| Peer-Reviewed

Operability of Wind Energy Conversion Systems at Aiyetoro Coastal Area, Southwestern Nigeria

Received: 4 May 2022    Accepted: 23 May 2022    Published: 8 June 2022
Views:       Downloads:
Abstract

Nigeria's overdependence on non-renewable sources of energy has undermined economic growth for more than seven decades. Renewable energy generation promises an electric power supply of >60 gW. Although Nigeria has invested in hydropower as a source of electricity, there is a need to diversify into wind energy sources. This study examines the wind energy conversion systems potential at Ayetoro, Ondo state (latitude 6.1077997 °N and longitude 4.7721257 °E) using a year of data (June 2018 - May 2019) collected at 5 minutes interval. The data was collected from the Marine Science and Technology weather station, which used an Atmos 41 to record wind data at 5.5 m altitude. The wind speed data was adjusted to 50 and 90 m and fitted to the 2-factor Weibull distribution function. The wind directional frequency and operability of wind energy conversion systems were also calculated. About 62% of the wind blew from the South of the Atlantic Ocean. At 50 m altitude, the Weibull shape parameter (K) was 2.74, and the scale parameter (C) was 4.59 m/s. The wind power density peaked at 134.8 W/m2. This wind power density can be classified as class 1 on the NREL wind power classification. The operating probability of a wind turbine with a shut-in speed of 3.5 m/s at 50 m altitude was 62%. Therefore, we conclude that the wind energy potential of the Aiyetoro Coast of the Atlantic Ocean is currently operable for small-scale, local applications but not commercializable for state or national energy distribution.

Published in American Journal of Electrical Power and Energy Systems (Volume 11, Issue 3)
DOI 10.11648/j.epes.20221103.11
Page(s) 48-55
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Wind Power Density, Wind Energy Conversion Systems, Weibull Probability Distribution, Renewable Energy, South-Western Nigeria

