Exploring the suitability of MERRA-2 reanalysis data for wind energy estimation, analysis of wind characteristics and energy potential assessment for selected sites in Pakistan

被引:44
作者
Rabbani, R. [1 ]
Zeeshan, M. [1 ]
机构
[1] Natl Univ Sci & Technol NUST, Sch Civil & Environm Engn SCEE, Inst Environm Sci & Engn IESE, H-12 Campus, Islamabad 44000, Pakistan
关键词
MERRA-2 wind data; Wind shear exponent; Wind potential; Weibull distribution; Reanalysis data; Pakistan; WEIBULL DISTRIBUTION; POWER; SPEED; RESOURCE; GENERATION; REGION; STATISTICS; PROVINCE; HEIGHT; MODELS;
D O I
10.1016/j.renene.2020.03.100
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In the first part of this study, correlation between MERRA-2 reanalysis wind data and ground data is assessed for 12 selected locations. The correlation coefficient ranges from 0.17 to 0.75 among the sites. Sites with higher average wind speeds show comparatively stronger correlation. Besides, site specific factors are also investigated. In the second part, wind energy potential at same 12 locations is evaluated using high frequency (10-min interval) ground observed data. The diurnal, monthly and annual means for the sites are calculated and wind speed variance is observed utilizing wind data at six altitude levels (10m, 20m, 40m, 50m, 60m and 80m). The data is fitted to the Weibull distribution. Most probable wind speeds, wind speeds carrying maximum energy and wind power densities for all the locations are calculated for 50m and 80m height wind data. Significant variation of wind power density is observed along the height. A low cut-in speed wind turbine is selected, and annual energy production and capacity factors are estimated. Four locations with high wind power densities, namely Sujawal (355.6 W/m(2)), Sanghar (312.9 W/m(2)), Tando Ghulam Ali (288.2 W/m(2)) and Umerkot (252.8 W/m(2)) showed good potential to add wind share to global energy mix. (C) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1240 / 1251
页数:12
相关论文
共 65 条
[1]  
Ab O.V., 2011, EUR WIND ENERGY C EX, V10
[2]  
Adaramola M., 2005, ASSESSMENT WIND POWE, V46
[3]   Assessment of electricity generation and energy cost of wind energy conversion systems in north-central Nigeria [J].
Adaramola, M. S. ;
Paul, S. S. ;
Oyedepo, S. O. .
ENERGY CONVERSION AND MANAGEMENT, 2011, 52 (12) :3363-3368
[4]  
Ahmad R.A., 2015, PAKISTANS POWER CRIS
[5]  
Ahmed A., 2008, EGYPT, V33, DOI [10.1016/j.renene.2007.06.001., DOI 10.1016/J.RENENE.2007.06.001.]
[6]   Review of the use of Numerical Weather Prediction (NWP) Models for wind energy assessment [J].
Al-Yahyai, Sultan ;
Charabi, Yassine ;
Gastli, Adel .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2010, 14 (09) :3192-3198
[7]   Developing a climate model for Iran using GIS [J].
Alijani, B. ;
Ghohroudi, M. ;
Arabi, N. .
THEORETICAL AND APPLIED CLIMATOLOGY, 2008, 92 (1-2) :103-112
[8]   WIND ENERGY POTENTIAL IN EMILIA ROMAGNA, ITALY [J].
AMBROSINI, G ;
BENATO, B ;
GARAVASO, C ;
BOTTA, G ;
CENERINI, M ;
COMAND, D ;
STORK, C .
JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS, 1992, 39 (1-3) :211-220
[9]  
Badger Jake, 2012, ESTIMATION WIND SOLA
[10]   Wind power potential assessment for seven buoys data collection stations in Aegean Sea using Weibull distribution function [J].
Bagiorgas, Haralambos S. ;
Mihalakakou, Giouli ;
Rehman, Shafiqur ;
Al-Hadhrami, Luai M. .
JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY, 2012, 4 (01)