Assessment Of Wind Power Potential Research Articles

Strategic analysis of wind energy potential and optimal turbine selection in Al-Jouf, Saudi Arabia

Wind power is considered one of the most environmentally friendly and rapidly growing form of renewable energy. This study aims at assessment of wind power potential for Al-Jouf region in Saudi Arabia. A long term historical wind speed data of 21 years (2000–2021) was analytically modeled using Weibull distribution function to determine the wind characteristics. The Weibull parameters and average wind speed for monthly and yearly were evaluated using MATLAB program. An online wind power calculator developed by Meteotest was used to evaluate the wind energy by selecting various turbines. Six types of commercial wind turbines of 3 MW capacity were selected for wind power assessment to optimize the turbine selection. Our analysis showed that average wind speed varies between 3.88 m/s and 4.99 m/s and an overall average wind speed is 4.38 m/s. The most frequent wind speed observed was 3.9 m/s with a probability of 20 % approximately. The Weibull distribution parameters, shape parameter “k” values ranged between 1.89 and 2.21 with an average of 1.98 and scale factor “c” values varied between 4.41 and 5.66 m/s with a mean value of 4.86 m/s. Performance evaluations of selected wind turbine models reveal that the Vestas V126 turbine outperforms others at the Al-Jouf site, generating an annual energy yield of 3779400 kWh at a capacity factor of 14.4 %. These results suggest that by constructing a wind farm consisting of 100 V126 turbines may compensate for the energy needs of 46000 individuals. These findings establish Al-Jouf as a viable location for utility-scale wind power projects, offering valuable insights for turbine manufacturers, developers, operators, and policymakers interested in deploying large-capacity turbines in the region. The method used in this study for wind power analysis and turbine selection is simple and helpful to provide an initial assessment about the site suitability and turbine selection without undergoing exhaustive efforts.

Assessment of wind power potential and economic viability at Al-Hodeidah in Yemen: Supplying local communities with electricity using wind energy

Wind energy is a promising sector in the power generation industry because it is renewable and globally available. This study assessed the wind power potential and the economic viability of utilizing wind turbines at the chosen location to produce electricity. This work uses wind speed data collected during (2012–2014) at 10 m height. This research presents the maximum and mean values of measured wind speeds. We have analyzed annual, seasonal, and monthly variations of wind speed. The wind characteristics and wind power potential of the study site have been investigated using Weibull and Rayleigh distribution functions. The annual mean wind speed and Weibull parameters (shape and scale) have been determined to be 4.477 m/s, 2.494, and 5.046 m/s, respectively, according to the actual data collected over the three years. The annual mean power density and energy density have been computed to be 86.39 W/m2 and 757.226 kWh/m2 according to the actual data collected over the three years, and the predicted annual mean power density and energy density have been computed to be 86.492 W/m2 and 758.358 kWh/m2 using Weibull parameters and 164.372 W/m2 and 1441.2 kWh/m2 using Rayleigh distribution, respectively. Accordingly, the power density computed using the Weibull parameters was similar to the power density computed using real wind data, whereas the power density calculated by the Rayleigh function was higher. An economic analysis has been done to choose a suitable turbine for the study site. The results indicate that the wind turbine ZEFIR D14-Px-Ty is indeed a viable option for wind power generation in Al-Hodeidah. It has a relatively low cost of energy of $0.03/kWh and a high capacity factor of 0.428, indicating its efficiency in converting wind energy into electricity. Therefore, the selected location is recommended for some small stand-alone systems as well as small wind farms.

