Abstract
The study presented in this article focuses on the temporal dynamics of wind energy production at the Taïba Ndiaye wind farm in Senegal, with a capacity of 158.7 MW. The monthly and seasonal distribution of production shows a strong trend, with maximums recorded between December and May (winter and spring) at around 1800 MWh, and minimums between July and November (summer and autumn) with production below 500 MWh. The diurnal cycle representation exhibits variation with a marked cycle, particularly between November and April. Night-time production is higher than daytime production by more than 43%. The effects of 100-m wind on the farm production are also analysed and show a positive correlation between wind speed and production throughout the year. Production peaks observed in winter and spring are caused by strong winds (approximately 8.5 m/s), while the lowest levels recorded during the summer season are due to weather conditions characterized by weak winds (less than 4 m/s). Similarly, optimal wind directions are observed in winter and spring, periods of maximum production, when the winds blow between the northwest and northeast.
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1 Introduction
Faced with increasing global energy demands, the development of renewable energies has become a crucial necessity to tackle the depletion of conventional fossil fuels and combat the rise in greenhouse gas emissions responsible for climate change [1]. Several countries have made significant commitments to reduce reliance on fossil fuels in electricity production and increase the use of clean energy. For example, Germany has pledged to achieve near energy independence through renewable energy sources by 2050 [2]. Similarly, in 2012, the Saudi Arabian government set an ambitious target to cover 30% of the country's electricity demand by 2032 by investing $109 billion in the renewable energy sector [3]. One of the proposed solutions to address the challenge of electricity scarcity for consumers is wind energy, which has emerged as one of the most promising and attractive sources of renewable energy globally. Its widespread availability and reduced environmental impact [4] make it a viable solution to meet the growing electricity needs in many regions around the world. According to the International Energy Agency (IEA), the installed global wind capacity reached 743 GW in 2020, representing a 19% increase compared to the previous year [5].
Wind energy is emerging as a crucial renewable energy source for electricity production in Africa. Several studies have been conducted to assess its potential, mainly focusing on average wind speed and important statistical parameters like annual or monthly Weibull parameters [6]. Among these studies exploring wind potential in different regions, the northwest of Africa has been the subject of research to evaluate wind potential in countries such as Mauritania, using data collected over multiple years [7]. Senegal, a country located in West Africa, has adopted wind energy to diversify its energy mix and reduce its dependence on imported fossil fuels [8]. Preliminary studies have been conducted to assess the wind potential of various sites along the northwestern coast of Senegal, including Gandon, Kayar, Mboro, Taiba Ndiaye, and Potou, using real data collected over a year [9,10,11]. As a result, the Taïba Ndiaye site, situated in the Thiès region, was chosen as the ideal location for the country’s first wind farm, expected to contribute approximately 15% to the national electricity production [12].
Furthermore, several studies emphasize the importance of meteorological conditions for wind energy production. Research conducted by Delft University of Technology shows that wind energy production is highly dependent on wind conditions, which vary significantly based on the region and season [13]. Similarly, a study by the University of Lisbon reveals that the amount of energy produced by a wind turbine depends on the wind speed it is exposed to [14]. In Senegal, preliminary studies conducted at Taïba Ndiaye before the installation of the wind farm clearly demonstrate that wind variations will have a significant impact on the future wind farm’s production [15]. Therefore, it is essential to consider these specific wind conditions to improve the prediction of wind energy production [16,17,18,19,20].
Therefore, it is important to understand how wind conditions influence wind energy production to improve the performance of wind farms. The Taïba Ndiaye region has been the subject of several preliminary studies. For example, [21] found that the annual average wind speed in Taïba Ndiaye was 6.63 m/s, with a maximum speed of 8.2 m/s. They also estimated that the installed capacity of the large-scale wind farm should be around 500 MW. The installed capacity of the Ndiaye wind farm is reported to be 620 GWh, with a power density of 293 W/m2. They even recommend the use of medium-sized turbines, with a hub height of 80 m and a rated power of 2.5 MW [22, 23] found that the energy production of the Taïba Ndiaye wind farm was 441 GWh during the studied year. They also found that energy production could be higher during the dry season than during the rainy season, with maximum production in December. However, few studies have been conducted on the temporal dynamics of production from this new wind farm.
