scholarly journals The Application of Small-Scale and Large-Scale Wind Turbines in Kerman, Iran; A Case Study

2021 ◽  
Vol 15 ◽  
pp. 124-131
Author(s):  
Ighball Baniasad Askari ◽  
Lina Baniasad Askari ◽  
Mohammad Mehdi Kaykhah
Keyword(s):  
Wind Energy ◽  
Wind Turbines ◽  
Large Scale ◽  
Small Scale ◽  
Energy Potential ◽  
Wind Speeds ◽  
Wind Energy Potential ◽  
Wind Characteristics ◽  
Technical Assessment

Wind data collected of the three synoptic sites for the period of Jul 2006 to Jun 2008 at the height of 40 m has been used to study the wind characteristics, monthly and annual wind energy potential for three agricultural districts in Kerman ( 30°15/N, 56°58/E ), Iran. Two statistical methods (Meteorological and Weibull) have been applied to determine the wind characteristics. Wind energy density, mean wind speeds and wind speed directions have been investigated. A technical assessment has been done and the electricity generation from five different wind turbines having capacity of (26 kW, 100 kW, 300 kW, 600 kW and 660 kW) has been calculated. The results show that all the locations studied are not suitable for electric wind application in a large-scale.

scholarly journals A Four Dimensional Variational Data Assimilation Framework for Wind Energy Potential Estimation

Atmosphere ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 167 ◽  
Author(s):  
Elias D. Nino-Ruiz ◽  
Juan C. Calabria-Sarmiento ◽  
Luis G. Guzman-Reyes ◽  
Alvin Henao
Keyword(s):  
Data Assimilation ◽  
Wind Energy ◽  
Wind Turbines ◽  
Energy Potential ◽  
Large Variability ◽  
Wind Speeds ◽  
Initial Analysis ◽  
Modified Cholesky Decomposition ◽  
Wind Energy Potential ◽  
Rate Capacity

In this paper, we propose a Four-Dimensional Variational (4D-Var) data assimilation framework for wind energy potential estimation. The framework is defined as follows: we choose a numerical model which can provide forecasts of wind speeds then, an ensemble of model realizations is employed to build control spaces at observation steps via a modified Cholesky decomposition. These control spaces are utilized to estimate initial analysis increments and to avoid the intrinsic use of adjoint models in the 4D-Var context. The initial analysis increments are mapped back onto the model domain from which we obtain an estimate of the initial analysis ensemble. This ensemble is propagated in time to approximate the optimal analysis trajectory. Wind components are post-processed to get wind speeds and to estimate wind energy capacities. A matrix-free analysis step is derived from avoiding the direct inversion of covariance matrices during assimilation cycles. Numerical simulations are employed to illustrate how our proposed framework can be employed in operational scenarios. A catalogue of twelve Wind Turbine Generators (WTGs) is utilized during the experiments. The results reveal that our proposed framework can properly estimate wind energy potential capacities for all wind turbines within reasonable accuracies (in terms of Root-Mean-Square-Error) and even more, these estimations are better than those of traditional 4D-Var ensemble-based methods. Moreover, large variability (variance of standard deviations) of errors are evidenced in forecasts of wind turbines with the largest rate-capacity while homogeneous variability can be seen in wind turbines with the lowest rate-capacity.

The wind energy potential in Kudat Malaysia by considering the levelized cost of energy for combined wind turbine capacities

2020 ◽  
pp. 0958305X2093700
Author(s):  
A Albani ◽  
MZ Ibrahim ◽  
KH Yong ◽  
ZM Yusop ◽  
MA Jusoh ◽  
...  
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Small Scale ◽  
Energy Potential ◽  
Levelized Cost ◽  
Cost Of Energy ◽  
Wind Energy Potential ◽  
Wind Park ◽  
Utility Scale

This paper presents the wind energy potential at Kudat Malaysia by considering the Levelized cost of energy (LCOE) model for combined wind turbine capacities. The combination of small- and utility-scale wind turbines is the key to the success of the operation of a wind park in the lower wind speed region. In a combination approach, the small-scale wind turbines provide the power required by the utility-scale wind turbines to start the blade rotation. For this reason, the particular closed-form equation was modified to determine the LCOE of a wind park with combined turbine capacities. The modified LCOE model can be used as a basis for setting tariff rates or define the economic feasibility of wind energy projects with combined wind turbine capacities.

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