Precision Computational Modeling of Wind Flow Dynamics to Optimize Wind Turbine Deployment in Nigeria's Varied Geographical Terrains

Hyginus Chidiebere Onyekachi Unegbu, Danjuma S. YAWAS

Abstract


This study evaluates the wind energy potential in Nigeria by analyzing optimal turbine placement across three distinct regions: northern highlands, coastal areas, and savannah regions. Using a hybrid framework combining computational fluid dynamics (CFD), geographic information systems (GIS), and advanced optimization algorithms, wind flow dynamics, energy yields, and turbine configurations were assessed with high precision. The results identify the northern highlands as the most viable region for large-scale wind energy deployment, characterized by an average wind speed of 7.2 m/s, low turbulence intensity, and minimal wake losses. Optimized layouts in this region achieved an energy yield of 3,600 MWh/year per turbine with a capacity factor of 42%. Coastal areas and savannah regions demonstrated moderate wind energy potential, producing 2,200 MWh/year and 1,900 MWh/year per turbine, respectively, but exhibited higher turbulence and wake-induced losses due to their geographical constraints. This research highlights the critical need for tailored deployment strategies, robust grid infrastructure, and policy support to accelerate wind energy development in Nigeria. The findings also underscore the importance of integrating wind energy with complementary renewable resources, such as solar, to enhance reliability and optimize energy generation. By addressing regional variability in wind resources, this study provides actionable insights for maximizing Nigeria’s wind energy potential, contributing to its renewable energy goals and advancing sustainable development.


Keywords


Wind energy, Nigeria, CFD modeling, Turbine optimization, Renewable energy planning

Full Text:

PDF

References


Blanco, M. I. (2009). The economics of wind energy. Renewable and Sustainable Energy Reviews, 13(6–7), 1372–1382. https://doi.org/10.1016/j.rser.2008.09.004

Çetinay, H., Kuipers, F. A., & Guven, A. N. (2017). Optimal siting and sizing of wind farms. Renewable Energy, 101, 51–58. https://doi.org/10.1016/j.renene.2016.08.008

Adewuyi, A. (2020). Challenges and prospects of renewable energy in Nigeria: A case of bioethanol and biodiesel production. Energy Reports, 6(Supplement 4), 77–88. https://doi.org/10.1016/j.egyr.2019.12.002

Dai, J., Tan, Y., & Shen, X. (2019). Investigation of energy output in mountain wind farm using multiple-units SCADA data. Applied Energy, 239, 225–238. https://doi.org/10.1016/j.apenergy.2019.01.207

Odubiyi, A., & Davidson, I. (2003). Distributed generation in Nigeria's new energy industry. Power Engineer, 17(5), 18–20. https://doi.org/10.1049/pe:20030505

Olomiyesan, B., Oyedum, O. D., Ugwuoke, P. E., & Abolarin, M. (2017). Assessment of wind energy resources in Nigeria – A case study of north-western region of Nigeria. International Journal of Physical Research, 5(2), 83. https://doi.org/10.14419/ijpr.v5i2.8327

Blocken, B., van der Hout, A., Dekker, J., & Weiler, O. (2015). CFD simulation of wind flow over natural complex terrain: Case study with validation by field measurements for Ria de Ferrol, Galicia, Spain. Journal of Wind Engineering and Industrial Aerodynamics, 147, 43–57. https://doi.org/10.1016/j.jweia.2015.09.007

Uchida, T., & Ohya, Y. (2003). Large-eddy simulation of turbulent airflow over complex terrain. Journal of Wind Engineering and Industrial Aerodynamics, 91(1–2), 219–229. https://doi.org/10.1016/S0167-6105(02)00347-1

Elgendi, M., AlMallahi, M., Abdelkhalig, A., & Selim, M. Y. E. (2023). A review of wind turbines in complex terrain. International Journal of Thermofluids, 17, 100289. https://doi.org/10.1016/j.ijft.2023.100289

Dhunny, A. Z., Lollchund, M. R., & Rughooputh, S. D. D. V. (2017). Wind energy evaluation for a highly complex terrain using Computational Fluid Dynamics (CFD). Renewable Energy, 101, 1–9. https://doi.org/10.1016/j.renene.2016.08.032

Szubel, M., Filipowicz, M., Papis-Frączek, K., & Kryś, M. (2023). Computational Fluid Dynamics in Renewable Energy Technologies: Theory, Fundamentals, and Exercises (1st ed.). CRC Press. https://doi.org/10.1201/9781003202226