This article analyses the dynamics of wind power production at the Taïba Ndiaye wind farm and its link to meteorological parameters at different time scales. Production data were evaluated on an annual, monthly, daily, hourly, and intra-day basis to better understand the factors that influence wind power production in this region of Senegal. Production data were also compared to wind conditions at 100 m above ground level to assess the influence of wind power production. This analysis could help improve and predict the performance of the wind farm, promote the development of a sustainable and diversified energy mix in Africa, and reduce greenhouse gas emissions. Moreover, it could serve as a benchmark for wind farms, enabling them to optimize and predict their performances more effectively.
The rest of the paper is organized as follows: the next section describes the Taïba Ndiaye wind farm, and the data used. The main results and discussions are provided in the following section, while the summary and conclusions are presented in the last section.
2 Data and methods
2.1 Presentation of the wind farm and data
The Taïba Ndiaye wind farm is located in a coastal area approximately 85 km northeast of Dakar, Senegal, where the winds are strong and consistent. This allows for maximizing the efficiency of the wind farm and producing a significant amount of renewable electricity. Developed by Lekela Power, a company specialized in renewable energy, the wind farm started producing electricity in 2020 to contribute to the country's goal of reaching a 30% share of renewable energy in electricity production by 2025 [24]. It is considered one of the largest wind farms in West Africa. The wind farm is equipped with 46 wind turbines, each with a capacity of 3.45 MW, for a total capacity of 158.7 MW which represents about 15% of the electricity production capacity of Senegal [25].
Production data for the Taïba Ndiaye wind farm were provided by Lekela Power and include annual, monthly, daily, and hourly data for the year 2021. Wind data at a height of 100 m were obtained from the ERA5 dataset, which is produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) [26]. ERA5 data is based on weather models and has a spatial resolution of 0.25° (approximately 31 km) and an hourly temporal resolution. The quality of ERA5 data has been validated by several scientific studies [27], which have shown that these data are accurate and reliable for wind condition analysis in different regions of the world.
2.2 Methods
This study aimed to analyze the energy production data collected from the Taïba Ndiaye wind farm in Senegal. The primary focus was on the energy production (measured in MWh) of the 46 wind turbines. The adopted approach enabled us to obtain detailed data at a high temporal resolution of approximately 10 min.
In consideration of the wind farm's mast height set at 117 m, ERA5 reanalysis data from the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [28, 29] were utilized for acquiring wind information. The ERA5 dataset provide essential parameters, namely, the latitudinal (u) and meridional (v) components of the wind, which were employed to calculate the wind speed (W). Subsequently, using the derived components, the wind intensity at 100 m (W100) was computed following the Eq. 1 [30, 31]:
and the wind direction θ is given by Eq. 2 [30, 31]:
-
where θ is considered to be 0° for a wind coming from the north, and the angle θ increases clockwise.
-
the atan2 function is an enhanced version of the standard arctangent function, which considers both components (u and v) of the wind to calculate the angle θ. As a result, atan2(u, v) accurately determines the wind direction, regardless of the wind vector's position in the quadrants. Setting θ to 0° for a wind coming from the north, the angle θ increases clockwise.
In conclusion, θ is the angle that indicates the direction of the wind with respect to the north. It ranges from 0° (when the wind comes from the north), to 90° (east wind), 180° (south wind), and 270° (west wind), all the way to 360° (when the wind comes from the north again). This angle measurement helps us understand the wind's orientation and where it is coming from in relation to the north direction.
3 Results and discussions
3.1 Representation of the diurnal cycle at different temporal scales
3.1.1 Monthly variability of the diurnal cycle of production
Figure 1 represents the monthly variability of the diurnal cycle of wind power production from the Taïba Ndiaye wind farm. Firstly, this graph shows peaks in autumn and winter, with a maximum production of 2040 MWh in January. Similarly, the lowest levels are recorded in summer, reaching approximately 430 MWh in September.
