IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v115y2018icp448-461.html
   My bibliography  Save this article

Statistical analysis of wind energy characteristics in Santiago island, Cape Verde

Author

Listed:
  • Qing, Xiangyun

Abstract

As a volcanic archipelago, the Republic of Cape Verde relies dominantly on diesel to power its electricity supply. Recognizing the financial and environmental burden of diesel generation and risk of energy security, the government of Cape Verde has launched an ambitious goal of 50% electricity from renewables by 2020, since the country is endowed with high potential of renewable energy resources such as wind and solar. Although the annual average penetration rate of wind power has reached 24% of total electricity production generated in Cape Verde, raising the wind energy penetration level in future will pose numerous challenges for the operation and control of the power system because of wind's inherent intermittency and unpredictability. In this study, a statistical analysis of the wind characteristics in Santiago island, is presented by using historical wind speed and power data of the Santiago wind farm in 2014. A two-parameter Weibull distribution is first applied to model the wind speeds on various timescales and to determine wind energy potential in Santiago island, Cape Verde. The annual average wind speed was 8.57 m/s with a standard deviation close to 3.29 m/s. The monthly Weibull scale parameters varied from 5.64 m/s to 13.7 m/s, while the monthly Weibull shape parameters varied from 1.97 to 9.13. Although the monthly mean power density of the rainy season from August to September was low, the annual mean power density shows that Santiago has good wind potential. Then, an approach to modeling the equivalent power curve based on available wind speed and power output data from the wind farm is proposed. By utilizing the estimated power curve, the uncertainty set of wind power generation, resulted from the uncertainty of wind speed forecast, can be obtained to quantify the power system reserve requirements. A statistical analysis of wind power ramp is also given for estimating the power capacity requirement of the energy storage system that can be considered as a reasonable way to mitigate the wind intermittency and minimize curtailment of wind. Results of this study contribute to assess the wind energy potential of Cape Verde for investors, and can be used to quantify the uncertainties of wind power generation for the power system operator.

