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Analysis of Seasonal Wind Energy Potential on Zanzibar Coastal Island

Buruhan Haji Shame, Dominicus D. D. P. Tjahjana, . Ubaidillah, Mohammad Aziz, Manala T. Mbumba

Abstract


The main objectives of this research were to numerically analyze the potential for seasonal wind power (WP), assess wind direction, and select the most effective wind turbine (WT) for installation at the research site. Wind data were collected half-hourly from a branch of the Tanzania Meteorological Authority nearest to the research site. The collected data were analyzed using a double-parameter Weibull distribution (WD) model, where the standard deviation (SD) method was used to fit the WD. The results revealed that the site experienced strong winds within the range of 4.5–7 m/s between the hours of 05:00 - 20:00, with the most likely seasonal wind speed (WS) ranging from 5–7 m/s, while the mean seasonal WS was 9.07–12.14 m/s. The highest possible wind energy density (wED) of 23.3 GWh/m2 at a hub height of 10 m occurred during winter, followed by spring, autumn, and summer, with 6.39, 6.32, and 3.33 GWh/m2, respectively. The annual wED was > 13.52 GWh/m2, with a typical month-to-month energy of 1.13 GWh/m2. Finally, the study concluded that the recommended WT model (POLARIS P62-1000) was the best choice for installation at the study site due to its sustainable WS and WP potential. Based on the findings of this research, which show that the site has sustainable seasonal wind resources, it is suggested that future wind research be carried out to extend the dataset to ensure the long-term seasonal wind pattern at the site.

 

Doi: 10.28991/HIJ-2024-05-02-08

Full Text: PDF


Keywords


Weibull Distribution; Wind Energy; Energy Analyss; Renewable Energy; Zanzibar.

References


Prihantini, N. B., Rakhmayanti, N., Handayani, S., Sjamsuridzal, W., Wardhana, W., & Nasruddin. (2020). Biomass production of indonesian indigenous leptolyngbya strain on NPK fertilizer medium and its potential as a source of biofuel. Evergreen, 7(4), 593–601. doi:10.5109/4150512.

Furutani, Y., Norinaga, K., Kudo, S., Hayashi, J. I., & Watanabe, T. (2017). Current situation and future scope of biomass gasification in Japan. Evergreen, 4(4), 24–29. doi:10.5109/1929681.

Jhalani, A., Agarwal, A., Singh, D., & Sharma, S. (2023). Energy Security Scenarios for India Under Diversified Demand and Supply. Evergreen, 10(4), 2683–2689. doi:10.5109/7160927.

Mirza, F. M., & Kanwal, A. (2017). Energy consumption, carbon emissions and economic growth in Pakistan: Dynamic causality analysis. Renewable and Sustainable Energy Reviews, 72, 1233–1240. doi:10.1016/j.rser.2016.10.081.

Bogner, J., Pipatti, R., Hashimoto, S., Diaz, C., Mareckova, K., Diaz, L., Kjeldsen, P., Monni, S., Faaij, A., Qingxian, G., Tianzhu, Z., Mohammed, A. A., Sutamihardja, R. T. M., & Gregory, R. (2008). Mitigation of global greenhouse gas emissions from waste: Conclusions and strategies from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report. Working Group III (Mitigation). Waste Management and Research, 26(1), 11–32. doi:10.1177/0734242X07088433.

Surendra, K. C., Takara, D., Hashimoto, A. G., & Khanal, S. K. (2014). Biogas as a sustainable energy source for developing countries: Opportunities and challenges. Renewable and Sustainable Energy Reviews, 31, 846–859. doi:10.1016/j.rser.2013.12.015.

Pan, S. Y., Gao, M., Kim, H., Shah, K. J., Pei, S. L., & Chiang, P. C. (2018). Advances and challenges in sustainable tourism toward a green economy. Science of the Total Environment, 635, 452–469. doi:10.1016/j.scitotenv.2018.04.134.

Kuriqi, A., Pinheiro, A. N., Sordo-Ward, A., Bejarano, M. D., & Garrote, L. (2021). Ecological impacts of run-of-river hydropower plants—Current status and future prospects on the brink of energy transition. Renewable and Sustainable Energy Reviews, 142. doi:10.1016/j.rser.2021.110833.

Mukimin, A., & Vistanty, H. (2023). Low carbon development based on microbial fuel cells as electrical generation and wastewater treatment unit. Renewable Energy Focus, 44, 132–138. doi:10.1016/j.ref.2022.12.005.

