Wind Power Potential Research Articles

Solar Energy Challenges for Rural Electrification: Case Study of Lower-Shabelle Region of Somalia

Renewable energy is a promising energy source for sustainability to meet the demands of clean energy in the future and the alternative way of fusil fuels. Somalia has great potential for alternative energy sources, particularly solar and wind power. Apart from that, there are challenges related to the implementation of this alternative energy source especially solar energy for rural electrification in the Lower-Shabelle region in Somalia which can be categorized as social awareness, financial difficulties, and technical obstacles. The aim of this research is to identify and evaluate the challenges of solar energy for rural electrification in Lower-Shabelle region in Somalia, the current energy sources that have been utilized and the possible future strategies that should be in Somalia. This research project is conducted through a survey using an online questionnaire that has been distributed in Afgooye district in Lower-Shabelle region in Somalia. The statistical model of IBM SPSS version 25 was used to study the descriptive analysis of the collected data, reliability analysis and distribution test analysis. Based on the statistical analysis, the results indicated the significance impact of scattered settlement pattern of rural people in this area as 46% of respondents agreed, while 32% participants strongly agreed and 26% agreed towards the impact of traditionalway of living of rural people and dependence on conventional energy source. A significantpercentage of the respondents have shown strong agreement 39% and agreement of 34% regarding the impact of higher cost of importing off-grid solar products. Additionally, a significant number (46.1%) of them have responded with agreement on low-rate return of from solar projects in the rural areas. Furthermore, respondents recognized the effect of shortage or lack of technical knowledge of solar technologies in this region, with 38% of them in agreement and 36% agreed. Finally, respondents widely agreed about other technical related to solar technologies efficiency like battery overcharge and poor layout of solar panels where most of the respondents agreed about these challenges.

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.

Unlocking wind power potential: The pivotal role of R&D investment in boosting wind power enterprise performance

Boosting the core competitiveness and operational efficiency of enterprises while minimizing costs through strategic R&D investments is crucial for the advancement of wind power companies. This paper examines data from listed wind power enterprises from 2015 to 2023 and develops a detailed multiple linear regression model for comprehensive analysis. Goldwind Science & Technology Co., Ltd. is used as a case study. The paper explores the impact of R&D investment on wind power enterprise performance through empirical and case analyses, revealing several key insights. First, both R&D intensity and per capita R&D investment positively affect wind power enterprise performance, with R&D intensity having a more substantial impact. Second, the case study of Goldwind demonstrates a clear positive correlation between R&D intensity, per capita R&D investment, and corporate performance. Third, the impact mechanism analysis shows that increased R&D investment enhances Goldwind's technological advancements, leading to new products that meet market demand and improve enterprise performance. Based on these findings, the paper proposes policy recommendations, including increasing R&D investment and recruiting more scientific researchers.

Towards Sustainable Energy Generation: Analyzing Wind Power Potential, Financial Viability, and Environmental Friendliness at Nok Kundi, Baluchistan

Pakistan is blessed with versatile renewable energy resources, solar and wind energies. Coastal areas of Sindh and Balochistan are the richest sources of wind energy, where adequate wind speeds are available for electricity generation. The Nok Kundi region of Balochistan has outstanding year-round wind speeds and tremendous potential for wind power generation. This study explores wind power potential, financial viability, and mitigation of carbon footprints. In this regard, simulations on the analysis of wind power potential, and financial viability are conducted in the ETAP and HOMMER software respectively. The data regarding the energy demand of Nok Kundi is obtained from Quetta Electric Supply Company (QESCO) Quetta and the data on wind speeds is obtained from the World Weather Online platform. The ETAP software is used to conduct the load flow analysis of a grid-connected system. The full financial analysis considered the state-of-the-art wind power installation site of 40 megawatts capacity to meet Nok Kundi's energy demand. Furthermore, the simulations are conducted in two scenarios i.e. isolated and grid-connected systems. Financial assessments (including the calculation of required capital and operating expenses) are conducted for both isolated and grid-connected systems by using HOMER software. According to the findings of the research, the grid-connected system provided significant benefits in terms of revenues and seamless grid connections. The proposed wind power plant can potentially help reduce the carbon footprints. The present research concluded that the proposed wind power plant can produce 47,581,913 kWh/year and at the same time can mitigate the generation of 47.20 million Kg of carbon/year and an average of 3.93 million Kg/month. This research contributes to providing a detailed feasibility of wind power plants from financial and environmental perspectives. This study addresses Sustainable Development Goal# 13 (SDG 13: Climate Action).

