Comparative Study of Wind Energy Potential Estimation Methods for Wind Sites in Togo and Benin (West Sub-Saharan Africa)

Sedzro, Kwami Senam; Salami, Adekunlé Akim; Agbessi, Pierre Akuété; Kodjo, Mawugno Koffi

doi:10.3390/en15228654

Cited by 12 publications

(6 citation statements)

References 76 publications

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“…The new ERA5 was created by the National Centre for Medium Range Weather Forecasting (NCMRWF) as part of the Copernicus Climatically Change Service (CDS). The latest generation of global reanalysis products, ERA5, has a wind speed height of 100 m, a spatial resolution of 0.28125º (31 km), and a temporal resolution of one hour [19,20]. A trustworthy data source that can have an immediate impact on wind energy studies, ERA5 has become well-known around the world.…”

Section: Acquisition Of Data and Its Areamentioning

confidence: 99%

Enhancing Wind Energy Potential Assessment with Three-Parameter Weibull Distribution: A Comparative Analysis using MATLAB

M K,

et al. 2024

E3S Web of Conf.

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To determine the wind energy potential, the probability density function is typically used. For data distribution with modest wind speeds, this paper developed a three-parameter Weibull model. The distribution factors were determined using the maximal likelihood technique. The world renowned, user-friendly programming language Matrix Laboratory (MATLAB) processes all data that needs analysis. A comparison was made between the 3-factor Weibull, the 2-factor Weibull, and Rayleigh through R2 and root mean square error (RMSE). The ECMWF Reanalysis v5 (ERA 5) reanalysis's hourly wind speeds are statistically represented by these three distributions. Due to its placement between the optimal R2 and RMSE, the three-parameter Weibull model achieves good results. Weibull with three parameters has a R2 of 0.9898, Weibull with two parameters has a R2 of 0.9886, and Rayleigh has a R2 of 0.5162. The root-mean-squared errors (RMSEs) for Rayleigh, 2-factor and 3-factor Weibull, are 0.0082 and 0.0070, respectively.

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Section: Acquisition Of Data and Its Areamentioning

confidence: 99%

Enhancing Wind Energy Potential Assessment with Three-Parameter Weibull Distribution: A Comparative Analysis using MATLAB

M K,

et al. 2024

E3S Web of Conf.

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“…direct measurement of wind speed, we can express the wind power of the site under consideration by the equation [12,14]:…”

Section: Scientific Models Of Microgrid Technologiesmentioning

confidence: 99%

Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming and Onsset Method

Moyème,

Yao,

Sedzro

et al. 2024

Preprint

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Optimal planning and design of microgrids is a priority for electrification of off-grid areas. Indeed, according to one of the Sustainable Development Goals (SDG) 7, the UN recommends universal access to electricity for all but, at the lowest cost. To achieve this goal, several optimization methods with different strategies have been proposed in the literature. This paper proposes a microgrid installation and planning model based on a combination of several techniques. Python 3.10, the programming language, has been used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. The results obtained enabled us to simulate the model obtained, with a cluster considered as a case study, based on the elbow and k-means clustering method; then, in a second phase, to size the microgrid with a capacity of 40 kW, by optimizing all the resources available on the site: the example of the various resources in the case of Togo was considered (solar, wind, hydropower). This study therefore highlighted the optimal resources obtained by integrating battery systems by the optimization model formulated on the basis of the various technology costs, such as investment, maintenance and operating costs, based on the technical limits of the various technologies. For the optimum results obtained, solar systems account for 80% of the maximum load considered, compared with 7.5% for wind systems and 12.5% for battery systems. Next, an optimal microgrid connection model was proposed, based on the constraints of a voltage stability limit estimated at 10% of the maximum voltage drop. The substation capacity limit was also taken into account. The results obtained from the case studied enabled us again, to present selective results for load nodes in relation to the substation node. And finally, the spatial technology planning tool made it possible to obtain results on the different strategies for the various technologies required for the electrification planning study of the Togo case. The various results obtained from the different techniques provide the leads needed for a feasibility study for optimal electrification of off-grid areas, using microgrid systems.

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“…Wind energy is the energy produced from wind movement, converted from kinetic energy into electrical energy through wind turbines (the principle of energy transformation). Determining the sites of wind farms depends heavily on studying the activity of wind movement in the region, which is measured by geographic studies, satellites, and sensor monitoring and measuring devices [2,8,9].…”

Section: Wind Energy Related Workmentioning

confidence: 99%

Wind Farm Power Prediction Considering Layout and Wake Effect: Case Study of Saudi Arabia

2023

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The world’s technological and economic advancements have led to a sharp increase in the demand for electrical energy. Saudi Arabia is experiencing rapid economic and demographic growth, which is resulting in higher energy needs. The limits of fossil fuel reserves and their disruption to the environment have motivated the pursuit of alternative energy options such as wind energy. In order to regulate the power system to maintain safe and dependable operation, projections of current and daily power generation are crucial. Thus, this work focuses on wind power prediction and the statistical analysis of wind characteristics using wind data from a meteorological station in Makkah, Saudi Arabia. The data were collected over four years from January 2015 to July 2018. More than twelve thousand data points were collected and analyzed. Layout and wake effect studies were carried out. Furthermore, the near wake length downstream from the rotor disc between 1 and 5 rotor diameters (1D to 5D) was taken into account. Five robust machine learning algorithms were implemented to estimate the potential wind power production from a wind farm in Makkah, Saudi Arabia. The relationship between the wind speed and power produced for each season was carefully studied. Due to the variability in the wind speeds, the power production fluctuated much more in the winter. The higher the wind speed, the more significant the difference in energy production between the five farm layouts, and vice versa, whereas at a low wind speed, there was no significant difference in the power production in all of the near wake lengths of the 1D to 5D rotor diameters downstream from the rotor disc. Among the utilized prediction models, the decision tree regression was found to have the best accuracy values in all four utilized evaluation metrics, with 0.994 in R-squared, 0.025 in MAE, 0.273 in MSE, and 0.522 in RMSE. The obtained results were satisfactory and provide support for the construction of several wind farms, producing hundreds of megawatts, in Saudi Arabia, particularly in the Makkah Region.

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Comparative Study of Wind Energy Potential Estimation Methods for Wind Sites in Togo and Benin (West Sub-Saharan Africa)

Cited by 12 publications

References 76 publications

Enhancing Wind Energy Potential Assessment with Three-Parameter Weibull Distribution: A Comparative Analysis using MATLAB

Enhancing Wind Energy Potential Assessment with Three-Parameter Weibull Distribution: A Comparative Analysis using MATLAB

Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming and Onsset Method

Wind Farm Power Prediction Considering Layout and Wake Effect: Case Study of Saudi Arabia

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