Offshore wind resource assessment by characterizing weather regimes based on self-organizing map

Yang, Shangshang1,2; Yuan, Huiling1,2,3; Dong, Li4,5

2022-12-01

10.1088/1748-9326/aca2c2

Environmental Research Letters   Impact Factor & Quartile

1748-9326

As offshore wind power is continuously integrated into the electric power systems in around the world, it is critical to understand its variability. Weather regimes (WRs) can provide meteorological explanations for fluctuations in wind power. Instead of relying on traditional large-scale circulation WRs, this study focuses on assessing the dependency of wind resources on WRs in the tailored region clustered based on the finer spatial scale. For this purpose, we have applied self-organizing map algorithm to cluster atmospheric circulations over the South China Sea (SCS) and characterized wind resources for the classified WRs. Results show that WRs at mesoscale can effectively capture weather systems driving wind power production variability, especially on multi-day timescale. Capacity factor reconstruction during four seasons illustrates that WRs highly influence most areas in winter and southern part of SCS in summer, and WRs can serve as a critical source of predicting the potential of wind resources. In addition, we further qualify the wind power intermittency and complementarity under different WRs, which have not been assessed associated with WRs. During WRs with changeable atmosphere conditions, the high complementarity over coastal areas can reduce the impact of intermittency on wind power generation. The proposed approach is able to be implemented in any region and may benefit wind resource evaluation and characterization.

offshore wind resource assessment wind power weather regime self-organizing map complementarity

SCI

English

Others

Shenzhen Science and Technology Program[20200925160922002] ; National Natural Science Foundation of China["42075187","41975060"] ; Fundamental Research Funds for the Central Universities[0209-14380104]

Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences

Environmental Sciences ; Meteorology & Atmospheric Sciences

WOS:000890023900001

IOP Publishing Ltd

Web of Science

1.Nanjing Univ, Sch Atmospher Sci, Nanjing, Peoples R China
2.Nanjing Univ, Key Lab Mesoscale Severe Weather, Minist Educ, Nanjing, Peoples R China
3.Nanjing Univ, Frontiers Sci Ctr Crit Earth Mat Cycling, Nanjing, Peoples R China
4.Southern Univ Sci & Technol, Dept Earth & Space Sci, Shenzhen, Peoples R China
5.Southern Univ Sci & Technol, Acad Adv Interdisciplinary Studies, Shenzhen, Peoples R China

Yang, Shangshang,Yuan, Huiling,Dong, Li. Offshore wind resource assessment by characterizing weather regimes based on self-organizing map[J]. Environmental Research Letters,2022,17(12).

Yang, Shangshang,Yuan, Huiling,&Dong, Li.(2022).Offshore wind resource assessment by characterizing weather regimes based on self-organizing map.Environmental Research Letters,17(12).

Yang, Shangshang,et al."Offshore wind resource assessment by characterizing weather regimes based on self-organizing map".Environmental Research Letters 17.12(2022).