The utilization of renewable energy and the economic potential of offshore wind power supported by digital finance

Colombia is well-positioned for the development of sustainable energy due to its abundance of natural resources, which include water, wind, and sun. Regulating the safe and sustainable use of offshore wind energy, which is considered non-conventional, is lacking in the nation, nonetheless. The development of offshore wind technology in Colombia shows potential to meet energy needs during dry hydrological conditions and El Niño/Southern Oscillation events when the hydroelectric system power supply is low. This study examines global initiatives that have encouraged nations to develop plans for cutting their CO2 emissions, stressing both their successes and shortcomings in putting offshore wind technology into practice. An examination of Colombia's renewable energy administrative framework finds a lack of data required to carry out offshore wind projects. Furthermore, a review of previous research on marine energy emphasizes how important it is to expand our knowledge of offshore wind generation. Although the majority of local renewable energy projects concentrate on terrestrial sources, an analysis of wind speed and wind power density in Colombia at different altitudes shows promising magnitudes and good trends.Digital finance plays a crucial role in this context by providing innovative funding mechanisms, enhancing financial accessibility, and reducing investment risks through improved financial technologies. These advancements support the mobilization of capital necessary for the development and expansion of offshore wind energy projects.As a result, the technical, economic, administrative, and legal data pertinent to renewable energy in Colombia is compiled in this study. It proposes to provide information to stakeholders involved in decision-making processes and promotes the possible installation of offshore wind farms in regions close to Colombia's Caribbean coast. Because of its plentiful resources, Colombia offers a great chance to implement offshore wind energy technology, which will lessen dependency on fossil fuels and provide a backup energy source in case of supply shortages. The integration of digital finance is key to unlocking the economic potential of these projects, ensuring sustainable and scalable energy solutions for the future.

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.

Impact of global warming on wind power potential over East Asia

Wind power, which is the fastest–growing renewable energy source, is directly influenced by weather or climate conditions. This study examined the recent changes and future projections under different carbon emission levels in the wind power potential (Wpot) over East Asia using the ERA5 datasets and high–resolution multiple regional climate models (RCMs). Because wind conditions are constantly changing, we employed the wind velocity range–based approach for estimating the Wpot using the shortest time interval data available, which is the three–hourly wind velocity. Seasonal Wpot climatology of area–averaged Wpot over East Asia was the highest in spring. The average Wpot showed the highest values in northern China and southern Mongolia throughout all seasons, while recent changes in the Wpot over East Asia were characterized by overall increases in spring, autumn, and winter, but a decrease in summer. However, there were large regional variabilities. Northern China and southern Mongolia are expected to remain significant regions with high Wpot in the future. This is due to the large contribution of the frequency of optimal wind conditions, ranging from 12 m s−1 to 25 m s−1, to the Wpot in these regions, both currently and in the future. The average future Wpot in East Asia is expected to show no significant changes in both the low and the highest carbon emission scenarios compared to the present. However, it is projected that regional disparities in the Wpot will increase in a scenario with enhanced emission by the end of the 21st century.

