution (1 × 1 km2) geospatial solar and wind geographical potential maps with an aim to calculate the theoretical and technical potentials with economic and environmental sustainability. These geographical potential maps are developed by taking into account 13 evaluation and restriction factors pertaining to technical, economic, and socio-environmental categories. The investigation identifies that Rajasthan, Karnataka, and Gujarat have a plurality of extremely favorable land areas for solar and wind energy sources. Furthermore, the results imply that appropriate planning for the installation of renewable projects at the identified optimum locations can fulfill India's commitments with regard to an optimal energy mix scenario, with energy available twice the available potential for consumption in 2030. Furthermore, with the least average levelized cost of energy of 38.8 $/MWh (2.83₹/kWh) and 42.3 $/MWh (3.09₹/kWh), solar and wind energy sources are potentially more appealing and affordable than conventional energy sources. The findings of this study will also significantly advance India's attempts to accept and develop renewable energy sources, helping to realize the government's objective for sustainable electricity production.

Assessment of offshore wind energy potential on the Syrian
 coast based on mathematical modeling in a GIS environment

Assessment of offshore wind energy potential on the Syrian
 coast based on mathematical modeling in a GIS environment

Statistical analysis of wind speed distribution based on sixWeibull Methods for wind power evaluation in Garoua, Cameroon

Wind data analysis and accurate wind energy potential assessment are critical factors for suitable development of wind power application at a given location. This paper explores wind speed distribution to select the two-parameter Weibull methods that provide accurate and efficient estimation of energy output for Wind Energy Conversion Systems (WECS).The dimensionless shape parameter k and the scale parameter C are determined based on measured hourly mean wind speed data in times-series from 2007 to 2012, collected at the Garoua International Airport, main meteorological station, in Garoua, Cameroon. Six numerical methods, namely Empirical Method (EM), Energy Pattern Factor method (EPF), Graphical Method (GM), Maximum Likelihood Method (MLM), Moment Method (MM) and Modified Maximum Likelihood Method (MMLM) are examined to estimate the Weibull parameters. To analyze the efficiency of the methods and to ascertain how closely the measured data follow the Weibull methods, goodness of fit tests were performed using the chi-square test (X2), correlation coefficient (R2), root mean square error (RMSE) and Kolmogorov-Smirnov test (KOL). The results revealed that the EPF followed by the MM were the most accurate and efficient methods for determining the value of C and k to approximate wind speed distribution. The statistical tests rejected the GM as an adequate method and revealed as well that the EM, MLM and MMLM ranked respectively third, fourth and fifth. Furthermore, the potential for wind energy development in Garoua is not fitted for generating electricity and a very fruitful result would be achieved if windmills were installed for producing community water supply, livestock watering, and farm irrigation.

Assessment of wind energy potential within through-building openings under twisted wind flows

Integrating wind turbines into high-rise buildings can generate renewable energy on-site. Through-building openings can effectively increase the utilization of wind energy in high-rise buildings. However, current research on wind energy potential within these openings is limited to conventional wind conditions. Meteorological observations indicate that cities near mountainous terrain, such as Hong Kong and Tokyo, often experience twisted wind flows due to topography. These wind flows cause changes in the wind direction angle with increasing vertical height, and previous studies have demonstrated significant differences in flow fields around high-rise buildings in twisted wind fields as compared to conventional wind fields. Consequently, the wind energy potential within through-building openings of high-rise buildings may be significantly impacted by twisted winds. This paper applies high-fidelity methods (LES) to evaluate the wind energy potential within through-building openings of buildings with different heights under twisted wind field conditions, and compares the results with those obtained under conventional wind conditions. The results show that the passage near the inflow experiences higher wind speed and wind power compared to the passage far from the inflow under the twisted wind profile. The twisted wind flows cause an increase in local wind power density within the through-building openings, but it also results in an elevation of turbulence intensity, which is unfavorable for enhancing the efficiency of wind turbines. Furthermore, the degree of enhancement in local wind power and local turbulence intensity within the through-building openings gradually intensifies as the wind twisted angle increases.