Wind Turbines Site Selection and Techno-Economic Analysis of Renewable Energy Integrated with Grid Stability Using Statistical Models
DOI:
https://doi.org/10.33003/fjs-2026-1009-5150Keywords:
Wind Turbines, Farm Site Selection, Statistical Models, Wind Speed, Power Curve ModelAbstract
References
A.H. Mamaghani, S. A. A. Escandon, B. Najafi, A. Shirazi, and F. Rinaldi, "Techno-economic feasibility of photovoltaic, wind, diesel and hybrid electrification systems for off-grid rural electrification in Colombia," Renewable Energy, vol. 97, pp. 293-305, 2016.
Al-mayyahi S, Yahya K, Yahya AEM, Sarreb RD, Ald ababsa M. Machine learning techniques for solar power output predicting. Int J Smart Grid. 2024;8(2):98–107. doi: 10.20508/ijsmartgrid. v8i2341.g356
American Wind Energy Association. The Clean Air Benefits of Wind Energy; American Wind Energy Association: Washington, DC, USA, 2014.
Ari, E.S. and Gencer, C. Proposal of a novel mixed integer linear programming model for site selection of a wind power plant based on power maximization with use of mixed type wind turbines. Energy Environ. 2020, 31, 825–841. [CrossRef]
Bennui, A.; Rattanamanee, P.; Puetpaiboon, U.; Phukpattaranont, P.; Chetpattananondh, K. Site selection for large wind turbine using GIS. In Proceedings of the PSU-UNS International Conference on Engineering and Environment, Phuket, Thailand, 10–11 May 2007.
Benti NE, Chaka MD, Semie AG. Forecasting renewable energy generation with machine learning and deep learning: current advances and future prospects. Sustainability. 2023;15(9):7087. doi: 10.3390/su15097087
Choukulkar, A.; Pichugina, Y.; Clack, C.T.; Calhoun, R.; Banta, R.; Brewer, A.; Hardesty, M. A new formulation for rotor equivalent wind speed for wind resource assessment and wind power forecasting. Wind Energy 2016, 19, 1439–1452. [CrossRef]
DeLellis, M.; Reginatto, R.; Saraiva, R.; Trofino, A. The Betz limit applied to airborne wind energy. Renew. Energy 2018, 127, 32–40. [CrossRef]
Duranay, Z.B.; Güldemir, H.; Co¸skun, B. The Role of Wind Turbine Siting in Achieving Sustainable Energy Goals. Processes 2024, 12, 2900. https://doi.org/10.3390/pr12122900
Embber Energy. Why Wind and Solar Are Key Solutions to Combat Climate Change. Available online: https://ember-energy.org/latest-insights/why wind and solar are key solutions to combat climate change/ (accessed on 10 December 2024).
Juma, M.I.; Mwinyiwiwa, B.M.M.; Msigwa, C.J.; Mushi, A.T. Design of a Hybrid Energy System with Energy Storage for Standalone DC Microgrid Application. Energies 2021, 14, 5994. https://doi.org/ 10.3390/en14185994
Kriechbaum L, Scheiber G, Kienberger T. Grid-based multi energy systems modelling, assessment, opensource mod elling frameworks and challenges. Energy Sustain Soc. 2018;8:35. doi: 10.1186/s13705-018-0176-x
Krishnamurthy S, Adewuyi OB, Luwaca E, Ratshitanga M, Moodley P. Artificial intelligence-based forecasting models for integrated energy system management planning: an exploration of the prospects for South Africa. Energy Convers Manag. 2024;24:100772. doi: 10.1016/j.ecmx.2024.100772
Kuczynski, W.; Wolniewicz, K.; Charun, H. Analysis of the wind turbine selection for the given wind conditions. Energies 2021, 14, 7740. [CrossRef]
