This is an open access article distributed under the CC BY 4.0
The potential for free, clean, and limitless energy from renewable sources has long been recognized. However, because of a lack of thorough wind and solar maps, expertise, and public understanding of the significance of these resources in the country, Jordan continues to rely on non-renewable sources for its energy needs. The main objective of this study is to analyze the potential of solar and wind energies as renewable resources for power generation. Weibull distribution function with two parameters and the Angstrom-Prescott model, respectively, are used in this study to offer estimates of the wind and solar energy in the coastal city of Jordan, Aqaba during a five-year period. According to the assessment of wind potential, the annual means of the shape and scale parameters at 10 m for the studied station varied between (1.65 to 1.73) and (4.42 to 4.86), respectively. During the dry season, the wind speed was seen to be stronger, while during the wet season, it was seen to be slower. The maximum power density is found to be in September with values of 622.81 W/m2 and 192.74 W/m2 for the elevations 80 m and 10 m, respectively. According to the forecast for solar potential in this area, the city's global solar radiation is promising for the production of solar energy. The maximum global solar radiation is found to be 8.3 KWh/m2 in June. Results also demonstrated that Aqaba city is suitable for wind and solar power generation.
Communication of this research is made possible through monetary assistance by Universiti Tun Hussein Onn Malaysia and the UTHM Publisher’s Office via Publication Fund E15216.
1. ZOU, C., MA, F., PAN, S., LIN, M., ZHANG, G., XIONG, B., YANG, Z. (2022). Earth energy evolution, human development and carbon neutral strategy. Petroleum Exploration and Development, 49(2), 468–488. https://doi.org/10.1016/S1876-3804(22)60040-5.
2. Al-Ghriybah, M., Zulkafli, M. F., Didane, D. H., & Mohd, S. (2020). Performance of Double Blade Savonius Rotor at Low Rotational Speed. Journal of Computational and Theoretical Nanoscience, 17(2), 729–735. https://doi.org/10.1166/jctn.2020.8711.
3. Al-Ghriybah, M., Zulkafli, M. F., & Didane, D. H. (2020). Numerical Investigation of Inner Blade Effects on the Conventional Savonius Rotor with External Overlap. Journal of Sustainable Development of Energy, Water and Environment Systems, 8(3), 561–576. https://doi.org/10.13044/j.sdewes.d7.0292.
4. Trypolska, G., Kurbatova, T., Prokopenko, O., Howaniec, H., & Klapkiv, Y. (2022). Wind and Solar Power Plant End-of-Life Equipment: Prospects for Management in Ukraine. Energies, 15(5), 1662. https://doi.org/10.3390/en15051662
5. Al-Ghriybah, M. (2022). Performance Analysis of a Modified Savonius Rotor Using a Variable Blade Thickness. EVERGREEN Joint Journal of Novel Carbon Resource Sciences & Green Asia Strategy, 9(3).
6. Kou, G., Yüksel, S., & Dinçer, H. (2022). Inventive problem-solving map of innovative carbon emission strategies for solar energy-based transportation investment projects. Applied Energy, 311, 118680. https://doi.org/10.1016/j.apenergy.2022.118680.
7. Li, G., Li, M., Taylor, R., Hao, Y., Besagni, G., & Markides, C. N. (2022). Solar energy utilisation: Current status and roll-out potential. Applied Thermal Engineering, 209, 118285. https://doi.org/10.1016/j.applthermaleng.2022.118285.
8. Zittis, G., Almazroui, M., Alpert, P., Ciais, P., Cramer, W., Dahdal, Y., … Lelieveld, J. (2022). Climate Change and Weather Extremes in the Eastern Mediterranean and Middle East. Reviews of Geophysics, 60(3). https://doi.org/10.1029/2021RG000762.
9. Monna, S., Abdallah, R., Juaidi, A., Albatayneh, A., Zapata-Sierra, A. J., & Manzano-Agugliaro, F. (2022). Potential Electricity Production by Installing Photovoltaic Systems on the Rooftops of Residential Buildings in Jordan: An Approach to Climate Change Mitigation. Energies, 15(2), 496. https://doi.org/10.3390/en15020496.
10. Al-Ghazawi, Z., & Alawneh, R. (2021). Use of artificial neural network for predicting effluent quality parameters and enabling wastewater reuse for climate change resilience – A case from Jordan. Journal of Water Process Engineering, 44, 102423. https://doi.org/10.1016/j.jwpe.2021.102423.
11. Farrar, L. W., Bahaj, A. S., James, P., Anwar, A., & Amdar, N. (2022). Floating solar PV to reduce water evaporation in water stressed regions and powering water pumping: Case study Jordan. Energy Conversion and Management, 260, 115598. https://doi.org/10.1016/j.enconman.2022.115598.
12. Breulmann, M., Khurelbaatar, G., Sanne, M., van Afferden, M., Subah, A., & Müller, R. A. (2022). Integrated Wastewater Management for the Protection of Vulnerable Water Resources in the North of Jordan. Sustainability, 14(6), 3574. https://doi.org/10.3390/su14063574.
13. Khatatbeh, M., Khasawneh, A., Hussein, H., Altahat, O., & Alhalaiqa, F. (2021). Psychological Impact of COVID-19 Pandemic Among the General Population in Jordan. Frontiers in Psychiatry, 12. https://doi.org/10.3389/fpsyt.2021.618993.
14. Alrwashdeh, S. S. (2022). Energy sources assessment in Jordan. Results in Engineering, 13, 100329. https://doi.org/10.1016/j.rineng.2021.100329.