Characterizing Supply Reliability Through the Synergistic Integration of VRE towards Enhancing Electrification in Kenya

Decentralized electrical power systems, driven by variable renewable energy sources such as solar PV and wind, have the potential to provide accessible and sustainable energy, contributing to the realization of a zero-carbon transition. However, these sources are susceptible to extreme weather conditions, presenting a challenge to the reliability of the power system. With abundant resources and a significant rural population lacking access to electricity, Africa has emerged as a key area for research on variable renewable energy-based electricity generation. Despite this focus, there remains a substantial gap in understanding at regional-scale the potential and variability of solar and wind power across various time scales, as well asthe impact of available resource synergy. Thisstudy aims to bridge this knowledge gap by conducting comprehensive simulations of hybrid wind and solar energy systems, both on-grid and off-grid, across 20 geographically diverse locations in Kenya. Using high-resolution hourly time step data, we examine the effect of resource complementarity on system reliability at varying time scales: daily, monthly and annually. The study findings shows the available VRE resource exhibit moderate tendency for complementarity, and optimizing their deployment can reduce hourly variability by 20%, significantly enhancing supply reliability, especially in the northern and eastern regions.

A comprehensive approach to wind turbine power curve modeling: Addressing outliers and enhancing accuracy

This research presents a comprehensive approach to improving the accuracy of wind turbine power curve (WTPC) modeling. The WTPC is a critical tool for monitoring wind turbine performance and estimating wind power potential, but current models have limited ability to capture the complex relationship between wind speed and power output. To address these limitations, this study implements a dual-pronged refinement of WTPC modeling. First, an innovative data preprocessing technique is introduced, using a 97 % confidence interval around a KNN-estimated WTPC constructed using the Laplace distribution to meticulously eliminate prominent outliers. Second, a novel WTPC modeling approach based on quantile regression is adopted, accounting for the asymmetric error characteristics in the loss function. Four distinct quantile regression models are developed, including three tree-based algorithms - decision trees, random forests, and gradient boosting - and a deep learning-based quantile regression neural network. Comparative analysis against ten established parametric and nonparametric techniques confirms the superiority of the proposed models, with the decision tree quantile regression model achieving the lowest validation errors. The proposed techniques are validated on two real-world datasets from operational wind turbines in Turkey and China, demonstrating significant improvements in WTPC modeling accuracy compared to conventional methods. Overall, this study successfully presents a comprehensive modeling approach that addresses outliers and leverages quantile regression to significantly enhance WTPC accuracy.

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

An advanced farm flow estimator for the real-time evaluation of the potential wind power of a down-regulated wind farm

This work aims at verifying the predictions of OnWaRDS, an open-source wake modelling framework that captures the main features of the wake dynamics, including its meandering, in ancillary services scenarios. OnWaRDS brings together Lagrangian flow modeling and flow sensing and runs in parallel with a wind farm environment (here Large-Eddy simulations coupled to an Actuator Disk model (LES-AD)) in order to use the available rotor states to predict the flow field. The performances of OnWaRDS are first assessed when it runs synchronously with the LES-AD of a down-regulated wind farm and tends to mimic the LES-AD behavior. This synchronous mode implies a continuous feeding of the wake model with LES-AD rotor data. Then, OnWaRDS is used in a predictive mode, in order to predict an alternate reality for the wind farm. In this study, OnWaRDS aims at evaluating, in real-time, what the potential power production would be when the LES-AD is down-regulated to provide operating reserve capacity to the electricity network. Switching to a predictive mode implies that certain measurements at the wind turbine level can no longer be used, because the flow and the rotor behavior change between LES-AD and OnWaRDS. The second part of this study thus aims at verifying the predictions of OnWaRDS, and highlighting the impact of switching from a synchronous to a predictive mode in OnWaRDS.

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.