References
[1] Oyedepo, S. O., Babalola, O. P., Nwanya, S. C., Kilanko, O., Leramo, R. O., Aworinde, A. K., Adekeye, T., Oyebanji, J. A., Abidakun, A. O., & Agberegha, O. L. (2018). Towards a Sustainable Electricity Supply in Nigeria: The Role of Decentralized Renewable Energy System. European Journal of Sustainable Development Research, 2 (4).
[2] Adhekpukoli, E. (2018). The democratization of electricity in Nigeria. The Electricity Journal, 31 (2), 1–6.
[3] Amin, A., Liu, Y., Yu, J., Chandio, A. A., Rasool, S. F., Luo, J., & Zaman, S. (2020). How does energy poverty affect economic development? A panel data analysis of South Asian countries. Environmental Science and Pollution Research, 27 (25), 31623–31635.
[4] Kose, T. (2019). Energy poverty and health: the Turkish case. Energy Sources, Part B: Economics, Planning, and Policy, 14 (5), 201–213.
[5] Iwayemi, A. (2008) Nigeria's dual-energy problems, policy issues, and challenges. Intl. Assoc. Energy. Econs, 53: 17-21.
[6] Olaoye, T., Ajilore, T., Akinluwade, K., Omole, F., and Adetunji, A., "Energy Crisis in Nigeria: Need for Renewable Energy Mix. American Journal of Electrical and Electronic Engineering, vol. 4, no. 1 (2016): 1-8.
[7] Olayinka S. O. (2010) Energy utilization and renewable energy sources in Nigeria, Journal of Engineering and applied sciences 5 vol. 2: 171-177.
[8] Dioha M. O., Dioha I. J., Olugboji O. A. (2016) an Assessment Of Nigeria Wind Energy Potential Based On Technical And Financial Analyses, Journal Of Sustainable Energy Vol. 7, No. 2.
[9] Adedipe O., Abolarin M. S., Mamman R. O (2018) A Review of Onshore and Offshore Wind Energy Potential in Nigeria, IOP Conf. Series: Materials Science and Engineering 413: 012039.
[10] M. H. Soulouknga, S. Y. Doka, N. Revanna, N. Djongyang, T. C. Kofane (2018), Analysis of wind speed data and wind energy potential in Faya-Largeau, Chad, using Weibull distribution, Renewable energy, 121: 1-8.
[11] Wais, P. (2017). A review of Weibull functions in wind sector. Renewable and Sustainable Energy Reviews, 70, 1099–1107.
[12] Allouhi, A., Zamzoum, O., Islam, M., Saidur, R., Kousksou, T., Jamil, A., & Derouich, A. (2017). Evaluation of wind energy potential in Morocco’s coastal regions. Renewable and Sustainable Energy Reviews, 72, 311–324.
[13] Katinas, V., Gecevicius, G., & Marciukaitis, M. (2018). An investigation of wind power density distribution at location with low and high wind speeds using statistical model. Applied Energy, 218, 442–451.
[14] Jung, C., & Schindler, D. (2019). Wind speed distribution selection – A review of recent development and progress. Renewable and Sustainable Energy Reviews, 114, 109290.
[15] Jiang, H., Wang, J., Wu, J., & Geng, W. (2017). Comparison of numerical methods and metaheuristic optimization algorithms for estimating parameters for wind energy potential assessment in low wind regions. Renewable and Sustainable Energy Reviews, 69, 1199–1217.
[16] Aries, N., Boudia, S. M., & Ounis, H. (2018). Deep assessment of wind speed distribution models: A case study of four sites in Algeria. Energy Conversion and Management, 155, 78–90.
[17] Pobočíková, I., Sedliačková, Z., & Michalková, M. (2017). Application of Four Probability Distributions for Wind Speed Modeling. Procedia Engineering, 192, 713–718.
[18] Murthy, K. S. R., & Rahi, O. P. (2018). Wind Power Density Estimation Using Rayleigh Probability Distribution Function. Advances in Intelligent Systems and Computing, 265–275.
[19] Shoaib, M., Siddiqui, I., Amir, Y. M., & Rehman, S. U. (2017). Evaluation of wind power potential in Baburband (Pakistan) using Weibull distribution function. Renewable and Sustainable Energy Reviews, 70, 1343–1351.
[20] Akpinar EK and Akpinar S (2005), A statistical analysis of wind speed data used in installation wind energy conversion systems, Energy conversion and management, 46: 515-532.
[21] Khalid Saeed, M., Salam, A., Rehman, A. U., & Abid Saeed, M. (2019). Comparison of six different methods of Weibull distribution for wind power assessment: A case study for a site in the Northern region of Pakistan. Sustainable Energy Technologies and Assessments, 36, 100541.
[22] Chaurasiya, P. K., Ahmed, S., & Warudkar, V. (2018). Study of different parameters estimation methods of Weibull distribution to determine wind power density using ground based Doppler SODAR instrument. Alexandria Engineering Journal, 57 (4), 2299–2311.
[23] Usta, I., Arik, I., Yenilmez, I., & Kantar, Y. M. (2018). A new estimation approach based on moments for estimating Weibull parameters in wind power applications. Energy Conversion and Management, 164, 570–578.
[24] Guarienti, J. A., Kaufmann Almeida, A., Menegati Neto, A., de Oliveira Ferreira, A. R., Ottonelli, J. P., & Kaufmann De Almeida, I. (2020). Performance analysis of numerical methods for determining Weibull distribution parameters applied to wind speed in Mato Grosso do Sul, Brazil. Sustainable Energy Technologies and Assessments, 42, 100854.
[25] Chang, T. P. (2011) Performance comparison of 6 numerical methods in estimating Weibull parameters for wind energy application. Appl. Energy, 88 (1): 272 – 282.
[26] Tizgui, I., el Guezar, F., Bouzahir, H., & Benaid, B. (2017). Comparison of methods in estimating Weibull parameters for wind energy applications. International Journal of Energy Sector Management, 11 (4), 650–663.
[27] Ayodele, T., Ogunjuyigbe, A., & Amusan, T. (2016). Wind power utilization assessment and economic analysis of wind turbines across fifteen locations in the six geographical zones of Nigeria. Journal of Cleaner Production, 129, 341–349.
[28] Olaofe, Z. (2018). Review of energy systems deployment and development of offshore wind energy resource map at the coastal regions of Africa. Energy, 161, 1096–1114.
[29] Ajayi, O. O., Fagbenle, R. O., Katende, J., Aasa, S. A., & Okeniyi, J. O. (2013). Wind profile characteristics and turbine performance analysis in Kano, north-western Nigeria. International Journal of Energy and Environmental Engineering, 4 (1), 27.
[30] NREL (2007) wind power classification. https://www.nrel.gov/gis/webmaster.html
[31] Majid Sedghi, Siamak K. Hannani, Mehrdad Boroushaki (2015) Estimation of Weibull parameters for wind energy application in Iran's cities, wind, and structures, vol. 21, No. 2: 203-221.
[32] Y. N. Udoakah and U. S. Ikafia (2017) Determination Of Weibull Parameters And Analysis Of Wind Power Potential In Coastal And Non-Coastal Sites In Akwa Ibom State, Nigerian Journal of Technology (Nijotech) Vol. 36, No. 3: 923 – 929.
[33] Z. R. Shu, Q. S. Li, P. W. Chan (2015) Investigation of offshore wind energy potential in Hong Kong based on Weibull distribution function, Applied Energy 156: 362–373.
[34] Akinsanola, A. A., Ogunjobi, K. O., Abolude, A. T., Sarris, S. C., & Ladipo, K. O. (2017). Assessment of wind energy potential for small communities in South-South Nigeria: Case study of Koluama, Bayelsa State. Journal of Fundamamentals of Renewable Energy and Applications 7 (02).
[35] Udo, N. A., Oluleye, A., & Ishola, K. A. (2017). Investigation of wind power potential over some selected coastal cities in Nigeria. Innovative Energy & Research, 6 (01), 2576-1463.
Cite This Article
  • APA Style

    Adedeji Adebukola Adelodun, Temitope Matthew Olajire. (2022). Operability of Wind Energy Conversion Systems at Aiyetoro Coastal Area, Southwestern Nigeria. American Journal of Electrical Power and Energy Systems, 11(3), 48-55. https://doi.org/10.11648/j.epes.20221103.11