Wang, Z., Tu, Y., Zhang, K., Han, Z., Cao, Y., & Zhou, D. (2024). An optimization framework for wind farm layout design using CFD-based Kriging model. Ocean Engineering, 293, 116644. https://doi.org/10.1016/j.oceaneng.2023.116644

Sharma, R., Kodamana, H., & Ramteke, M. (2022). Multi-objective dynamic optimization of hybrid renewable energy systems. Chemical Engineering and Processing - Process Intensification, 170, 108663. https://doi.org/10.1016/j.cep.2021.108663

Pookpunt, S., & Ongsakul, W. (2013). Optimal placement of wind turbines within wind farm using binary particle swarm optimization with time-varying acceleration coefficients. Renewable Energy, 55, 266–276. https://doi.org/10.1016/j.renene.2012.12.005

AlShannaq, H., & Aly, A. M. (2024). Review of artificial neural networks for wind turbine fatigue prediction. SDHM Structural Durability and Health Monitoring, 18(6), 707–737. https://doi.org/10.32604/sdhm.2024.054731

Yu, X., & Lu, Y. (2023). Reinforcement learning-based multi-objective differential evolution for wind farm layout optimization. Energy, 284, 129300. https://doi.org/10.1016/j.energy.2023.129300

He, Y., Liu, X.-H., Zhang, H.-L., Zheng, W., Zhao, F.-Y., Schnabel, M. A., & Mei, Y. (2021). Hybrid framework for rapid evaluation of wind environment around buildings through parametric design, CFD simulation, image processing, and machine learning. Sustainable Cities and Society, 73, 103092. https://doi.org/10.1016/j.scs.2021.103092

Global Wind Energy Council. (2023). Global Wind Report 2023. Retrieved from https://gwec.net/globalwindreport2023/

Liu, K., Chen, W., Chen, G., Dai, D., Ai, C., Zhang, X., & Wang, X. (2023). Application and analysis of hydraulic wind power generation technology. Energy Strategy Reviews, 48, 101117. https://doi.org/10.1016/j.esr.2023.101117

Bishoge, O. K., Kombe, G. G., & Mvile, B. (2020). Renewable energy for sustainable development in sub-Saharan African countries: Challenges and way forward. Journal of Renewable and Sustainable Energy, 12(5), 053702. https://doi.org/10.1063/5.0009297

IRENA. (2022). Renewable energy opportunities in Africa. Retrieved from https://www.irena.org/publications/2022/Jan/Renewable-Energy-Market-Analysis-Africa

Zhang, W., Calderon-Sanchez, J., Duque, D., & Souto-Iglesias, A. (2024). Computational Fluid Dynamics (CFD) applications in floating offshore wind turbine (FOWT) dynamics: A review. Applied Ocean Research, 138, 104075. https://doi.org/10.1016/j.apor.2024.104075

Ma, T., & Sun, C. (2024). Large eddy simulation of wind turbulence over non-breaking and breaking waves. Ocean Engineering, 305, 117898. https://doi.org/10.1016/j.oceaneng.2024.117898

López, G., Arboleya, P., Núñez, D., Freire, A., & López, D. (2023). Wind resource assessment and influence of atmospheric stability on wind farm design using Computational Fluid Dynamics in the Andes Mountains, Ecuador. Energy Conversion and Management, 284, 116972. https://doi.org/10.1016/j.enconman.2023.116972

Bangga, G., & Lutz, T. (2021). Aerodynamic modeling of wind turbine loads exposed to turbulent inflow and validation with experimental data. Energy, 223, 120076. https://doi.org/10.1016/j.energy.2021.120076

Elyasichamazkoti, F., & Khajehpoor, A. (2021). Application of machine learning for wind energy from design to energy-water nexus: A survey. Energy Nexus, 2, 100011. https://doi.org/10.1016/j.nexus.2021.100011

Abkar, M., Zehtabiyan-Rezaie, N., & Iosifidis, A. (2023). Reinforcement learning for wind-farm flow control: Current state and future actions. Theoretical and Applied Mechanics Letters, 13(6), 100475. https://doi.org/10.1016/j.taml.2023.100475

Ramli, M. A. M., Bouchekara, H. R. E. H., & Milyani, A. H. (2023). Wind farm layout optimization using a multi-objective electric charged particles optimization and a variable reduction approach. Energy Strategy Reviews, 45, 101016. https://doi.org/10.1016/j.esr.2022.101016