Mayer MJ, Biró B, Szücs B, Aszódi A. Probabilistic modeling of future electricity systems with high renewable energy penetration using machine learning. Appl Energy. 2023;336:120801. doi: 10.1016/j.apenergy.2023.120801
Mitra J, Vallem MR, Singh C. Optimal deployment of dis tributed generation using a reliability criterion IEEE Trans Ind Appl. 2016;52(3):1989–97. doi: 10.1109/TIA.2016.25 17067
Nasser Yimen, Theodore Tchotang, Abraham Kanmogne, Idriss Abdelkhalikh Idriss, Bashir Musa, Aliyu Aliyu, Eric C. Okonkwo, Sani Isah Abba, Lucien Meva’a, Oumarou Hamandjoda, and Mustafa Dagbasi. “Optimal Sizing and Techno-Economic Analysis of Hybrid Renewable Energy Systems—A Case Study of a Photovoltaic/Wind/Battery/Diesel System in Fanisau, Northern Nigeria” Processes 2020, 8, 1381
Olabi, A.G.; Obaideen, K.; Abdelkareem, M.A.; AlMallahi, M.N.; Shehata, N.; Alami, A.H.; Sayed, E.T. Wind energy contribution to the sustainable development goals: Case study on London array. Sustainability 2023, 15, 4641. [CrossRef]
Owusu-Ansah EDJ, Avuglah RK, Harris E, Kyere AY, Amankwaa BD. Optimizing renewable energy integration: statistical models for grid stability and economic viability. Academia Green Energy 2025;2. https://doi.org/10.20935/AcadEnergy7430
Ren, G.; Liu, J.; Wan, J.; Li, F.; Guo, Y.; Yu, D. The analysis of turbulence intensity based on wind speed data in onshore wind farms. Renew. Energy 2018, 123, 756–766. [CrossRef]
Sani Salisu, Mohd Wazir Mustafa, Olatunji Obalowu Mohammed, Mamunu Mustapha, Touqeer Ahmed Jumani. “Techno-Economic Feasibility Analysis of an Off-grid Hybrid Energy System for Rural Electrification in Nigeria” International Journal of Renewable Energy Research Vol.9, No.1, March, 2019.
Sani Salisu, Mohd Wazir Mustafa, Olatunji Obalowu Mohammed, Mamunu Mustapha, and Touqeer Ahmed Jumani (2019). “Techno-Economic Feasibility Analysis of an Offgrid Hybrid Energy System for Rural Electrification in Nigeria” International Journal of Renewable Energy Research, vol.9, no.1, march, 2019
Sultan, A.Y.; Charabi, Y.; Gastli, A.; Al-Alawi, S. Assessment of wind energy potential locations in Oman using data from existing weather stations. Renew. Sustain. Energy Rev. 2010, 14, 1428–1436.
Talari S, Shafie-khah M, Osório GJ, Aghaei J, Catalão JPS. Stochastic modelling of renewable energy sources from operators’ point-of-view: a survey. Renew Sustain Energy Rev. 2018;81:1953–65. doi: 10.1016/j.rser.2017.06.006
The Excel 10 kW Wind Power. Available online: Bergey.com/products/wind-turbines/10kw-bergey-excel (accessed on 4 September 2020).
Wind Turbine Models. Available online: https://en.wind-turbine-models.com/turbines/1270-nps-northern-power-nps-60-24 (accessed on 15 October 2024).
Wu, X.; Zhang, C.; Jiang, L.; Liao, H. An integrated method with PROMETHEE and conflict analysis for qualitative and quantitative decision-making: Case study of site selection for wind power plants. Cogn. Computing. 2020, 12, 100–114. [CrossRef]
Zhang Y, Wang J, Wang X, Zhao D. Review on probabilistic forecasting of wind power generation. Renew Sustain Energy Rev. 2014;32:255–70. doi: 10.1016/j.rser.2014.01.032
Zhao Y, Zeng S, Ding Y, Ma L, Wang Z, Liang A, Ren H. Cost-benefit analysis of distributed energy systems considering the monetization of indirect benefits. Sustainability. 2024;16(2):820. doi: 10.3390/su16020820
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