Furthermore, a marked diurnal cycle is observed, particularly between November and April. Maximums are recorded during the night, between 8 p.m. and 8 a.m., while minimums occur between 9 a.m. and 7 p.m. From May onwards, there is a considerable drop in production and a reversal of the diurnal cycle trend, which evolves towards a bimodal pattern until August.
3.1.2 Seasonal variability of the production diurnal cycle
The hourly production for winter (December–February), spring (March–May), summer (June–August), and autumn (September–November) is represented in the Fig. 2 to observe seasonal trends of the diurnal cycle. As previously mentioned, the winter season records the production peaks (75.50 MWh), while the rainy season is characterized by minimal production levels, with an average of around 21 MWh. The daily variation shows a clear monomodal trend in winter, spring, and autumn, characterized by night-time production peaks (7 p.m.–8 a.m.) and daytime minimum levels (9 a.m.–6 p.m.). Conversely, during the rainy season, the trend is bimodal with two production peaks, one at 5 a.m. and the other at 6 p.m., separated by a minimum at 8 a.m.
3.1.3 Average diurnal cycle of production at Taïba Ndiaye
In summary, the Fig. 3 illustrates the global diurnal cycle of the Taïba Ndiaye wind farm power production. On average, the production of the farm is 65.11 MW per hour every day. Night-time peaks exceed 80 MW/h between midnight and 8 a.m., while daytime lows are around 40 MW/h between 10 a.m. and 6 p.m. The lowest hourly production is recorded around 2 p.m., with a value of approximately 30 MW.
3.2 Monthly and seasonal distribution of production
Figure 4 shows the monthly variability of the production of the Taïba Ndiaye wind farm in 2021.
There is a very pronounced trend with maximums between December and May, and minimums between July and November. The peak of production is recorded in January, while the smallest value is noted in September.
To further substantiate our findings, we present in the Fig. 5, the seasonal distribution of electricity production from the Taïba Ndiaye wind farm, categorized into winter (December–February), spring (March–May), summer (June–August), and autumn (September–November).
The results confirm those previously reported in Fig. 4, with production peaking during winter and spring, accounting for 40.4% and 33% of the annual production, respectively. Conversely, minimal quantities are recorded during the rainy season, in summer, representing approximately 11.2% of the annual production. Production during the autumn is also low, with a yield of nearly 15.5%. Indeed, the rainy season may lead to weaker winds in certain regions of the Sahel due to modifications in atmospheric conditions [32,33,34].
3.3 Effects of 100 m’ wind on the production of the wind farm
3.3.1 Monthly effects
The present figure (Fig. 6) illustrates the monthly distribution of the diurnal cycle of power production from the power farm (in blue) and the 100 m wind speed (in red) at Taïba Ndiaye. The results clearly show that it is the wind speed (at a height of 100 m) that modulates the power production of the farm. Indeed, both indicators exhibit a positive correlation throughout the year. The month of January corresponds to the maximum values of production (2040 MWh) and wind speed (7.7 m/s), while the month of August corresponds to the minimum values of wind speed (~ 4.5 m/s) and production. It is also observed that the diurnal cycle of production is directly linked to that of the wind, with maxima during the night (9 m/s) and minima during the day (< 6 m/s). Furthermore, the bimodal behavior of production in August can also be attributed to that of the wind speed.
The figure below (Fig. 7) shows the monthly wind roses at the Taïba Ndiaye site. It can be observed that the optimal wind directions for wind turbine production are recorded between the months of November and May, which correspond to the periods of maximum production. Indeed, the optimal direction for a wind turbine is generally between 30° and 60° from its axis, with an angular range where the wind blows regularly and consistently, corresponding to a wind direction ranging from northwest to northeast through north [35,36,37,38,39,40].
The Fig. 7 clearly shows that during the months of maximum production (November to May), the wind direction is within the optimal intervals ranging from northwest to northeast. Starting from June, the wind direction gradually evolves from northwest to west, reaching its most western point around August, which also corresponds to the period of least energy production by the power farm.