China's onshore wind energy potential in the context of climate change

China has great potential for developing renewable energy to achieve its carbon neutrality goals. Orderly development of renewable energy is essential to enhance resource utilization efficiency and ensure safety during the energy system transition process. This study presents a thorough assessment of China's onshore wind power potential by considering the land suitability, the potential and temporal characteristics, and the impacts of climate change. The high-resolution maps combining wind resources with land conditions and climate scenarios are produced to provide insights into system planning, grid integration, and flexibility management. The results show that the capacity potential of onshore wind energy in China is 9.6 TW with an annual generation of 12.6 PWh, and 83 % of total capacity has a cost advantage with the levelized cost lower than the 60 $/MWh threshold. By comprehensively considering geographical, economic, and social criteria, around 8.1 % of the national territorial area is identified as the most suitable area for wind power development, primarily in Inner Mongolia and Xinjiang. The annual electricity generation from these areas can fulfill nearly 69 % of the nation's electricity demand. Future climate change projections indicate a remarkable generalized drop by 18 % in the north and a slight increase by 7 % in the south under the RCP 8.5 scenario. However, significant changes in wind resources are mostly within restricted areas, suggesting that future climate change would like to bring negative but limited impacts on wind power production in China.

Long-Term Assessment of Morocco’s Offshore Wind Energy Potential Using ERA5 and IFREMER Wind Data

Offshore wind energy is a promising resource for renewable energy development. Reanalysed wind data are unmatched by other wind data sources in providing a long-term assessment of wind power potential. In this study, 10 of the selected offshore locations close to the Moroccan coast were used to evaluate the ERA5 wind reanalysis dataset against the IFREMER-blended observational dataset covering the years 1993–2016. The ERA5 wind data’s capacity to represent wind variability in the area was confirmed by the results of the statistical methodologies used. All the reanalysed data scored better at capturing the observed wind variability at the southern sites than at the northern ones, where the wind variability was more complex. In a long-term evaluation from 1981 to 2020, the wind power potential in the Moroccan Atlantic coast was found to be very stable except in the northern sites and between Agadir and Bou Arich. Seven of the 10 sites considered were ranked as promising sites for offshore wind power generation, with wind power densities above 420 W/m2 at 100 m in height. Additionally, the change in signs in the variability toward the middle of the ERA5 record, which was seen at all locations and was also evident in the observations, did not significantly affect the yearly wind power density. However, the seasonal distribution of the latter was modified according to the local features of the seasonal variability.

Assessment of Offshore Wind Power Potential and Wind Energy Prediction Using Recurrent Neural Networks

In recent years, Taiwan has actively pursued the development of renewable energy, with offshore wind power assessments indicating that 80% of the world’s best wind fields are located in the western seas of Taiwan. The aim of this study is to maximize offshore wind power generation and develop a method for predicting offshore wind power, thereby exploring the potential of offshore wind power in Taiwan. The research employs machine learning techniques to establish a wind speed prediction model and formulates a real-time wind power potential assessment method. The study utilizes long short-term memory networks (LSTM), gated recurrent units, and stacked recurrent neural networks with LSTM units as the architecture for the wind speed prediction model. Furthermore, the prediction models are categorized into annual and seasonal patterns based on the seasonal characteristics of the wind. The research evaluates the optimal model by analyzing the results of the two patterns to predict the power generation conditions for 1 to 12 h. The study region includes offshore areas near Hsinchu and Kaohsiung in Taiwan. The novelty of the study lies in the systematic analysis using multiple sets of wind turbines, covering aspects such as wind power potential assessment, wind speed prediction, and fixed and floating wind turbine considerations. The research comprehensively considers the impact of different offshore locations, turbine hub heights, rotor-swept areas, and wind field energy on power generation. Ultimately, based on the research findings, it is recommended to choose the SG 8.0-167 DD wind turbine system for the Hsinchu offshore area and the SG 6.0-154 wind turbine system for the Kaohsiung offshore area, serving as reference cases for wind turbine selection.