An evaluation of the reliability of the Weather Research Forecasting (WRF) model in predicting wind data: a case study of Burundi

BackgroundThe Weather Research and Forecasting (WRF) Model is an exceptional software for mesoscale climate modeling. It is extensively used to simulate key meteorological variables, including temperature, rainfall, and wind.MethodsThis study thoroughly examined the effectiveness of the WRF model in generating precise wind data for assessing the potential of wind power in Burundi. A meticulous evaluation of various combinations of model physics parameterization schemes was conducted to ensure accuracy.By comparing the simulated data with measurements from four meteorological stations and utilizing statistical metrics such as root-mean-square error (RMSE) and bias, the accuracy of the WRF model was determined.Results The findings of the study uncovered that utilizing WRF Single-Moment 3-Class (WSM3) for microphysics, Grell-Devenyi ensemble for cumulus physics, and Yonsei University for planetary boundary layer yields highly accurate wind data results for Burundi.Furthermore, the WRF model was utilized to create detailed seasonal and annual mean wind maps with a high resolution. ConclusionThese maps demonstrated that the western part of Burundi experiences higher wind speeds (ranging from 4 to 9.7 m/s) during the dry seasons revealing the potential for wind energy harvesting in the different areas of Burundi.

Wind energy resource assessment and wind turbine selection analysis for sustainable energy production

The objective of this study is to perform an analysis to determine the most suitable type of wind turbine that can be installed at a specific location for electricity generation, using annual measurements of wind characteristics and meteorological parameters. Wind potential analysis has shown that the analyzed location is suitable for the development of a wind farm. The analysis was carried out for six different types of wind turbines, with a power ranging from 1.5 to 3.0 MW and a hub height set at 80 m. Wind power potential was assessed using the Weibull analysis. The values of the scale coefficient c were determined, and a large monthly variation was observed, with values ranging from 1.92 to 8.36 m/s and an annual value of 4.95 m/s. Monthly values for the shape coefficient k varied between 0.86 and 1.53, with an annual value of 1.07. Additionally, the capacity factor of the turbines was determined, ranging from 17.75 to 22.22%. The Vestas turbine, with a nominal power of 2 MW and a capacity factor of 22.22%, proved to be the most efficient wind turbine for the specific conditions of the location. The quantity of greenhouse gas emissions that will be reduced if this type of turbine is implemented was also calculated, considering the average CO2 emission intensity factor (kg CO2/kWh) of the national electricity system.

Temporal Complementarity Analysis of Wind and Solar Power Potential for Distributed Hybrid Electric Generation in Chile

We evaluate the temporal complementarity in daily averages between wind and solar power potential in Chile using Spearman’s correlation coefficient. We used hourly wind speed and solar radiation data for 176 geographic points from 2004 to 2016. The results allow us to identify four zones: Zone A1 on the coast and in the valleys in the north of Chile between latitudes 18° S and 36° S, with moderate positive correlation; Zone A2 in the north Andes between latitudes 25° S and 33° S, with weak negative correlation; Zone B in the center-south part of the country between latitudes 36° S and 51° S with moderate negative correlation; and Zone C in the south, between latitudes 51° S and 55° S with null or weak positive correlation. On the one hand, the interannual analysis shows that Zone A1 keeps uniform correlation values with negative asymmetry, i.e., higher correlation values. On the other hand, there is positive asymmetry in most of the years in Zone A2, i.e., lower (or negative) values of correlation. Zone B shows an interannual oscillation of the median correlation, while Zone C shows a larger dispersion in the interannual results. Significance analysis shows that 163 out of the 176 points are statistically significant, while Zones A1, A2, and B have significant correlations, with Zone C being marginally significant. The results obtained are relevant information for further studies on the location of hybrid generation facilities. We expect our methodology to be instrumental in Chile’s energetic transition to a 100% renewable generation matrix.

Renewable Energy Complementarity (RECom) maps – a comprehensive visualisation tool to support spatial diversification

Abstract. Maps showing the mean wind speed only give an inaccurate indication of the quality of locations for future wind power developments. Calculating the capacity factor and plotting that on a map gives a better indication of the expected mean power output, but the outcome depends on the turbine choice. In this article, we introduce a general step-by-step method for improved visualisation of potential wind power locations. First, the mentioned dependency on turbine choice is compensated for by putting the expected mean power output in relation to the expected mean power output of all other wind parks of the region. This relative capacity factor results in comprehensive wind resource maps and can be plotted for the situation today and also for a future scenario. Since the expected income of a potential wind park is the product of mean power output and mean market value, looking at the relative capacity factor only does not give the full picture. The mean market value is influenced by the merit order effect that is mainly driven by covariance with other wind parks and the capacity factor's relation to production at low-wind moments. A market value factor is introduced that captures the expected mean market value relative to other wind parks, based on a simplified power market model. Finally the Renewable Energy Complementarity (RECom) index is defined, combining the relative capacity factor and market value factor into a single index, resulting in RECom maps. This map can comprehensively show the revenue potential of different locations for potential future wind power developments.

