Investigation of wind characteristics and assessment of wind energy potential for Waterloo region, Canada

被引:92
作者
Li, MS [1 ]
Li, XG [1 ]
机构
[1] Univ Waterloo, Dept Mech Engn, Waterloo, ON N2L 3G1, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
maximum entropy principle (MEP); wind characteristics; wind energy potential; wind speed distribution;
D O I
10.1016/j.enconman.2005.02.011
中图分类号
O414.1 [热力学];
学科分类号
摘要
Wind energy becomes more and more attractive as one of the clean renewable energy resources. Knowledge of the wind characteristics is of great importance in the exploitation of wind energy resources for a site. It is essential in designing or selecting a wind energy conversion system for any application. This study examines the wind characteristics for the Waterloo region in Canada based on a data source measured at an elevation 10 in above the ground level over a 5-year period (1999-2003) with the emphasis on the suitability for wind energy technology applications. Characteristics such as annual, seasonal, monthly and diurnal wind speed variations and wind direction variations are examined. Wind speed data reveal that the windy months in Waterloo are from November to April, defined as the Cold Season in this study, with February being the windiest month. It is helpful that the high heating demand in the Cold Season coincides with the windy season. Analysis shows that the day time is the windy time, with 2 p.m. in the afternoon being the windiest moment. Moreover, a model derived from the maximum entropy principle (MEP) is applied to determine the diurnal, monthly, seasonal and yearly wind speed frequency distributions, and the corresponding Lagrangian parameters are determined. Based on these wind speed distributions, this study quantifies the available wind energy potential to provide practical information for the application of wind energy in this area. The yearly average wind power density is 105 W/m(2). The day and night time wind power density in the Cold Season is 180 and 111 W/m(2), respectively. (c) 2005 Elsevier Ltd. All rights reserved.
引用
收藏
页码:3014 / 3033
页数:20
相关论文
共 12 条
[1]   Energy output estimation for small-scale wind power generators using Weibull-representative wind data [J].
Celik, AN .
JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS, 2003, 91 (05) :693-707
[2]   Estimating wind speed distribution [J].
Dorvlo, ASS .
ENERGY CONVERSION AND MANAGEMENT, 2002, 43 (17) :2311-2318
[3]  
Elliott DL, 1993, PNLSA23109
[4]  
Holman J.P., 1984, Experimental Methods for Engineers
[5]   INFORMATION THEORY AND STATISTICAL MECHANICS [J].
JAYNES, ET .
PHYSICAL REVIEW, 1957, 106 (04) :620-630
[6]  
LI M, IN PRESS ATOMIZ SPRA
[7]  
LI M, 2004, INT J ENERGY, V1, P237
[8]  
LI M, 2004, P COMB I CAN SECT SP
[9]   The distribution and potential utilizablity of Zimbabwe's wind energy resource [J].
Mungwena, W .
RENEWABLE ENERGY, 2002, 26 (03) :363-377
[10]  
SHANNON E, 1948, BELL SYST TECH J, V379, P623