Hu, W., Yang, Q., Yuan, Z., & Yang, F. (2024). Wind farm layout optimization in complex terrain based on CFD and IGA-PSO. Energy, 288, 129745. https://doi.org/10.1016/j.energy.2023.129745

Sun, H., Qiu, C., Lu, L., Gao, X., Chen, J., & Yang, H. (2020). Wind turbine power modelling and optimization using artificial neural network with wind field experimental data. Applied Energy, 280, 115880. https://doi.org/10.1016/j.apenergy.2020.115880

Yang, J. J., Yang, M., Wang, M. X., Du, P. J., & Yu, Y. X. (2020). A deep reinforcement learning method for managing wind farm uncertainties through energy storage system control and external reserve purchasing. International Journal of Electrical Power & Energy Systems, 119, 105928. https://doi.org/10.1016/j.ijepes.2020.105928

Itodo, I. (2014). Obstacles and way forward in promoting renewable energy in Nigeria. Journal of Technology Innovations in Renewable Energy, 3(4), 166–170. https://doi.org/10.6000/1929-6002.2014.03.04.2

Ajayi, O. O. (2009). Assessment of utilization of wind energy resources in Nigeria. Energy Policy, 37(2), 750–753. https://doi.org/10.1016/j.enpol.2008.10.020

Adaramola, M. S., & Oyewola, O. M. (2011). On wind speed pattern and energy potential in Nigeria. Energy Policy, 39(5), 2501–2506. https://doi.org/10.1016/j.enpol.2011.02.016

Adelaja, A. O. (2020). Barriers to national renewable energy policy adoption: Insights from a case study of Nigeria. Energy Strategy Reviews, 30, 100519. https://doi.org/10.1016/j.esr.2020.100519

Abedi, H. (2023). Assessment of flow characteristics over complex terrain covered by the heterogeneous forest at slightly varying mean flow directions: A case study of a Swedish wind farm. Renewable Energy, 202, 537–553. https://doi.org/10.1016/j.renene.2022.11.030

Aliyu, A. S., Dada, J. O., & Adam, I. K. (2015). Current status and future prospects of renewable energy in Nigeria. Renewable and Sustainable Energy Reviews, 48, 336–346. https://doi.org/10.1016/j.rser.2015.03.098

López, G., Arboleya, P., Núñez, D., Freire, A., & López, D. (2023). Wind resource assessment and influence of atmospheric stability on wind farm design using Computational Fluid Dynamics in the Andes Mountains, Ecuador. Energy Conversion and Management, 284, 116972. https://doi.org/10.1016/j.enconman.2023.116972

Hu, J., Song, Z., Tan, Y., & Tan, M. (2024). Optimizing integrated energy systems using a hybrid approach blending grey wolf optimization with local search heuristics. Journal of Energy Storage, 87, 111384. https://doi.org/10.1016/j.est.2024.111384

Malakouti, S. M., Karimi, F., Abdollahi, H., Menhaj, M. B., Suratgar, A. A., & Moradi, M. H. (2024). Advanced techniques for wind energy production forecasting: Leveraging multi-layer Perceptron + Bayesian optimization, ensemble learning, and CNN-LSTM models. Case Studies in Chemical and Environmental Engineering, 10, 100881. https://doi.org/10.1016/j.cscee.2024.100881

Idris, W. O., Ibrahim, M. Z., & Albani, A. (2020). The status of the development of wind energy in Nigeria. Energies, 13(23), 6219. https://doi.org/10.3390/en13236219

Ezugwu, C. N. (2015). Renewable energy resources in Nigeria: Sources, problems, and prospects. Journal of Clean Energy Technologies, 3(1), 68–71. https://doi.org/10.7763/JOCET.2015.V3.171

Okonkwo, C. C., Edoziuno, F. O., Adediran, A. A., Ibitogbe, E. M., Mahamood, R., & Akinlabi, E. T. (2021). Renewable energy in Nigeria: Potentials and challenges. [Journal Title], 56*(3). (Add specific journal details and DOI if available).

