Probability distributions for wind speed volatility characteristics: A case study of Northern Norway

The Norwegian Arctic is rich in wind resources. The development of wind power in this region can boost green energy and also promote local economies. In wind power engineering, it is a tremendous advantage to base projects on a sound understanding of the intrinsic properties of wind resources in an...

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Autores principales:	Hao Chen, Stian Normann Anfinsen, Yngve Birkelund, Fuqing Yuan
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Wind energy Wind speed volatility Statistical analysis Probability distribution Arctic Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/f5d185323c574e778b320b09781198ce
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spelling	oai:doaj.org-article:f5d185323c574e778b320b09781198ce2021-11-18T04:49:49ZProbability distributions for wind speed volatility characteristics: A case study of Northern Norway2352-484710.1016/j.egyr.2021.07.125https://doaj.org/article/f5d185323c574e778b320b09781198ce2021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721005916https://doaj.org/toc/2352-4847The Norwegian Arctic is rich in wind resources. The development of wind power in this region can boost green energy and also promote local economies. In wind power engineering, it is a tremendous advantage to base projects on a sound understanding of the intrinsic properties of wind resources in an area. Wind speed volatility, a phenomenon that strongly affects wind power generation, has not received sufficient research attention. In this paper, a framework for studying short-term wind speed volatility with statistical analysis and probabilistic modeling is constructed for an existing wind farm in Northern Norway. It is found that unlike the characteristics of wind power volatility, wind speed volatility cannot be described by the normal distribution. The reason is that even though the probability distribution of wind speed volatility is centrally symmetric, it is much more centrally concentrated and has thicker tails. After comparing three distributions corresponding to different sampling periods, this paper suggests utilizing the t distribution, with average modeling RMSE less than 0.006 and R2 exceeding 0.995 and with the best modeling scenario of temporal resolution, the 30 mins has an RMSE of 0.0051 and an R2 of 0.997, to more accurately and effectively explore the fluctuating characteristics of wind speed.Hao ChenStian Normann AnfinsenYngve BirkelundFuqing YuanElsevierarticleWind energyWind speed volatilityStatistical analysisProbability distributionArcticElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 7, Iss , Pp 248-255 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Wind energy Wind speed volatility Statistical analysis Probability distribution Arctic Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Wind energy Wind speed volatility Statistical analysis Probability distribution Arctic Electrical engineering. Electronics. Nuclear engineering TK1-9971 Hao Chen Stian Normann Anfinsen Yngve Birkelund Fuqing Yuan Probability distributions for wind speed volatility characteristics: A case study of Northern Norway
description	The Norwegian Arctic is rich in wind resources. The development of wind power in this region can boost green energy and also promote local economies. In wind power engineering, it is a tremendous advantage to base projects on a sound understanding of the intrinsic properties of wind resources in an area. Wind speed volatility, a phenomenon that strongly affects wind power generation, has not received sufficient research attention. In this paper, a framework for studying short-term wind speed volatility with statistical analysis and probabilistic modeling is constructed for an existing wind farm in Northern Norway. It is found that unlike the characteristics of wind power volatility, wind speed volatility cannot be described by the normal distribution. The reason is that even though the probability distribution of wind speed volatility is centrally symmetric, it is much more centrally concentrated and has thicker tails. After comparing three distributions corresponding to different sampling periods, this paper suggests utilizing the t distribution, with average modeling RMSE less than 0.006 and R2 exceeding 0.995 and with the best modeling scenario of temporal resolution, the 30 mins has an RMSE of 0.0051 and an R2 of 0.997, to more accurately and effectively explore the fluctuating characteristics of wind speed.
format	article
author	Hao Chen Stian Normann Anfinsen Yngve Birkelund Fuqing Yuan
author_facet	Hao Chen Stian Normann Anfinsen Yngve Birkelund Fuqing Yuan
author_sort	Hao Chen
title	Probability distributions for wind speed volatility characteristics: A case study of Northern Norway
title_short	Probability distributions for wind speed volatility characteristics: A case study of Northern Norway
title_full	Probability distributions for wind speed volatility characteristics: A case study of Northern Norway
title_fullStr	Probability distributions for wind speed volatility characteristics: A case study of Northern Norway
title_full_unstemmed	Probability distributions for wind speed volatility characteristics: A case study of Northern Norway
title_sort	probability distributions for wind speed volatility characteristics: a case study of northern norway
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/f5d185323c574e778b320b09781198ce
work_keys_str_mv	AT haochen probabilitydistributionsforwindspeedvolatilitycharacteristicsacasestudyofnorthernnorway AT stiannormannanfinsen probabilitydistributionsforwindspeedvolatilitycharacteristicsacasestudyofnorthernnorway AT yngvebirkelund probabilitydistributionsforwindspeedvolatilitycharacteristicsacasestudyofnorthernnorway AT fuqingyuan probabilitydistributionsforwindspeedvolatilitycharacteristicsacasestudyofnorthernnorway
_version_	1718425012332920832

Probability distributions for wind speed volatility characteristics: A case study of Northern Norway

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