Estimating wind power generation capacity in Zimbabwe using vertical wind profile extrapolation techniques: A case study
DOI:
https://doi.org/10.17159/2413-3051/2021/v32i1a8205Keywords:
renewable energy; wind energy; power estimation, Hellman lawAbstract
Only 40% of Zimbabwe’s population has access to electricity. The greater proportion of the power is generated from thermal stations, with some from hydro and solar energy sources. However, there is little investment in the use of wind for electricity generation except for small installations in the Eastern Highlands, as Zimbabwe generally has wind speeds which are too low to be utilised for electricity generation. This paper presents the use of vertical wind profile extrapolation methods to determine the potential of generating electricity from wind at different hub heights in Zimbabwe, using the Hellman and exponential laws to estimate wind speeds. The estimated wind speeds are used to determine the potential of generating electricity from wind. Mangwe district in Matabeleland South province of Zimbabwe was used as a test site. Online weather datasets were used to estimate the wind speeds. The investigation shows that a 2.5kW wind turbine installation in Mangwe can generate more than 3MWh of energy per annum at hub heights above 40m, which is enough to supply power to a typical Zimbabwean rural village. This result will encourage investment in the use of wind to generate electricity in Zimbabwe.
Highlights- Wind power utilisation is low in Zimbabwe.
- Vertical wind profile is estimated using extrapolation methods.
- Online weather data for soil and water analysis tool was used.
- Electricity can viably be generated from wind in Zimbabwe.
Downloads
References
Ayodele, T. R., Jimoh, A. A., Munda, J. L., and Agee, T. J. 2012. Statistical analysis of wind speed and wind power potential of Port Elizabeth using Weibull parameters, Journal of Energy in Southern Africa, 23(2): 30–38. doi: 10.17159/2413-3051/2012/v23i2a3160.
Bañuelos-Ruedas, F., Ángeles Camacho, C. and Rios-Marcuello, S. 2012. Methodologies used in the extrapolation of wind speed data at different heights and its impact in the wind energy resource assessment in a region. Suvire, G. (Ed.), ISBN: 978-953-307-483-2, InTech. Available from: http://www.intechopen.com/books/wind-farm-technical-regulations-potential-estimation-and-sitingassessment/methodologies-used-in-the-extrapolation-of-wind-speed-data-at-different-heights-and-its-impactin-the-wind-energy-resource-assessment-in-a-region (Accessed 11 March 2019). doi: 10.5772/20669.
Bratton, D. C. and Womeldorf, C. A. 2012. The wind shear exponent: Comparing measured against simulated values and analyzing the phenomena that affect the wind shear. Proceedings of the ASME 2011 5th International Conference on Energy Sustainability (ES2011), 7-10 August 2011, Washingon, DC, USA. 2245–2251. doi: 10.1115/es2011-54823.
Carbon Brief. 2019. Analysis: Global coal power set for record fall in 2019. Available at: https://www.carbonbrief.org/analysis-global-coal-power-set-for-record-fall-in-2019 (Accessed: 29 November 2019).
Delgado, A., Gertig, C., Blesa, A., Loza, A., Hidalgo, C. and Ron, R. . 2016. Evaluation of the variability of wind speed at different heights and its impact on the receiver efficiency of central receiver systems. AIP Conference Proceedings. doi: 10.1063/1.4949063.
Emeis, S. 2014. Current issues in wind energy meteorology, Meteorological Applications, 21(4): 803–819. doi: 10.1002/met.1472.
Firtin, E., Güler, Ö. and Akdaĝ, S. A. 2011. Investigation of wind shear coefficients and their effect on electrical energy generation, Applied Energy, 88(11): 4097–4105. doi: 10.1016/j.apenergy.2011.05.025.
Globalweather. 2019. Global weather data for SWAT. Available at: https://globalweather.tamu.edu/ (Accessed: 18 July 2019).
Hove, T., Madiye, L. and Musademba, D. 2014. Mapping wind power density for Zimbabwe: A suitable weibull-parameter calculation method, Journal of Energy in Southern Africa, 25(4): 37-47.
IRENA. 2015. Multi-criteria Analysis for Planning Renewable Energy (MapRE): Interactive map-Zimbabwe. doi: 10.1016/B978-0-444-62722-3.00004-5.
Jung, C. 2016. High spatial resolution simulation of annual wind energy yield using near-surface wind speed time series, Energies 9(5): 344-350. doi: 10.3390/en9050344.