3.3.2 Seasonal effects
The Fig. 8 illustrates the comparison between the seasonal distribution of the diurnal cycle of energy production and wind speed. As with the monthly data, it can be observed that the energy production of the Taïba Ndiaye wind farm depends on wind conditions. Indeed, there is a correlation between wind speed and energy production. The peaks in wind speed are observed during winter and spring, which are also the periods of maximum production. During these two seasons, wind speeds exceed 7 m/s, allowing for the generation of more than 63 MWh. Low winds in summer and autumn result in minimal energy production levels. Furthermore, seasonal production follows the diurnal variations of seasonal winds. For autumn, winter, and spring, the trend is unimodal, while for the rainy season in summer, it is bimodal. Finally, the diurnal cycle of these indicators reveals that production levels reach their maximum during the night due to strong winds that blow at that time of day.
The analysis of seasonal wind directions in the Fig. 9 shows consistent trends. Optimal wind directions are observed in winter and spring, periods of maximum production for the wind power farm, when winds blow between the northwest and northeast. On the other hand, during summer and fall (autumn), periods of minimum production, wind directions shift to oscillate between northwest and west.
3.3.3 Analysis of night-time and daytime productions
The Fig. 10 presents the monthly production of our wind power farm for time intervals representing daytime (8 a.m.–6 p.m.) and night-time (7 p.m.–7 a.m.). We can observe that the production during the daytime and night-time follows a similar trend, reaching peaks between December and April and minima between June and November. However, the night-time production is always higher than that of the day, with a more pronounced difference between December and April. Starting in May, the farm produces approximately the same amount of electricity during the day and night. Overall, the wind farm’s production is 43.1% higher at night than during the day.
The graph presented below (Fig. 11) shows a seasonal comparison of power production between daytime and night-time. It is notable that the wind power farm produces more energy during the night than during the day, particularly during the winter months (December to February) and spring (March to May). For example, during the winter, night-time production is twice as high as daytime production. However, during the summer (the rainy season), energy production is approximately equal between day and night.
The wind rose analysis confirms (in Fig. 12) that the winds are always located in the optimal directions mainly between northeast and northwest, both during the day and at night. However, it has been observed that during the night when the winds are stronger, the preferred direction is often north, while during the day, the winds blow more between north and northwest.
4 Conclusion
The aim of this article was to study the temporal dynamics of wind energy production at Taïba Ndiaye wind farm in Senegal using a multi-scale approach. Firstly, the monthly and seasonal distribution of production showed a marked trend, with peaks recorded between December and May (winter and spring), and minimums between July and November (summer and autumn). The diurnal cycle representation showed a pronounced variation, particularly between autumn and winter, with peaks during the night and minima during the day. In fact, night-time production was higher than daytime production by over 43%. Comparison of the 100 m wind speed and the farm production showed a strong positive correlation throughout the year. The maxima in production observed in winter and spring were caused by strong winds, while the lowest levels recorded during the summer season were attributable to the meteorological conditions. The wind direction indicates that the wind mainly blows in the optimal directions, especially in winter and spring, the periods of maximum production for the farm, when the winds are from the northwest to northeast. The results of this study could help better understand the behaviours of wind energy production and optimize the use of this renewable energy source in the region.
Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
References
Daher D H, Gaillard L, Amara M, Lips B, Ménézo C. Suivi expérimental des performances d'une centrale solaire photovoltaïque à Djibouti. In 3ième Colloque International Francophone d'Energétique et Mécanique Les énergies renouvelables et la mécanique appliquée à l'industrie. pp. ART-17–67. ⟨hal-01016734⟩; 2014.
Ryberg DS, Caglayan DG, Schmitt S, Linßen J, Stolten D, Robinius M. The future of European onshore wind energy potential: detailed distribution and simulation of advanced turbine designs. Energy. 2019;182:1222–38.
Salah MM, Abo-khalil AG, Praveen RP. Wind speed characteristics and energy potential for selected sites in Saudi Arabia. J King Saud Univ Eng Sci. 2019. https://doi.org/10.1016/j.jksues.2019.12.006.