Hersbach, H., Bell, B., Berrisford, P., & Hirahara, S. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730). https://doi.org/10.1002/qj.3803

Nigerian Meteorological Agency (NiMet). (2022). Climate and weather reports. Retrieved from https://climateknowledgeportal.worldbank.org/country/nigeria/climate-data-historical

Farr, T. G., Rosen, P., Caro, E., & Crippen, R. (2007). The Shuttle Radar Topography Mission. Reviews of Geophysics, 45. https://doi.org/10.1029/2005RG000183

Ahmad, A., Xiao, X., Mo, H., & Dong, D. (2024). Tuning data preprocessing techniques for improved wind speed prediction. Energy Reports, 11, 287–303. https://doi.org/10.1016/j.egyr.2023.11.056

Mughal, M. O., Lynch, M., Yu, F., McGann, B., Jeanneret, F., & Sutton, J. (2017). Wind modelling, validation, and sensitivity study using Weather Research and Forecasting model in complex terrain. Environmental Modelling & Software, 90, 107–125. https://doi.org/10.1016/j.envsoft.2017.01.009

Ricci, A. (2024). Review of OpenFOAM applications in the computational wind engineering: From wind environment to wind structural engineering. Meccanica. https://doi.org/10.1007/s11012-024-01826-x

Madjidian, D., & Rantzer, A. (2011). A stationary turbine interaction model for control of wind farms. IFAC Proceedings Volumes, 44(1), 4921–4926. https://doi.org/10.3182/20110828-6-IT-1002.00267

Reddy, S. R. (2020). Wind Farm Layout Optimization (WindFLO): An advanced framework for fast wind farm analysis and optimization. Applied Energy, 269, 115090. https://doi.org/10.1016/j.apenergy.2020.115090

Taghinezhad, J., & Sheidaei, S. (2022). Prediction of operating parameters and output power of ducted wind turbine using artificial neural networks. Energy Reports, 8, 3085–3095. https://doi.org/10.1016/j.egyr.2022.02.065

Simpson, J. G., & Loth, E. (2022). Super-rated operational concept for increased wind turbine power with energy storage. Energy Conversion and Management: X, 14, 100194. https://doi.org/10.1016/j.ecmx.2022.100194

Frantál, B., Frolova, M., & Liñán-Chacón, J. (2023). Conceptualizing the patterns of land use conflicts in wind energy development: Towards a typology and implications for practice. Energy Research & Social Science, 95, 102907. https://doi.org/10.1016/j.erss.2022.102907

Adeyeye, K., Ijumba, N., & Colton, J. (2022). A preliminary feasibility study on wind resource and assessment of a novel low-speed wind turbine for application in Africa. Energy Engineering, 119(3), 997–1015. https://doi.org/10.32604/ee.2022.018677

Ohunakin, O. S. (2011). Wind resource evaluation in six selected high altitude locations in Nigeria. Renewable Energy, 36(12), 3273–3281. https://doi.org/10.1016/j.renene.2011.04.026

Argungu, G. M., Bala, E. J., Momoh, M., Musa, M., & Dabai, K. A. (2013). Analysis of wind energy resource potentials and cost of wind power generation in Sokoto, Northern Nigeria. International Journal of Engineering Research and Technology (IJERT), 2(5). https://doi.org/10.17577/IJERTV2IS50376

Mukasa, A. D., Mutambatsere, E., Arvanitis, Y., & Triki, T. (2015). Wind energy in sub-Saharan Africa: Financial and political causes for the sector's under-development. Energy Research & Social Science, 5, 90–104. https://doi.org/10.1016/j.erss.2014.12.019

Hodgkin, A., Deskos, G., & Laizet, S. (2023). On the interaction of a wind turbine wake with a conventionally neutral atmospheric boundary layer. International Journal of Heat and Fluid Flow, 102, 109165. https://doi.org/10.1016/j.ijheatfluidflow.2023.109165

Wang, Z., Tu, Y., Zhang, K., Han, Z., Cao, Y., & Zhou, D. (2024). An optimization framework for wind farm layout design using CFD-based Kriging model. Ocean Engineering, 293, 116644. https://doi.org/10.1016/j.oceaneng.2023.116644

Zhang, W., Calderon-Sanchez, J., Duque, D., & Souto-Iglesias, A. (2024). Computational Fluid Dynamics (CFD) applications in Floating Offshore Wind Turbine (FOWT) dynamics: A review. Applied Ocean Research, 150, 104075. https://doi.org/10.1016/j.apor.2024.104075

Shen, W. Z. (2019). Special Issue on Wind Turbine Aerodynamics. Applied Sciences, 9(9), 1725. https://doi.org/10.3390/app9091725