Alias, N. D., & Go, Y. I. (2023). Decommissioning platforms to offshore solar system: Road to green hydrogen production from seawater. Renewable Energy Focus, 46, 136–155. doi:10.1016/j.ref.2023.05.003.

Kuncoro, A., & Purwanto, W. W. (2020). Analysis of energy-water nexus palm oil biodiesel production in Riau using life cycle assessment and water footprint methods. Evergreen, 7(1), 104–110. doi:10.5109/2740965.

Wang, J., & Dong, K. (2019). What drives environmental degradation? Evidence from 14 Sub-Saharan African countries. Science of the Total Environment, 656, 165–173. doi:10.1016/j.scitotenv.2018.11.354.

Ahlborg, H., & Hammar, L. (2014). Drivers and barriers to rural electrification in Tanzania and Mozambique - grid-extension, off-grid, and renewable energy technologies. Renewable Energy, 61, 117–124. doi:10.1016/j.renene.2012.09.057.

Mofijur, M., Masjuki, H. H., Kalam, M. A., Hazrat, M. A., Liaquat, A. M., Shahabuddin, M., & Varman, M. (2012). Prospects of biodiesel from Jatropha in Malaysia. Renewable and Sustainable Energy Reviews, 16(7), 5007–5020. doi:10.1016/j.rser.2012.05.010.

Soemanto, A., Mohi, E., al Irsyad, M. I., & Gunawan, Y. (2023). The Role of Oil Fuels on the Energy Transition toward Net Zero Emissions in Indonesia: A Policy Review. Evergreen 10(4), 2074–2083. doi:10.5109/7160867.

Bansal, M., Agarwal, A., Pant, M., & Kumar, H. (2021). Challenges and opportunities in energy transformation during COVID-19. Evergreen 8(2), 255–261. doi:10.5109/4480701.

Eberhard, A., Gratwick, K., Morella, E., & Antmann, P. (2017). Independent Power Projects in Sub-Saharan Africa: Investment trends and policy lessons. Energy Policy, 108(August 2016), 390–424. doi:10.1016/j.enpol.2017.05.023.

Ulsrud, K., Winther, T., Palit, D., & Rohracher, H. (2015). Village-level solar power in Africa: Accelerating access to electricity services through a socio-technical design in Kenya. Energy Research and Social Science, 5, 34–44. doi:10.1016/j.erss.2014.12.009.

Ismaiel, A. M. M., Metwalli, S. M., Elhadidi, B. M. N., & Yoshida, S. (2017). Fatigue analysis of an optimized HAWT composite blade. Evergreen, 4(2–3), 1–6. doi:10.5109/1929656.

Franco, A., Shaker, M., Kalubi, D., & Hostettler, S. (2017). A review of sustainable energy access and technologies for healthcare facilities in the Global South. Sustainable Energy Technologies and Assessments, 22, 92–105. doi:10.1016/j.seta.2017.02.022.

Li, C., & Ito, K. (2014). Performance evaluation of wind decontamination system by computational fluid dynamics. Evergreen, 1(2), 12–17. doi:10.5109/1495158.

Takeyeldein, M. M., Lazim, T. M., Ishak, I. S., Nik Mohd, N. A. R., & Ali, E. A. (2020). Wind lens performance investigation at low wind speed. Evergreen, 7(4), 481–488. doi:10.5109/4150467.

Ashwindran, S. N., Azizuddin, A. A., & Oumer, A. N. (2021). Study of √2 conjecture in the construction of drag induced wind turbine blade morphology. Evergreen, 8(3), 574–585. doi:10.5109/4491649.

Ongaki, N. L., Maghanga, C. M., & Kerongo, J. (2021). Evaluation of the Technical Wind Energy Potential of Kisii Region Based on the Weibull and Rayleigh Distribution Models. Journal of Energy, 2021, 1–17. doi:10.1155/2021/6627509.

Topaloǧlu, F., & Pehlivan, H. (2018). Analysis of Wind Data, Calculation of Energy Yield Potential, and Micrositing Application with WAsP. Advances in Meteorology, 2018. doi:10.1155/2018/2716868.

Narula, K. (2019). Energy security and sustainability. Lecture Notes in Energy, 68(1), 3–22. doi:10.1007/978-981-13-1589-3_1.