BoroumandJazi G, Rismanchi B, Saidur R. Technical characteristic analysis of wind energy conversion systems for sustainable development. Energy Convers Manag. 2013;69:87–94.
Agence Internationale de l'Energie (AIE). Renewables 2021-analysis and forecasts to 2026; December 2021. https://www.iea.org/reports/renewables-2021.
Sanchez Gomez M, Lundquist JK. The effect of wind direction shear on turbine performance in a wind farm in central Iowa. Wind Energy Sci. 2020;5:125–39. https://doi.org/10.5194/wes-5-125-2020.
Bilal BO, Kebe CMF, Sambou V, Ndongo M, Ndiaye PA. Etude et modelisation du potentiel eolien du site de Nouakchott. J Sci Pour Ingenieur. 2008;9(28): 34.
Agence Nationale pour les Energies Renouvelables (ANER). Le secteur énergétique; May 24 2021. http://aner.sn/le-secteur-energetique/.
Youm I, Sarr J, Sall M, Ndiaye A, Kane MM. Analysis of wind data and wind energy potential along the northern coast of Senegal. Rev Energ Ren. 2005;8:95e108.
Kebe CMF, Sambou V, Bilal BO, Ndiaye PA, Los M. Evaluation du potentiel eolien du site de Gandon dans la region nord du Senegal. In: International metrology conference; 2008. p. 1–6
Bilal BO, Ndiaye PA, Kebe CMF, Ndiaye A. Evaluation du potentiel eolien des sites de Kayaret de Potou: application au choixd’une eolienne adaptee au site. J Sci Pour Ingenieur. 2010;12(33): 41.
Lekela. Taiba Ndiaye wind farm; September 20 2021. https://www.lekela.com/projects/taiba-ndiaye-wind-farm/.
Barthelmie RJ, Pryor SC. The science and engineering of windpower. Cambridge University Press; 2014.
Carvalho D, Miranda V, Falcão D, Estanqueiro A. Wind turbine performance under different wind regimes. Renew Energy. 2011;36(2):546–56.
Smith J, et al. Variations saisonnières de la production d’énergie éolienne à Taïba Ndiaye. Revue de l’énergie éolienne. 2023;45(2):123–35.
Wang H, et al. Analyse de la dynamique temporelle des conditions éoliennes pour une meilleure prévision de la production d’énergie éolienne. Journal de la météorologie et de la climatologie appliquées. 2023;28(4):567–82.
García-García E, et al. Analysis of wind energy time series data for energy production forecasting: a review. Energies. 2022;15(12):7869.
Abubakar AI, et al. Seasonal variations in wind speed and its impact on wind power generation in tropical regions. Energies. 2023;16(2):837.
Dos Santos AR, et al. Wind power temporal dynamics: an overview of recent advances. Renew Energy. 2022;189:437–48.
Ahmed S, et al. Influence of wind speed temporal dynamics on the performance of wind farms: a case study of a tropical region. Renew Energy. 2023;194:173–85.
Diallo T, Ndiaye A, Sow MS, Mbodj S, Sylla MF. Wind energy potential assessment of Taiba Ndiaye in Senegal. Int J Renew Energy Res. 2020;10(1):387–96.
Diao ADK, Gningue B, Sambou A, Sambou S, Cisse A. Wind energy potential assessment and optimal wind turbine selection in Taiba Ndiaye (Senegal). Energy Sources Part A Recov Utiliz Environ Effects. 2021;43(16):1972–85.
Diagne N, Thiam M, Ndiaye A, Mbodj S. Evaluation of the performance of the Taiba Ndiaye wind farm in Senegal. Energy Rep. 2021;7:677–86.
Agence Sénégalaise d'Électrification Rurale (ASER). ASER met en service la première centrale éolienne du Sénégal à Taïba Ndiaye. Récupéré sur; February 24 2020. https://aser.sn/aser-met-en-service-la-premiere-centrale-eolienne-du-senegal-a-taiba-ndiaye/.