Wędołowski, K., & Bajer, K. (2012). Application of meteorological data in computational modelling of wind over real terrain topography. In Progress in Turbulence and Wind Energy IV (pp. 175–178). https://doi.org/10.1007/978-3-642-28968-2_37

Ajayi, O. O. (2010). The potential for wind energy in Nigeria. Wind Engineering, 34(3), 303–311. https://doi.org/10.1260/0309-524X.34.3.303

Kamal, T., Precup, R.-E., & Hassan, S. Z. (2019). Advanced Control and Optimization Paradigms for Wind Energy Systems. Springer, Singapore. ISBN: 978-981-13-5995-8. https://doi.org/10.1007/978-981-13-5995-8

Hu, W., Yang, Q., Yuan, Z., & Yang, F. (2024). Wind farm layout optimization in complex terrain based on CFD and IGA-PSO. Energy, 288, 129745. https://doi.org/10.1016/j.energy.2023.129745

Back, Y., Kumar, P., Bach, P. M., Rauch, W., & Kleidorfer, M. (2023). Integrating CFD-GIS modelling to refine urban heat and thermal comfort assessment. Science of The Total Environment, 858(Part 1), 159729. https://doi.org/10.1016/j.scitotenv.2022.159729

EL-Shimy, M., Said, M., & Abdelraheem, M. A. (2017). Improved framework for techno-economical optimization of wind energy production. Sustainable Energy Technologies and Assessments, 23, 57–72. https://doi.org/10.1016/j.seta.2017.09.002

Crossett, C. C., Betts, A. K., Dupigny-Giroux, L.-A. L., & Bomblies, A. (2020). Evaluation of daily precipitation from the ERA5 global reanalysis against GHCN observations in the northeastern United States. Climate, 8(12), 148. https://doi.org/10.3390/cli8120148

Hodgkin, A., Deskos, G., & Laizet, S. (2023). On the interaction of a wind turbine wake with a conventionally neutral atmospheric boundary layer. International Journal of Heat and Fluid Flow, 102, 109165. https://doi.org/10.1016/j.ijheatfluidflow.2023.109165

Summers, D. M., Hanson, T., & Wilson, C. B. (1986). Validation of a computer simulation of wind flow over a building model. Building and Environment, 21(2), 97–111. https://doi.org/10.1016/0360-1323(86)90016-8

Ding, J.-W., Chuang, M.-J., Tseng, J.-S., & Hsieh, I.-Y. L. (2024). Reanalysis and ground station data: Advanced data preprocessing in deep learning for wind power prediction. Applied Energy, 375, 124129. https://doi.org/10.1016/j.apenergy.2024.124129

Szubel, M., Filipowicz, M., & Papis, K. (2023). Computational Fluid Dynamics in Renewable Energy Technologies: Theory, Fundamentals and Exercises. https://doi.org/10.1201/9781003202226

Nielsen, S., Østergaard, P. A., & Sperling, K. (2023). Renewable energy transition, transmission system impacts and regional development – A mismatch between national planning and local development. Energy, 278(Part A), 127925. https://doi.org/10.1016/j.energy.2023.127925

Appiah, M., Ashraf, S., Tiwari, A. K., Gyamfi, B. A., & Onifade, S. T. (2023). Does financialization enhance renewable energy development in Sub-Saharan African countries? Energy Economics, 125, 106898. https://doi.org/10.1016/j.eneco.2023.106898

Ugwu, J., Odo, K. C., Oluka, L. O., & Salami, K. O. (2022). A systematic review on the renewable energy development, policies, and challenges in Nigeria with an international perspective and public opinions. International Journal of Renewable Energy Development, 11(1), 287–308. https://doi.org/10.14710/ijred.2022.40359

Bai, F., Ju, X., Wang, S., & Zhou, W. (2021). Wind farm layout optimization using adaptive evolutionary algorithm with Monte Carlo Tree Search reinforcement learning. Energy Conversion and Management, 252(7), 115047. https://doi.org/10.1016/j.enconman.2021.115047

Maghami, M. R., & Mutambara, A. (2023). Challenges associated with hybrid energy systems: An artificial intelligence solution. Energy Reports, 9, 924–940. https://doi.org/10.1016/j.egyr.2022.11.195




DOI: http://dx.doi.org/10.12962%2Fj25807471.v10i1.23175

Creative Commons License
JMES The International Journal of Mechanical Engineering and Sciences by Lembaga Penelitian dan Pengabdian kepada Masyarakat (LPPM) ITS is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://iptek.its.ac.id/index.php/jmes.