Method to describe the distribution of wind velocity and its application in wind resource evaluation. Issue 12 (21st September 2022)

• Record Type: Journal Article
• Authors: Zhou, Hai; Hu, Siyu; Wu, Ji; Qin, Fang; Li, Dengxuan; Wei, Haikun
• Is Part Of: Energy science & engineering. Volume 10:Issue 12(2022)
• Page Start: 4747
• Page End: 4755
• Publication Date: 2022-09-21
• Subjects: concentration degree -- concentration period -- renewable resource evaluation -- wind power; Energy industries -- Periodicals; Energy development -- Periodicals; Power resources -- Periodicals; 621.042
• DOI: 10.1002/ese3.1302
• Languages: English
• ISSNs: 2050-0505

Abstract

Abstract: A convenient and effective method based on vector analysis was proposed in this paper to quantitatively describe the pattern of the wind speed distribution curve. This method can accurately describe the steepness of the single‐peak curve by using two parameters of concentration degree (CD) and concentration period (CP). Through the analysis of wind speed data in China over the past 40 years, this paper found that regional wind resources with larger CD were poorer, while regional wind resources with larger CP were richer. In addition, there were obvious cubic relationships between the CP and the average wind power density. These two parameters could reflect the richness of wind energy and realize the comparisons of wind resources across time and scales. In comparison with conventional approaches, this method is simpler and avoids fitting process, which has broad promotion prospects in the field of power grids. Abstract : This paper proposed a statistical method based on vector analysis to describe the shape of the wind speed distribution curve. By calculating two parameters of concentration degree (CD) and concentration period (CP), this method can accurately describe the shape of the wind speed probability curve, and realize the comparisons of wind resources across time and scales. In addition, there were obvious cubic relationships between the CP and the average wind power density. So that the richness of wind resources can be well reflected and estimated by CP value.