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

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

Assessment of Wind and Solar Power Potential and Their Temporal Complementarity in China’s Northwestern Provinces: Insights from ERA5 Reanalysis

In the quest to scientifically develop power systems increasingly reliant on renewable energy sources, the potential and temporal complementarity of wind and solar power in China’s northwestern provinces necessitated a systematic assessment. Using ERA5 reanalysis data for wind speed and solar irradiance, an evaluation was carried out to determine the potential and spatial distribution of wind and solar power across these provinces. Land use types and terrestrial surface slopes were considered in gauging this potential. Theoretical wind and solar power outputs were then compared to understand their complementarity on annual, monthly, and hourly temporal scales. This exploration utilized methodologies including rank correlation coefficients, crossover frequency analysis, and standard deviation complementarity rates. Areas such as the Tarim Basin, Jungar Basin, and the northeastern part of Xinjiang, northwestern Qinghai, and northern Gansu were identified as having significant wind and solar power potential, with wind power densities reaching as high as 600 W/m2 and solar irradiance surpassing 2000 kWh/m2. In these energy-rich areas, the distinct complementarity between theoretical wind and solar outputs was discerned. On an annual scale, the complementarity appeared weakest, with only 7.48% of the combined provinces’ area showing medium-level complementarity. On a monthly scale, conversely, a pronounced complementarity was displayed, especially during the March–May and October–November periods. When evaluated on an hourly basis, an impressive 63.63% of the total output duration exhibited complementary characteristics.

Assessment of wind power potential in the North region of Malaysia, Chuping Perlis

The wind turbines is a main device that convert the kinetic energy from blades to electrical energy. Before installing wind turbines, the Weibull probability distribution must be calculated to determine the certain wind speed probability. Many problems will come if there no analysis the characteristics of wind in selected location, such as wind speed that not suitable for building wind farm to supply the population in that area. Shape and scale factors, which be controlled in a variety of ways, influence the Weibull distribution. Many studies have looked into which of the various Weibull parameter estimation methods is the most dependable. However, because the results of these investigations were inconsistent, research into more trustworthy Weibull parameter estimation methods is still ongoing. An analysis of data collected Chuping, Perlis for two years was conducted in this study (from 2018 to 2019). By using statistical analysis to evaluate the Weibull distribution method, this study used three methods to compared the Weibull parameters and identified the most reliable and effective method to obtain the Weibull probability distribution by using a three approach that compares the variances of RMSE, MSE and R2, which provides comprehensive insight into level error and volatility. Modified maximum likelihood method, graphical method, and power density method are the three methods used in this study. Therefore, the graphical method has the best accuracy in the wind speed distribution prediction, several methods such as the modified maximum likelihood method, and the power density method have the worst prediction of the wind speed distribution based on all the statistical method variances for this region.

Wind Power Potential Assessment at Different Locations in Lebanon: Best–Fit Probability Distribution Model and Techno-Economic Feasibility

The objective of the current paper is to evaluate Lebanon's wind energy generation potential as an alternative solution to the electricity supply to households and to enhance sustainable technological development. Firstly, the paper aims to investigate the appropriateness of 44 distribution function models for the evaluation of wind speed characteristics and compared them with popular models at 12 locations in Lebanon for the first time. The results showed that Wakeby and Beta distribution functions gave the best fit to the actual data for most locations. Secondly, the techno-economic and environmental feasibility assessment for 10MW grid-connected wind farms was developed based on variations in financial parameters using RETScreen Experts software. The findings demonstrate that the proposed power plant is both technically and financially feasible. It was found that Ain ed Dabaa is the most viable location for the installation of a wind farm.

Assessment of the offshore wind power potential of the Black and Azov Sea

Assessment of the offshore wind power potential of the Black and Azov Sea

China's provincial wind power potential assessment and its potential contributions to the "dual carbon" targets.

Wind power development is one of the important measures to achieve China's committed dual carbon targets (carbon peak before 2030 and carbon neutrality before 2060). This study assessed the technical and economic potential of China's onshore and offshore wind power potential through Geographic Information System (GIS) layer overlay and raster calculations. Based on the assessment, provincial contributions of wind power under the 'dual carbon' targets are also estimated. The results show that (a) the technical potential of China's wind power is 25.57PWh/year, and the economic potential is 11.69PWh/year. Fifty-six percent of the potential is located in Inner Mongolia, Xinjiang, and Gansu provinces, while 75% of the offshore potential is distributed over the coastal provinces (Liaoning, Zhejiang, Fujian, Guangdong, and Shandong). (b) Ten provinces, including Anhui, Jiangxi, and Henan, have insufficient remaining economic potential. In comparison, five provinces, including Jiangxi, Henan, Hubei, Yunnan, and Ningxia, have advanced wind power development, with 2020 as the base year. (c) For carbon neutrality, China's potential contributions of wind power may need 1088-2620GW by 2060. Eight provinces can constitute 72% of the new potential contributions. China may need approximately 9.23 trillion CNY (2020 constant price) investment in wind power for the contributions.