15. IRENA. (2021). Renewable Readiness Assessment: The Hashemite Kingdom of Jordan.
16. Wang, W., & Okaze, T. (2022). Statistical analysis of low-occurrence strong wind speeds at the pedestrian level around a simplified building based on the Weibull distribution. Building and Environment, 209, 108644. https://doi.org/10.1016/j.buildenv.2021.108644.
17. Mithun Mondal, Djamal Hissein Didane, Alhadj Hisseine Issaka Ali, & Bukhari Manshoor. (2022). Wind Energy Assessment as a Source of Power Generation in Bangladesh. Journal of Advanced Research in Applied Sciences and Engineering Technology, 26(3), 16–22. https://doi.org/10.37934/araset.26.3.1622.
18. Abdul Rashid Shoib, Djamal Hissein Didane, Akmal Nizam Mohammed, Kamil Abdullah, & Mas Fawzi Mohd Ali. (2021). Technical Assessment of Wind Energy Potentiality in Malaysia Using Weibull Distribution Function. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 86(1), 1–13. https://doi.org/10.37934/arfmts.86.1.113.
19. Teimourian, H., Abubakar, M., Yildiz, M., & Teimourian, A. (2022). A Comparative Study on Wind Energy Assessment Distribution Models: A Case Study on Weibull Distribution. Energies, 15(15), 5684. https://doi.org/10.3390/en15155684.
20. AlQdah, K. S., Alahmdi, R., Alansari, A., Almoghamisi, A., Abualkhair, M., & Awais, M. (2021). Potential of wind energy in Medina, Saudi Arabia based on Weibull distribution parameters. Wind Engineering, 45(6), 1652–1661. https://doi.org/10.1177/0309524X211027356.
21. Hassane, A. I., Didane, D. H., Tahir, A. M., & Hauglustaine, J.-M. (2018). Wind and Solar Assessment in the Sahelian Zone of Chad. International Journal of Integrated Engineering, 10(8). https://doi.org/10.30880/ijie.2018.10.08.026.
22. Alami, A. H., Tawalbeh, M., Zhang, D., Aokal, K., Elsherbiny, L., Yasser, Z., & Abdelghani, A. (2018). Linear angstrom model applied to weather data collected for the city of Sharjah. In 2018 5th International Conference on Renewable Energy: Generation and Applications (ICREGA) (pp. 150–153). IEEE. https://doi.org/10.1109/ICREGA.2018.8337583.
23. Serban, A., Paraschiv, L. S., & Paraschiv, S. (2020). Assessment of wind energy potential based on Weibull and Rayleigh distribution models. Energy Reports, 6, 250–267. https://doi.org/10.1016/j.egyr.2020.08.048.
24. Al-Mhairat, B., & Al-Quraan, A. (2022). Assessment of Wind Energy Resources in Jordan Using Different Optimization Techniques. Processes, 10(1), 105. https://doi.org/10.3390/pr10010105.
25. Al-Ghriybah, M., Fadhli, Z., Hissein, D., & Mohd, S. (2019). Wind energy assessment for the capital city of Jordan, Amman. Journal of Applied Engineering Science, 17(3), 311–320. https://doi.org/10.5937/jaes17-20241.
26. Mohanad Al-Ghriybah. (2022). Assessment of Wind Energy Potentiality at Ajloun, Jordan Using Weibull Distribution Function. Evergreen, 9(1), 10–16. https://doi.org/10.5109/4774211.
27. Wang, Z., & Liu, W. (2021). Wind energy potential assessment based on wind speed, its direction and power data. Scientific Reports, 11(1), 16879. https://doi.org/10.1038/s41598-021-96376-7.
28. Onay, A. E., Dokur, E., & Kurban, M. (2021). Performance Comparison of New Generation Parameter Estimation Methods for Weibull Distribution to Compute Wind Energy Density. Elektronika Ir Elektrotechnika, 27(5), 41–48. https://doi.org/10.5755/j02.eie.28919.
29. Safari, M. A. M., Masseran, N., & Majid, M. H. A. (2022). Wind energy potential assessment using Weibull distribution with various numerical estimation methods: a case study in Mersing and Port Dickson, Malaysia. Theoretical and Applied Climatology, 148(3–4), 1085–1110. https://doi.org/10.1007/s00704-022-03990-0.
30. Wan, J., Zheng, F., Luan, H., Tian, Y., Li, L., Ma, Z., … Li, Y. (2021). Assessment of wind energy resources in the urat area using optimized weibull distribution. Sustainable Energy Technologies and Assessments, 47, 101351. https://doi.org/10.1016/j.seta.2021.101351.
31. Nwokolo, S. C., Amadi, S. O., Obiwulu, A. U., Ogbulezie, J. C., & Eyibio, E. E. (2022). Prediction of global solar radiation potential for sustainable and cleaner energy generation using improved Angstrom-Prescott and Gumbel probabilistic models. Cleaner Engineering and Technology, 6, 100416. https://doi.org/10.1016/j.clet.2022.100416.
32. Salau, A. O., Shonkora, S. S., & Owoeye, V. A. (2021). Analysis of Solar Energy Potential Using Sunshine Based Model. In 2021 IEEE AFRICON (pp. 1–4). IEEE. https://doi.org/10.1109/AFRICON51333.2021.9570973.
33. Almorox, J., Voyant, C., Bailek, N., Kuriqi, A., & Arnaldo, J. A. (2021). Total solar irradiance’s effect on the performance of empirical models for estimating global solar radiation: An empirical-based review. Energy, 236, 121486. https://doi.org/10.1016/j.energy.2021.121486.
34. Ahamed, M. S., Guo, H., & Tanino, K. (2022). Cloud cover-based models for estimation of global solar radiation: A review and case study. International Journal of Green Energy, 19(2), 175–189. https://doi.org/10.1080/15435075.2021.1941043.