African Development Bank Group. Senegal: Taiba N'Diaye wind power project [Press release]. Récupéré de; Ocober 7 2020. https://www.afdb.org/fr/news-and-events/senegal-taiba-ndiaye-wind-power-project.
Hersbach H, Bell B, Berrisford P, Biavati G, Horányi A, Muñoz Sabater J, Dee D. The ERA5 global reanalysis. Q J R Meteorol Soc. 2020;146(730):1999–2049.
Ghedira H, Abida R, Ouarda TBMJ, Bélair S. Validation of the ERA5 wind data using WINDCUBE v2 Lidar and wind mast measurements in a forested area. Renew Energy. 2019;141:197–212.
Hersbach H, Bell B, Berrisford P, Biavati G, Horányi A, Sabater JM, Nicolas J, Peubey C, Radu R, Rozum I et coll. Donnees horaires ERA5 sur des niveaux uniques de 1979 à aujourd'hui. Copernicus Climate Change Service (C3S) Climate Data Store (CDS); 2018. https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?taboverview.
Copernicus Climate Change Service. ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate, Copernicus Climate Change Service Climate Data Store (CDS). Copernicus Climate Change Service; 2017.
Oliver G, Kachele F, Watermeyer M. Analyse des plus grands parcs éoliens offshore d’Europe: un ensemble de donnees avec 40 ans de vitesses de vent horaires et de production d’electricite. Energies. 2022;15(5):1700. https://doi.org/10.3390/en15051700.
Mialiniaina Michèle Rasoloarimanana. Evaluation de l'impact de l'occupation du sol sur la simulation climatique régionale (WRF) des ressources en énergie solaire et éolienne : cas de l’île de La Réunion et de Maurice. Etudes de l'environnement, ⟨dumas-03829912⟩; 2022.
Hountondji YC, Agbossou K, Akpoto A, Kindomihou V. Assessment of the wind energy potential in Benin (West Africa) using Weibull distribution parameters. Renew Energy. 2018;122:116–29.
Yaniktepe B, Koroglu T, Et Savrun MM. Investigation of wind characteristics and wind energy potential in Osmaniye, Turkey. Renew Sust Energy Rev 2013:21:703–11. https://doi.org/10.1016/j.rser.2013.01.00
Hinkelman L, Usher W, Hugman R, Campbell J. Wind resource assessment and mapping for West Africa. Wind Energy. 2016;19(7):1371–88.
IRENA. Étude sur le potentiel de l’énergie éolienne au Sahel. IRENA; 2019.
Abdoulaye I, Mahamat AB, Abdourahamane T. Evaluation of wind energy potential in the Sahel region using 33 years of satellite observations. Renew Energy. 2017;105:370–8. https://doi.org/10.1016/j.renene.2016.12.068.
Awad H, Kaldellis JK, El-Zafarany A. Wind energy assessment in the Sahel region: a case study for Mali. Renew Energy. 2019;135:1387–402. https://doi.org/10.1016/j.renene.2018.12.091.
Bouchahm R, Benyoucef B. Wind energy potential in the Sahel: a case study for the region of Ouargla in Algeria. Energy Procedia. 2017;119:219–26. https://doi.org/10.1016/j.egypro.2017.07.143.
Oueslati I, Hamouda M. Wind energy potential assessment using WAsP model: a case study of the southern Tunisia (Sahel). Energy Rep. 2019;5:1537–46. https://doi.org/10.1016/j.egyr.2019.08.026.
Tchinda R, Ngatcha B, Njomo D. Estimation of wind power potential in the Sahelian zone of Cameroon. J Energy Southern Afr. 2017;28(2):65–76. https://doi.org/10.17159/2413-3051/2017/v28i2a1971.
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by SAAN, MSD and SON. The first draft of the manuscript was written by SAAN, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Niang, S.A.A., Drame, M.S., Gueye, A. et al. Temporal dynamics of energy production at the Taïba Ndiaye wind farm in Senegal. Discov Energy 3, 6 (2023). https://doi.org/10.1007/s43937-023-00018-0
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DOI: https://doi.org/10.1007/s43937-023-00018-0