Suggested Citation

  • Qing, Xiangyun, 2018. "Statistical analysis of wind energy characteristics in Santiago island, Cape Verde," Renewable Energy, Elsevier, vol. 115(C), pages 448-461.
  • Handle: RePEc:eee:renene:v:115:y:2018:i:c:p:448-461
    DOI: 10.1016/j.renene.2017.08.077
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148117308406
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2017.08.077?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Segurado, Raquel & Krajacic, Goran & Duic, Neven & Alves, Luís, 2011. "Increasing the penetration of renewable energy resources in S. Vicente, Cape Verde," Applied Energy, Elsevier, vol. 88(2), pages 466-472, February.
    2. Mathew, Sathyajith & Pandey, K.P. & Kumar.V, Anil, 2002. "Analysis of wind regimes for energy estimation," Renewable Energy, Elsevier, vol. 25(3), pages 381-399.
    3. Jamil, M. & Parsa, S. & Majidi, M., 1995. "Wind power statistics and an evaluation of wind energy density," Renewable Energy, Elsevier, vol. 6(5), pages 623-628.
    4. Salci, Sener & Jenkins, Glenn, 2016. "An Economic and Stakeholder Analysis for the Design of IPP Contracts for Wind Farms," MPRA Paper 70578, University Library of Munich, Germany.
    5. Salci, Sener & Jenkins, Glenn, 2016. "An Economic and Stakeholder Analysis for the Design of IPP Contracts for Wind Farms," MPRA Paper 70578, University Library of Munich, Germany.
    6. Ilinca, Adrian & McCarthy, Ed & Chaumel, Jean-Louis & Rétiveau, Jean-Louis, 2003. "Wind potential assessment of Quebec Province," Renewable Energy, Elsevier, vol. 28(12), pages 1881-1897.
    7. Carta, J.A. & Ramírez, P., 2007. "Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions," Renewable Energy, Elsevier, vol. 32(3), pages 518-531.
    8. Ranaboldo, Matteo & Lega, Bruno Domenech & Ferrenbach, David Vilar & Ferrer-Martí, Laia & Moreno, Rafael Pastor & García-Villoria, Alberto, 2014. "Renewable energy projects to electrify rural communities in Cape Verde," Applied Energy, Elsevier, vol. 118(C), pages 280-291.
    9. Monteiro Alves, Luis M & Lopes Costa, Anildo & da Graça Carvalho, Maria, 2000. "Analysis of potential for market penetration of renewable energy technologies in peripheral islands," Renewable Energy, Elsevier, vol. 19(1), pages 311-317.
    10. Jaramillo, O.A. & Borja, M.A., 2004. "Wind speed analysis in La Ventosa, Mexico: a bimodal probability distribution case," Renewable Energy, Elsevier, vol. 29(10), pages 1613-1630.
    11. Duic, N. & Alves, L. M. & Chen, F. & da Graça Carvalho, M., 2003. "Potential of Kyoto Protocol Clean Development Mechanism in transfer of clean energy technologies to Small Island Developing States: case study of Cape Verde," Renewable and Sustainable Energy Reviews, Elsevier, vol. 7(1), pages 83-98, February.
    12. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Akpan, Anthony E. & Ben, Ubong C. & Ekwok, Stephen E. & Okolie, Chukwuma J. & Epuh, Emeka E. & Julzarika, Atriyon & Othman, Abdullah & Eldosouky, Ahmed M., 2024. "Technical and performance assessments of wind turbines in low wind speed areas using numerical, metaheuristic and remote sensing procedures," Applied Energy, Elsevier, vol. 357(C).
    2. Wang, Jianzhou & Huang, Xiaojia & Li, Qiwei & Ma, Xuejiao, 2018. "Comparison of seven methods for determining the optimal statistical distribution parameters: A case study of wind energy assessment in the large-scale wind farms of China," Energy, Elsevier, vol. 164(C), pages 432-448.
    3. Siyavash Filom & Soheil Radfar & Roozbeh Panahi & Erfan Amini & Mehdi Neshat, 2021. "Exploring Wind Energy Potential as a Driver of Sustainable Development in the Southern Coasts of Iran: The Importance of Wind Speed Statistical Distribution Model," Sustainability, MDPI, vol. 13(14), pages 1-24, July.
    4. Tian, Qun & Huang, Gang & Hu, Kaiming & Niyogi, Dev, 2019. "Observed and global climate model based changes in wind power potential over the Northern Hemisphere during 1979–2016," Energy, Elsevier, vol. 167(C), pages 1224-1235.
    5. Ciupăgeanu, Dana-Alexandra & Lăzăroiu, Gheorghe & Barelli, Linda, 2019. "Wind energy integration: Variability analysis and power system impact assessment," Energy, Elsevier, vol. 185(C), pages 1183-1196.
    6. Marania Hopuare & Tao Manni & Victoire Laurent & Keitapu Maamaatuaiahutapu, 2022. "Investigating Wind Energy Potential in Tahiti, French Polynesia," Energies, MDPI, vol. 15(6), pages 1-13, March.
    7. Ciulla, G. & D’Amico, A. & Di Dio, V. & Lo Brano, V., 2019. "Modelling and analysis of real-world wind turbine power curves: Assessing deviations from nominal curve by neural networks," Renewable Energy, Elsevier, vol. 140(C), pages 477-492.
    8. Razmi, Amir Reza & Soltani, M. & Ardehali, Armin & Gharali, Kobra & Dusseault, M.B. & Nathwani, Jatin, 2021. "Design, thermodynamic, and wind assessments of a compressed air energy storage (CAES) integrated with two adjacent wind farms: A case study at Abhar and Kahak sites, Iran," Energy, Elsevier, vol. 221(C).