Assessment of wind and photovoltaic power potential in China

Decarbonization of the energy system is the key to China’s goal of achieving carbon neutrality by 2060. However, the potential of wind and photovoltaic (PV) to power China remains unclear, hindering the holistic layout of the renewable energy development plan. Here, we used the wind and PV power generation potential assessment system based on the Geographic Information Systems (GIS) method to investigate the wind and PV power generation potential in China. Firstly, the high spatial-temporal resolution climate data and the mainstream wind turbines and PV modules, were used to assess the theoretical wind and PV power generation. Then, the technical, policy and economic (i.e., theoretical power generation) constraints for wind and PV energy development were comprehensively considered to evaluate the wind and solar PV power generation potential of China in 2020. The results showed that, under the current technological level, the wind and PV installed capacity potential of China is about 56.55 billion kW, which is approximately 9 times of those required under the carbon neutral scenario. The wind and PV power generation potential of China is about 95.84 PWh, which is approximately 13 times the electricity demand of China in 2020. The rich areas of wind power generation are mainly distributed in the western, northern, and coastal provinces of China. While the rich areas of PV power generation are mainly distributed in western and northern China. Besides, the degree of tapping wind and PV potential in China is not high, and the installed capacity of most provinces in China accounted for no more than 1% of the capacity potential, especially in the wind and PV potential-rich areas.

Integrated assessment of offshore wind power potential using Weather Research and Forecast (WRF) downscaling with Sentinel-1 satellite imagery, optimal sites, annual energy production and equivalent CO2 reduction

This study aims to assess the offshore wind power potential in Cambodia using the WRF and Sentinel-1 level 2 ocean (L2 OCN) products, to evaluate potential sites and to estimate the annual energy production (AEP) and equivalent CO2 reduction. The model is initially calibrated and validated against observed onshore winds at four weather stations before its main simulation for the two-years study period of 2018–2019. As a result, the spatially averaged annual wind speed errors between the WRF and L2 OCN data are 0.70 m/s for mean bias error and 0.79 m/s for root mean square error, indicating good model performance. The annual mean wind speeds over the Cambodian Sea are 5.15 m/s, 5.20 m/s and 5.27 m/s at 80 m, 100 m and 140 m above sea level, respectively. Then, analytic hierarchy process (AHP) is applied to evaluate the optimal sites for offshore wind power generation. The most potential areas in Cambodian sea are along Kampot and Kep shoreline. The resulting AEPs are 11,949.02 GWh for 80-m V112 turbines, 20,013.34 GWh for 100-m V164 turbines and 31,880.48 GWh for 150-m HX12 turbines, which could reduce CO2 emissions by 5.48 Mt-CO2, 9.18 Mt-CO2 and 14.63 Mt-CO2 per year, respectively. If 10% of the total AEP could be generated by 2030, the offshore wind source would contribute to 1.95%, 3.27%, or 5.20% of the country's electric demands forecasted for 2030. The integrated assessment methodology and resources adopted in this study can be applicable to other regions particularly where offshore measurements are not readily available.