Keyhani, A., Ghasemi-Varnamkhasti, M., Khanali, M., & Abbaszadeh, R. (2010). An assessment of wind energy potential as a power generation source in the capital of Iran, Tehran. Energy, 35(1), 188–201. doi:10.1016/j.energy.2009.09.009.

Wang, Z., Wang, X., & Liu, W. (2023). Genetic least square estimation approach to wind power curve modelling and wind power prediction. Scientific Reports, 13(1), 1–15. doi:10.1038/s41598-023-36458-w.

Babu, V. V., Preetha Roselyn, J., & Sundaravadivel, P. (2023). Multi-objective genetic algorithm-based energy management system considering optimal utilization of grid and degradation of battery storage in microgrid. Energy Reports, 9, 5992–6005. doi:10.1016/j.egyr.2023.05.067.

Chirwa, D., Goyal, R., & Mulenga, E. (2023). Floating solar photovoltaic (FSPV) potential in Zambia: Case studies on six hydropower power plant reservoirs. Renewable Energy Focus, 44, 344–356. doi:10.1016/j.ref.2023.01.007.

Jowder, F. A. L. (2009). Wind power analysis and site matching of wind turbine generators in Kingdom of Bahrain. Applied Energy, 86(4), 538–545. doi:10.1016/j.apenergy.2008.08.006.

Awad, M. M. (2013). Comments on “assessment of different methods used to estimate Weibull distribution parameters for wind speed in Zafarana wind farm, Suez Gulf, Egypt.” Energy, 58, 714. doi:10.1016/j.energy.2013.06.034.

Celik, A. N. (2003). Energy output estimation for small-scale wind power generators using Weibull-representative wind data. Journal of Wind Engineering and Industrial Aerodynamics, 91(5), 693–707. doi:10.1016/S0167-6105(02)00471-3.

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. doi:10.1016/j.apenergy.2018.02.163.

Katinas, V., Marčiukaitis, M., Gecevičius, G., & Markevičius, A. (2017). Statistical analysis of wind characteristics based on Weibull methods for estimation of power generation in Lithuania. Renewable Energy, 113, 190–201. doi:10.1016/j.renene.2017.05.071.

Sukkiramathi, K., & Seshaiah, C. V. (2020). Analysis of wind power potential by the three-parameter Weibull distribution to install a wind turbine. Energy Exploration and Exploitation, 38(1), 158–174. doi:10.1177/0144598719871628.

Luankaeo, S., & Tirawanichakul, Y. (2017). Assessment of Wind Energy Potential in Prince of Songkla University (South Part of Thailand): Hatyai campus. Energy Procedia, 138, 704–709. doi:10.1016/j.egypro.2017.10.204.

Michael, E., Tjahjana, D. D. D. P., & Prabowo, A. R. (2021). Estimating the potential of wind energy resources using Weibull parameters: A case study of the coastline region of Dar es Salaam, Tanzania. Open Engineering 11(1), 1093–1104. doi:10.1515/eng-2021-0108.

Oyedepo, S. O., Adaramola, M. S., & Paul, S. S. (2012). Analysis of wind speed data and wind energy potential in three selected locations in South-East Nigeria. International Journal of Energy and Environmental Engineering, 3(1), 1–11. doi:10.1186/2251-6832-3-7.

Gagliano, A., Nocera, F., Patania, F., & Capizzi, A. (2013). Assessment of micro-wind turbines performance in the urban environments: An aided methodology through geographical information systems. International Journal of Energy and Environmental Engineering, 4(1), 1–14. doi:10.1186/2251-6832-4-43.

Fyrippis, I., Axaopoulos, P. J., & Panayiotou, G. (2010). Wind energy potential assessment in Naxos Island, Greece. Applied Energy, 87(2), 577–586. doi:10.1016/j.apenergy.2009.05.031.

Pellegrini, L., & Tasciotti, L. (2013). Rural electrification now and then: Comparing contemporary challenges in developing countries to the USA’s experience in retrospect. Forum for Development Studies, 40(1), 153–176. doi:10.1080/08039410.2012.732108.

Aly, A., Jensen, S. S., & Pedersen, A. B. (2017). Solar power potential of Tanzania: Identifying CSP and PV hot spots through a GIS multicriteria decision making analysis. Renewable Energy, 113, 159–175. doi:10.1016/j.renene.2017.05.077.