    Copy | Download

    ACS Style

    Adedeji Adebukola Adelodun; Temitope Matthew Olajire. Operability of Wind Energy Conversion Systems at Aiyetoro Coastal Area, Southwestern Nigeria. Am. J. Electr. Power Energy Syst. 2022, 11(3), 48-55. doi: 10.11648/j.epes.20221103.11

    Copy | Download

    AMA Style

    Adedeji Adebukola Adelodun, Temitope Matthew Olajire. Operability of Wind Energy Conversion Systems at Aiyetoro Coastal Area, Southwestern Nigeria. Am J Electr Power Energy Syst. 2022;11(3):48-55. doi: 10.11648/j.epes.20221103.11

    Copy | Download

  • @article{10.11648/j.epes.20221103.11,
      author = {Adedeji Adebukola Adelodun and Temitope Matthew Olajire},
      title = {Operability of Wind Energy Conversion Systems at Aiyetoro Coastal Area, Southwestern Nigeria},
      journal = {American Journal of Electrical Power and Energy Systems},
      volume = {11},
      number = {3},
      pages = {48-55},
      doi = {10.11648/j.epes.20221103.11},
      url = {https://doi.org/10.11648/j.epes.20221103.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20221103.11},
      abstract = {Nigeria's overdependence on non-renewable sources of energy has undermined economic growth for more than seven decades. Renewable energy generation promises an electric power supply of >60 gW. Although Nigeria has invested in hydropower as a source of electricity, there is a need to diversify into wind energy sources. This study examines the wind energy conversion systems potential at Ayetoro, Ondo state (latitude 6.1077997 °N and longitude 4.7721257 °E) using a year of data (June 2018 - May 2019) collected at 5 minutes interval. The data was collected from the Marine Science and Technology weather station, which used an Atmos 41 to record wind data at 5.5 m altitude. The wind speed data was adjusted to 50 and 90 m and fitted to the 2-factor Weibull distribution function. The wind directional frequency and operability of wind energy conversion systems were also calculated. About 62% of the wind blew from the South of the Atlantic Ocean. At 50 m altitude, the Weibull shape parameter (K) was 2.74, and the scale parameter (C) was 4.59 m/s. The wind power density peaked at 134.8 W/m2. This wind power density can be classified as class 1 on the NREL wind power classification. The operating probability of a wind turbine with a shut-in speed of 3.5 m/s at 50 m altitude was 62%. Therefore, we conclude that the wind energy potential of the Aiyetoro Coast of the Atlantic Ocean is currently operable for small-scale, local applications but not commercializable for state or national energy distribution.},
     year = {2022}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Operability of Wind Energy Conversion Systems at Aiyetoro Coastal Area, Southwestern Nigeria
    AU  - Adedeji Adebukola Adelodun
    AU  - Temitope Matthew Olajire
    Y1  - 2022/06/08
    PY  - 2022
    N1  - https://doi.org/10.11648/j.epes.20221103.11
    DO  - 10.11648/j.epes.20221103.11
    T2  - American Journal of Electrical Power and Energy Systems
    JF  - American Journal of Electrical Power and Energy Systems
    JO  - American Journal of Electrical Power and Energy Systems
    SP  - 48
    EP  - 55
    PB  - Science Publishing Group
    SN  - 2326-9200
    UR  - https://doi.org/10.11648/j.epes.20221103.11
    AB  - Nigeria's overdependence on non-renewable sources of energy has undermined economic growth for more than seven decades. Renewable energy generation promises an electric power supply of >60 gW. Although Nigeria has invested in hydropower as a source of electricity, there is a need to diversify into wind energy sources. This study examines the wind energy conversion systems potential at Ayetoro, Ondo state (latitude 6.1077997 °N and longitude 4.7721257 °E) using a year of data (June 2018 - May 2019) collected at 5 minutes interval. The data was collected from the Marine Science and Technology weather station, which used an Atmos 41 to record wind data at 5.5 m altitude. The wind speed data was adjusted to 50 and 90 m and fitted to the 2-factor Weibull distribution function. The wind directional frequency and operability of wind energy conversion systems were also calculated. About 62% of the wind blew from the South of the Atlantic Ocean. At 50 m altitude, the Weibull shape parameter (K) was 2.74, and the scale parameter (C) was 4.59 m/s. The wind power density peaked at 134.8 W/m2. This wind power density can be classified as class 1 on the NREL wind power classification. The operating probability of a wind turbine with a shut-in speed of 3.5 m/s at 50 m altitude was 62%. Therefore, we conclude that the wind energy potential of the Aiyetoro Coast of the Atlantic Ocean is currently operable for small-scale, local applications but not commercializable for state or national energy distribution.
    VL  - 11
    IS  - 3
    ER  - 

    Copy | Download

Author Information
  • Department of Marine Science and Technology, the Federal University of Technology, Akure, Nigeria

  • Department of Marine Science and Technology, the Federal University of Technology, Akure, Nigeria

  • Sections