Wind power resource assessment and wind-hydrogen generation potential: a case study

ABSTRACT A case study of assessing wind power generation potentials for seven locations in a district of India is presented in the paper with special focus on small wind turbines with low cut in speeds operating in hybrid complementary mode with a hydrogen generation system. The major motivation for the study is to accelerate the efforts for the region toward carbon neutral status. The wind speeds are obtained for one hour time intervals for all days for 20 years during the time period 2001–2020 for the seven locations from European Center for Medium-Range Weather Forecasts Reanalysis v5 (ERA-5) data. Mean wind speeds for the period are analyzed and wind rose diagrams are generated using the WindroseAxes library in Python environment. Wind power potential assessment has been carried out using the data. From the analysis, it has been noted that the coastal and southern regions of the district get higher wind speeds and have higher power generation potential. Analysis has shown that based on the Beaufort scale, usually the available wind speed is in the calm to moderate breeze types (<0.3–8 m/s). Study has shown that deployment of small wind turbines may be beneficial, to potentially facilitate decentralized power generation. A small wind turbine with cut-in speed 2.5 m/s and cutoff speed 15.5 m/s is selected for the study. The region has a generic upper speed availability of 8 m/s. The corresponding wind power potential values are 0.84 and 27.58 kW. Hydrogen, considered as an energy carrier, and a clean fuel for the future, is proposed as an option for complementary energy storage. Hydrogen, generated from wind energy through electrolysis, may be stored and used to meet the peak power demands. A preliminary analysis of the hydrogen generation potential of the region via the power-to-gas route is also presented. The analysis also points to the necessity for developing small wind turbines, which can generate power at lower cut-in speeds, for maximum utilization of wind power generation potential. The methodology can be adapted to other regions.

Assessment and validation of wind power potential at convection-permitting resolution for the Caribbean region of Colombia

High-resolution wind power assessments, validated with ground data, are key for wind energy projects in regions of promising potential such as the Colombian Caribbean. This study assesses the annual wind power for this region at convection-permitting resolution, using the Weather Research and Forecasting (WRF) model with two nested domains (9 km and 3 km) and 50 vertical levels with 15 min outputs for the northern coast of South America. WRF simulations with three planetary boundary layer schemes (YSU, MYJ and QNSE) perform considerably better than ERA5 reanalysis at representing ground observations of near surface winds in the months with the strongest and weakest winds. WRF accurately simulates phase and amplitude of observed wind diurnal cycles, extreme values, and hodographs in terms of correlations, standard deviations, bias and Weibull distributions. The QNSE scheme served to validate the Wind Power Density (WPD) observed at ground stations, using variations of air density to improve the calculations. The assessment shows areas within the domain with WPD at 100 m up to 1200 Wm-2 onshore and 2000 Wm-2 offshore, with annual values above 400 Wm-2. In this regard, WRF provides a more detailed framework of the atmospheric dynamics for this part of the Caribbean region.

Assessment of Wind Characteristics and Wind Power Potential of Gharo, Pakistan

The objective of this research work is to assess the wind characteristics and wind power potential of Gharo site. The wind parameters of the site have been used to calculate the wind power density, annual energy yield, and capacity factors at 10, 30, and 50 m. The wind frequency distribution including seasonal as well as percentage of seasonal frequency distribution has been investigated to determine accurately the wind power of the site. The coefficient of variation is calculated at three different heights. Also, economic assessment per kWh of energy has been carried out. The site-specific annual mean wind speeds were 6.89, 5.85, and 3.85 m/s at 50, 30, and 10 m heights with corresponding standard deviations of 2.946, 2.489, and 2.040. The mean values of the Weibull k parameter are estimated as 2.946, 2.489, and 2.040 while those of scale parameter are estimated as 7.634, 6.465, and 4.180 m/s at 50, 30, and 10 m, respectively. The respective mean wind power and energy density values are found to be 118.3, 92.20, and 46.10 W/m2 and 1036.6, 807.90, and 402.60 kWh/m2. As per cost estimation of wind turbines, the wind turbine WT-C has the lowest cost of US$ Cents 0.0346/kWh and highest capacity factors of 0.3278 (32.78%). Wind turbine WT-C is recommended for this site for the wind farm deployment due to high energy generation and minimum price of energy. The results show the appropriateness of the methodology for assessing the wind speed and economic assessment at the lowest price of energy.