Assessment of Wind Resource and Its Energy Potential in Three States of Northwest Nigeria

The growing need for clean energy to address the environmental issues caused by the usage of fossil fuels results to the need for Nigeria to develop an alternative energy from untapped wind which is in abundance in the region. The aim of this study is to evaluate wind power potential and wind energy resources of three locations (Kaduna, Kano and Katsina) of northwestern part of Nigeria. The Nigerian Meteorological agency (NIMET) provided the wind speed data spanning 25 years (1996-2020). In the analysis, Weibull two-parameter statistical model was used. The distribution of wind speed across Nigeria demonstrates that some areas in the North are equipped to generate wind. Microsoft excel was used in analyzing the wind speed data obtained. In addition, evaluation of the region’s wind energy resources shows that Kano recorded the highest potential, with WPD of 443.03 Wm-2 at 10m AGL with annual WED of 4.921 kWhm-2day-1 while Kaduna recorded the lowest potential of 198.43 Wm-2 with annual WED of 2.093 kWhm-2day-1. As a result, Kaduna is found to be ideal for small scale wind power generation, while Kano and Katsina may be suitable for large scale wind power generation.

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

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

Technoeconomic Analysis of a Wind Power Generation System and Wind Resource Mapping Using GIS Tools: The Case of Twelve Locations in the Commune of Evodoula, Cameroon

This paper performs a technoeconomic analysis of the wind power potential and evaluates the cost of wind power generation in Evodoula, comparing two methods, the conventional method and the uncertainty method based on a comparative spatiotemporal approach using the geographic information system (GIS) software tool. This study is based on satellite wind data measured at 10 m above ground level (AGL) over a 40-year period (1980-2019), by the meteorological service NASA (National Aeronautics and Space Administration)/Goddard Space Flight Center (GSFC). The main objectives are to obtain an appropriate design for a proven optimal location and to assess the viability of wind power at Evodoula. For this type of study, there is little literature available. The optimization of an onshore wind farm and deployment in the wind energy development interest area (ZIDEE) is carried out to obtain a minimum and parsimonious discounted cost production unitary (CPU) of the electricity produced. The results showed that in Okok, a location with a large energy deficit, an onshore wind farm with an electricity generation capacity of 12.5 MW with 5 NORDEX N100-2.5MW wind turbines would have a total energy production (TEP) of about 64.0254825 TWh and a selling price of electricity that would be 0.0034 CAD$/kWh, which is very low compared to the utility price (about 98% cheaper). The total cost of the wind farm would be about $11 million, with a net present cost of about $218 million. The annual profit generated by the wind farm would be over $6 billion. The return on investment (ROI) of the project is estimated at 2880.882%. The constructed onshore wind farm would avoid CO2 emission at over 11 MtCO2,eq/year as the energy generated is from the atmosphere. The wind farm would realize an average annual cash flow estimated at nearly $30 million after 20 years of operation. These savings would allow the installation of CO2 capture systems in conventional power plants. In addition, the analysis of uncertainties and risks was identified and quantified to estimate the confidence levels of the project development results. The risks have been assessed, and we recommend that the total uncertainty of the project is around 15%. The energy values in P75 and P90 are 10.08% and 19.22%, respectively, lower than the energy value in P50 of 11.60 GWh/An. Finally, the main policy recommendations for an inclusive design process are highlighted. The contribution of this study is to assist policy makers in making appropriate decisions in the development and implementation of energy and environmental policy in Cameroon and in many continental areas.