Feasibility Study and Simulation of Wind Energy Integration at PDEU Campus
DOI:
https://doi.org/10.61453/INTIj.20260105Keywords:
Renewable energy integration, Small-scale wind turbines, Wind power density, Sustainable campus energyAbstract
This study presents a comprehensive assessment of wind energy potential at the Pandit Deendayal Energy University (PDEU) campus by integrating wind resource analysis, turbine performance evaluation, and hybrid system simulation. Historical wind speed data at 10 m height were extrapolated to 20 m hub height using power law and logarithmic profiles to account for surface roughness. Estimated wind power densities ranged from 51.6 W/m² (2020) to 65.9 W/m² (2021), with seasonal peaks during summer and monsoon. The Archimedes AWM 1500D turbine, suitable for moderate wind regimes with a cut-in speed of 3 m/s and rated power of 1 kW, was selected. Applying the turbine's power curve to site-specific wind frequency distributions yielded annual average outputs of 32.6-41.2 W, translating to capacity factors of 5-6%. While insufficient to meet full demand, the turbine can provide supplementary power to campus energy needs
References
Astolfi, D., Castellani, F., Lombardi, A., & Terzi, L. (2021). Multivariate SCADA data analysis methods for real world wind turbine power curve monitoring. Energies, 14(4), 1105. https://doi.org/10.3390/en14041105
Bandi, M., & Apt, J. (2016). Variability of the wind turbine power curve. Applied Sciences, 6(9), 262. https://doi.org/10.3390/app6090262
Gottschall, J., & Peinke, J. (2008). How to improve the estimation of power curves for wind turbines. Environmental Research Letters, 3(1), 015005. https://doi.org/10.1088/1748-9326/3/1/015005
Manobel, B., Sehnke, F., Lazzús, J. A., Salfate, I., Felder, M., & Montecinos, S. (2018). Wind turbine power curve modeling based on Gaussian processes and artificial neural networks. Renewable Energy, 125, 1015–1020. https://doi.org/10.1016/j.renene.2018.02.081
Pelletier, F., Masson, C., & Tahan, A. (2016). Wind turbine power curve modelling using artificial neural network. Renewable Energy, 89, 207–214. https://doi.org/10.1016/j.renene.2015.11.065
Shokrzadeh, S., Jafari Jozani, M., & Bibeau, E. (2014). Wind turbine power curve modeling using advanced parametric and nonparametric methods. IEEE Transactions on Sustainable Energy, 5(4), 1262–1269. https://doi.org/10.1109/TSTE.2014.2345059
Teyabeen, A. A., Akkari, F. R., & Jwaid, A. E. (2017). Power curve modelling for wind turbines. In 2017 UKSim-AMSS 19th International Conference on Computer Modelling & Simulation (UKSim) (pp. 179–184). IEEE. https://doi.org/10.1109/UKSim.2017.30
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