Statistical Analysis of Wind Speed Data and Assessment of Wind Power Density Using Weibull Distribution Function (Case Study: Four Regions in Iraq)

Assessment of accurate wind power potential is very essential before planning to implement wind power conversion system in a particular site. In order to determine the wind power potential, a statistical analysis of wind speed measurements is required. The main objective of this paper is to analyse the daily measured wind speed data at the height of 10 m and 50 m over the ground for four regions (Al-Rutba, Sinjar, Al-Qa’im and Al-Bayji) in Iraq using the Weibull distribution function. The wind data that is used in this research is collected from NASA surface meteorology. The Weibull parameters for the individual month have also been computed. This statistical analysis is programmed in MATLAB environment to evaluate the monthly and yearly wind power density of the selected areas. In addition, the wind power classes for the four locations will be determined. The results explain the months that have maximum power wind generation in the four regions. According to the calculated yearly wind power density, it can be concluded that the selected areas are between marginal to fair for wind power generation.

Assessment of wind power potential in the coastal region of Tamil Nadu, India

Tamil Nadu is among India's most developed and industrialized states. Onshore wind is the fastest-growing, with Tamil Nadu contributing 23 percent of the total wind capacity installed in India at 37,505 MW. This paper aims to present a preliminary assessment of the potential for wind power to the Tamil Nadu coastal region as estimating the wind characteristics is the first essential step in evaluating a wind energy project. Therefore, it is important to have a thorough understanding of the wind resource. To assess the wind resource, ground-based meteorological masts measurement data from seven selected sites have been used with a wind power density of more than 200 W/m2 spanning between one and two years of data at the height of between 20 m and 100 m. Data is extrapolated to 100 m (at sites where data is not available at 100 m) and derived mean wind speed, average wind density, and Weibull parameters. All seven sites reveal excellent potential with wind power density ranging from 208.64W/m2 in Muttom to 684.2 W/m2 in Kanyakumari, which is the desired electricity generation parameter for the wind turbine option. The exceptional mean wind speed & average wind power density at Dhanushkodi has also been found to be 7.60 m/s & 362.18 W/m2 at 10 m. At all sites, the diurnal patterns were consistent with the coastal regions' trends. The analyzed data is the preliminary characteristic of wind potential, which helps in the conversion of wind power and identifying the state's actual wind potential.

Wind energy potential and economic analysis with a comparison of different methods for determining the optimal distribution parameters

Abstract Pakistan is one of the countries heavily dependent on hydrocarbon fuel for energy production which is causing a severe climate change; however, wind energy seems to be a long-term solution. Various statistical distributions have been used to draw the analysis of wind data, but the selection of an optimum method has been a challenge. This work is an assessment of wind power potential of a site located near the southern coast of Pakistan. Data collected in two years is analyzed at four heights using three variations of Weibull parameters. Weibull parameters computed through the proposed mean bias error-based artificial intelligence grey wolf optimization were compared with the computations through Rayleigh and Justus’s empirical numerical methods. Root mean square error, determination of coefficient, and mean bias error, are computed to validate and compare the computed results. The wind characteristics like most probable and Maximum energy-carrying wind are found to be in excellent compatibility with most of the wind turbines that could be used on the site. The results obtained along with a brief cost analysis, for eight selected wind turbine systems, show that the considered site is suitable for the production of a wind power project.

Wind Power Potential Assessment for Three Locations in New Zealand

This paper proposes and evaluates an optimized system of offshore wind turbines operating as a renewable energy generating unit in New Zealand. A comprehensive simulation model has been set up, using several available commercial software packages to test performance, capacity and efficiency of the proposed system. Available wind records have been used to select three coastal regions which are suitable for wind energy generation and conduct simulation model runs at three locations. Findings so far have been firstly that horizontal axis wind turbines are the most suitable for offshore installation; secondly that a direct current microgrid appears suitable for linking to the onshore electricity supply network; thirdly, many coastal sites can be ruled out because generating capacity is too low or because site factors preclude installation; and fourthly there are some sites where simulation results indicate good potential for offshore wind energy and other site factors do not preclude development. Evaluation of the economic feasibility and returns is now in progress.