Khosravi, A., Koury, R. N. N., Machado. L. and Pabon, J. J. G. 2018. Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system, Sustainable Energy Technologies and Assessments 25(January) : 146–160. doi: 10.1016/j.seta.2018.01.001.
Kubik, M. L., Coker, P. J. and Hunt, C. 2011. Using meteorological wind data to estimate turbine generation output: A sensitivity analysis, Proceedings of the World Renewable Energy Congress – Sweden, 8–13 May, 2011,
Linköping, Sweden, 57: 4074–4081. doi: 10.3384/ecp110574074.
Mawire, W. 2017. Wind energy resource assessment to be conducted in Zimbabwe - Green Energy Zim, Green Energy Zimbabwe. Available at: http://greenenergyzim.misa.org/2017/10/27/wind-energy-resource-assessment-conducted-zimbabwe/ (Accessed: 15 April 2019).
Meteoblue (2019) Weather tube pan - meteoblue. Available at: https://www.meteoblue.com/en/weather/forecast/week/tube-pan_botswana_932969 (Accessed: 8 March 2019).
Ministry of New and Renewable Energy of India. 2016. Draft national wind-solar hybrid policy. Available at: http://mnre.gov.in/file-manager/UserFiles/Draft-Wind-Solar-Hybrid-Policy.pdf.
Mungwena, W. 2002. The distribution and potential utilizability of Zimbabwe’s wind energy resource, Renewable Energy, 26(3): 363–377. doi: 10.1016/S0960-1481(01)00141-0.
Okorie, M. E., Inambao, F. and Chiguvare, Z. 2017. Evaluation of Wind Shear Coefficients, Surface Roughness and Energy Yields over Inland Locations in Namibia, Procedia Manufacturing, 7, pp. 630–638. doi: 10.1016/j.promfg.2016.12.094.
Poudyal, R., Loskot, P., Nepal, R., Parajuli, R. and Khadka, S. K. 2019. Mitigating the current energy crisis in Nepal with renewable energy sources, Renewable and Sustainable Energy Review 116(April): doi: 10.1016/j.rser.2019.109388.
Premono, B. S., Tjahjana, D. D. D. P. and Hadi, S. 2017. Wind energy potential assessment to estimate performance of selected wind turbine in northern coastal region of Semarang-Indonesia. Proceedings of the
International Conference on Engineering, Science and Nanotechnology 2016 (ICESNANO 2016), 1788. doi: 10.1063/1.4968279.
REN21. 2019. Renewables 2019 global report. Paris. Available at: https://www.ren21.net/wp-content/uploads/2019/05/gsr_2019_full_report_en.pdf (Accessed: 16 October 2019).
Sigma. 2013. Statistical analysis 3: Paired t-test.
Tazvinga, H., Zhu, B. and Xia, X. 2014. Energy dispatch strategy for a photovoltaic – wind – diesel – battery hybrid power system, Solar Energy, 2068.
United Nations Children’s Fund. 2015. Sustainable Energy for Children in Zimbabwe : Situational analysis of the energy status of institutions that support children in five districts of Zimbabwe. ISBN: 9789280648508.
United Nations. 1998. Kyoto protocol to the United Nations Framework. doi ; 10.1111/1467-9388.00150.
Rehman, S., Shoaib, M., Siddiqui, I., Ahmed, F., Rashid, T. M. and Jilani, S. U. 2015. Effect of wind shear coefficient for the vertical extrapolation of wind speed data and its impact on the viability of wind energy project, Journal of Basic & Applied Sciences, 11: 90–100. Available at: http://lifescienceglobal.com/pms/index.php/jbas/article/viewFile/2723/1595 (Accessed: 11 March 2019).
Werapun, W., Tirawanichakul, Y. and Waewsak, J. 2017. Wind shear coefficients and their effect on energy production, Energy Procedia. 138: 1061–1066. doi: 10.1016/j.egypro.2017.10.111.
Wood, M. 2019. Simple methods for estimating confidence levels , or tentative probabilities , for hypotheses instead of p values. Methodological Innovations. 1-9. Available from https://doi.org/10.1177/2059799119826518 (Accessed 03 March 2019).
Downloads
Published
Issue
Section
License
Copyright (c) 2021 L. Gunda, E. Chikuni, H. Tazvinga, J. Mudare
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
.png){kind=spacer}