Trotter, P. A., McManus, M. C., & Maconachie, R. (2017). Electricity planning and implementation in sub-Saharan Africa: A systematic review. Renewable and Sustainable Energy Reviews, 74, 1189–1209. doi:10.1016/j.rser.2017.03.001.

Pishgar-Komleh, S. H., Keyhani, A., & Sefeedpari, P. (2015). Wind speed and power density analysis based on Weibull and Rayleigh distributions (a case study: Firouzkooh county of Iran). Renewable and Sustainable Energy Reviews, 42, 313–322. doi:10.1016/j.rser.2014.10.028.

Werapun, W., Tirawanichakul, Y., & Waewsak, J. (2015). Comparative Study of Five Methods to Estimate Weibull Parameters for Wind Speed on Phangan Island, Thailand. Energy Procedia, 79. doi:10.1016/j.egypro.2015.11.596.

Al-Ghriybah, M. (2022). Assessment of Wind Energy Potentiality at Ajloun, Jordan Using Weibull Distribution Function. Evergreen, 9(1), 10–16. doi:10.5109/4774211.

Patrai, K., & Gupta, S. (2023). Reliability Estimation of a Degradable System using Intuitionistic Fuzzy Weibull Lifetime Distribution. Evergreen, 10(4), 2317–2324. doi:10.5109/7160909.

Ali, S., Lee, S. M., & Jang, C. M. (2018). Statistical analysis of wind characteristics using Weibull and Rayleigh distributions in Deokjeok-do Island – Incheon, South Korea. Renewable Energy, 123, 652–663. doi:10.1016/j.renene.2018.02.087.

Akpinar, E. K., & Akpinar, S. (2005). An assessment on seasonal analysis of wind energy characteristics and wind turbine characteristics. Energy conversion and management, 46(11-12), 1848-1867. doi:10.1016/j.enconman.2004.08.012.

Mahmood, F. H., Resen, A. K., & Khamees, A. B. (2020). Wind characteristic analysis based on Weibull distribution of Al-Salman site, Iraq. Energy Reports, 6(September 2019), 79–87. doi:10.1016/j.egyr.2019.10.021.

Chang, T. P. (2011). Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application. Applied Energy, 88(1), 272–282. doi:10.1016/j.apenergy.2010.06.018.

MERT, İ., & KARAKUŞ, C. (2015). A statistical analysis of wind speed data using Burr, generalized gamma, and Weibull distributions in Antakya, Turkey. Turkish Journal of Electrical Engineering & Computer Sciences, 23(6), 1571–1586. doi:10.3906/elk-1402-66.

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. doi:10.1016/j.seta.2020.100854.

Alanazi, M. A., Aloraini, M., Islam, M., Alyahya, S., & Khan, S. (2023). Wind energy assessment using Weibull distribution with different numerical estimation methods: a case study. Emerging Science Journal, 7(6), 2260-2278. doi:10.28991/ESJ-2023-07-06-024.

Wang, Z., & Liu, W. (2021). Wind energy potential assessment based on wind speed, its direction and power data. Scientific Reports, 11(1), 1–15. doi:10.1038/s41598-021-96376-7.

Chang, T. P. (2011). Estimation of wind energy potential using different probability density functions. Applied Energy, 88(5), 1848–1856. doi:10.1016/j.apenergy.2010.11.010.

Eriksson, S., Bernhoff, H., & Leijon, M. (2008). Evaluation of different turbine concepts for wind power. Renewable and Sustainable Energy Reviews, 12(5), 1419–1434. doi:10.1016/j.rser.2006.05.017.

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), 1–15. doi:10.1186/2251-6832-4-27.

Daoudi, M., Abdelaziz, A. S. M., Mohammed, E., & Elmostapha, E. (2019). Wind speed data and wind energy potential using weibull distribution in zagora, morocco. International Journal of Renewable Energy Development, 8(3), 267–273. doi:10.14710/ijred.8.3.267-273.

Filom, S., Radfar, S., Panahi, R., Amini, E., & Neshat, M. (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 (Switzerland), 13(14), 7702. doi:10.3390/su13147702.


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DOI: 10.28991/HIJ-2024-05-02-08

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Copyright (c) 2024 Buruhan Haji Shame, Dominicus Danardono Dwi Prija Tjahjana, Ubaidillah Ubaidillah, Mohammad Aziz, Manala Tabu Mbumbae