    9. Bilal, Boudy & Adjallah, Kondo Hloindo & Yetilmezsoy, Kaan & Bahramian, Majid & Kıyan, Emel, 2021. "Determination of wind potential characteristics and techno-economic feasibility analysis of wind turbines for Northwest Africa," Energy, Elsevier, vol. 218(C).
    10. Surroop, Dinesh & Raghoo, Pravesh & Bundhoo, Zumar M.A., 2018. "Comparison of energy systems in Small Island Developing States," Utilities Policy, Elsevier, vol. 54(C), pages 46-54.
    11. Wang, Yingli & Duan, Jialong & Zhao, Yuanyuan & He, Benlin & Tang, Qunwei, 2018. "Harvest rain energy by polyaniline-graphene composite films," Renewable Energy, Elsevier, vol. 125(C), pages 995-1002.
    12. Qing, Xiangyun & Niu, Yugang, 2018. "Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM," Energy, Elsevier, vol. 148(C), pages 461-468.
    13. Kwami Senam A. Sedzro & Adekunlé Akim Salami & Pierre Akuété Agbessi & Mawugno Koffi Kodjo, 2022. "Comparative Study of Wind Energy Potential Estimation Methods for Wind Sites in Togo and Benin (West Sub-Saharan Africa)," Energies, MDPI, vol. 15(22), pages 1-28, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Calif, Rudy & Emilion, Richard & Soubdhan, Ted, 2011. "Classification of wind speed distributions using a mixture of Dirichlet distributions," Renewable Energy, Elsevier, vol. 36(11), pages 3091-3097.
    2. Liu, Feng-Jiao & Chen, Pai-Hsun & Kuo, Shyi-Shiun & Su, De-Chuan & Chang, Tian-Pau & Yu, Yu-Hua & Lin, Tsung-Chi, 2011. "Wind characterization analysis incorporating genetic algorithm: A case study in Taiwan Strait," Energy, Elsevier, vol. 36(5), pages 2611-2619.
    3. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
    4. Chang, Tian Pau, 2011. "Estimation of wind energy potential using different probability density functions," Applied Energy, Elsevier, vol. 88(5), pages 1848-1856, May.
    5. Simon Watson, 2014. "Quantifying the variability of wind energy," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 3(4), pages 330-342, July.
    6. Ahmed, Ahmed Shata, 2011. "Investigation of wind characteristics and wind energy potential at Ras Ghareb, Egypt," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 2750-2755, August.
    7. Shin, Ju-Young & Ouarda, Taha B.M.J. & Lee, Taesam, 2016. "Heterogeneous mixture distributions for modeling wind speed, application to the UAE," Renewable Energy, Elsevier, vol. 91(C), pages 40-52.
    8. Muhammad Fitra Zambak & Catra Indra Cahyadi & Jufri Helmi & Tengku Machdhalie Sofie & Suwarno Suwarno, 2023. "Evaluation and Analysis of Wind Speed with the Weibull and Rayleigh Distribution Models for Energy Potential Using Three Models," International Journal of Energy Economics and Policy, Econjournals, vol. 13(2), pages 427-432, March.
    9. Wais, Piotr, 2017. "Two and three-parameter Weibull distribution in available wind power analysis," Renewable Energy, Elsevier, vol. 103(C), pages 15-29.
    10. Liu, Feng Jiao & Chang, Tian Pau, 2011. "Validity analysis of maximum entropy distribution based on different moment constraints for wind energy assessment," Energy, Elsevier, vol. 36(3), pages 1820-1826.
    11. Arslan, Talha & Bulut, Y. Murat & Altın Yavuz, Arzu, 2014. "Comparative study of numerical methods for determining Weibull parameters for wind energy potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 820-825.
    12. Emilio Gómez-Lázaro & María C. Bueso & Mathieu Kessler & Sergio Martín-Martínez & Jie Zhang & Bri-Mathias Hodge & Angel Molina-García, 2016. "Probability Density Function Characterization for Aggregated Large-Scale Wind Power Based on Weibull Mixtures," Energies, MDPI, vol. 9(2), pages 1-15, February.
    13. Hu, Qinghua & Wang, Yun & Xie, Zongxia & Zhu, Pengfei & Yu, Daren, 2016. "On estimating uncertainty of wind energy with mixture of distributions," Energy, Elsevier, vol. 112(C), pages 935-962.
    14. Celik, Ali N. & Kolhe, Mohan, 2013. "Generalized feed-forward based method for wind energy prediction," Applied Energy, Elsevier, vol. 101(C), pages 582-588.
    15. Jin, Jingliang & Zhou, Dequn & Zhou, Peng & Miao, Zhuang, 2014. "Environmental/economic power dispatch with wind power," Renewable Energy, Elsevier, vol. 71(C), pages 234-242.
    16. Wais, Piotr, 2017. "A review of Weibull functions in wind sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1099-1107.
    17. Trotter, Philipp A. & McManus, Marcelle C. & Maconachie, Roy, 2017. "Electricity planning and implementation in sub-Saharan Africa: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1189-1209.
    18. Carta, José A. & Velázquez, Sergio & Cabrera, Pedro, 2013. "A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 362-400.
    19. Jung, Christopher & Schindler, Dirk, 2019. "Wind speed distribution selection – A review of recent development and progress," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    20. Sergei Kolesnik & Yossi Rabinovitz & Michael Byalsky & Asher Yahalom & Alon Kuperman, 2023. "Assessment of Wind Speed Statistics in Samaria Region and Potential Energy Production," Energies, MDPI, vol. 16(9), pages 1-35, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:115:y:2018:i:c:p:448-461. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.