Errors in wind resource and energy yield assessments based on the Weibull distribution

被引:10
|
作者
Jourdier, Benedicte [1 ,2 ,3 ]
Drobinski, Philippe [1 ]
机构
[1] UPMC Univ Paris 06, ENS, PSL Res Univ, Sorbonne Univ,LMD IPSL,Ecole Polytech,Univ Paris, Palaiseau, France
[2] French Environm & Energy Management Agcy ADEME, Angers, France
[3] EDF R&D MFFE, Appl Meteorol Grp, Chatou, France
关键词
Meteorology and atmospheric dynamics (mesoscale meteorology); PROBABILITY-DISTRIBUTIONS; OUTPUT ESTIMATION; SPEED DATA; FIT;
D O I
10.5194/angeo-35-691-2017
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
The methodology used in wind resource assessments often relies on modeling the wind-speed statistics using a Weibull distribution. In spite of its common use, this distribution has been shown to not always accurately model real wind-speed distributions. Very few studies have examined the arising errors in power outputs, using either observed power productions or theoretical power curves. This article focuses on France, using surface wind measurements at 89 locations covering all regions of the country. It investigates how statistical modeling using a Weibull distribution impacts the prediction of the wind energy content and of the power output in the context of an annual energy production assessment. For this purpose it uses a plausible power curve adapted to each location. Three common methods for fitting the Weibull distribution are tested (maximum likelihood, first and third moments, and the Wind Atlas Analysis and Application Program (WAsP) method). The first two methods generate large errors in the production (mean absolute error around 5 %), especially in the southern areas where the goodness of fit of the Weibull distribution is poorer. The production is mainly overestimated except at some locations with bimodal wind distributions. With the third method, the errors are much lower at most locations (mean absolute error around 2 %). Another distribution, a mixed Rayleigh-Rice distribution, is also tested and shows better skill at assessing the wind energy yield.
引用
收藏
页码:691 / 700
页数:10
相关论文
共 50 条
  • [1] Statistical Analysis of Wind Power Using Weibull Distribution to Maximize Energy Yield
    Aldaoudeyeh, Al-Motasem, I
    Alzaareer, Khaled
    2020 IEEE PES & IAS POWERAFRICA CONFERENCE, 2020,
  • [2] Wind Resource Assessment for Wind Energy Utilization in Port Harcourt, River State, Nigeria, Based on Weibull Probability Distribution Function
    Izelu, Christopher Okechukwu
    Agberegha, Orobome Larry
    Oguntuberu, Olusola Bode
    INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH, 2013, 3 (01): : 180 - 185
  • [3] Potential of wind energy in Cameroon based on Weibull, normal, and lognormal distribution
    Christian Kenfack-Sadem
    Raphaël Tagne
    François Beceau Pelap
    Gerard Nfor Bawe
    International Journal of Energy and Environmental Engineering, 2021, 12 : 761 - 786
  • [4] Potential of wind energy in Cameroon based on Weibull, normal, and lognormal distribution
    Kenfack-Sadem, Christian
    Tagne, Raphael
    Pelap, Francois Beceau
    Bawe, Gerard Nfor
    INTERNATIONAL JOURNAL OF ENERGY AND ENVIRONMENTAL ENGINEERING, 2021, 12 (04) : 761 - 786
  • [5] Assessment of wind energy potential based on Weibull and Rayleigh distribution models
    Serban, Alexandru
    Paraschiv, Lizica Simona
    Paraschiv, Spiru
    ENERGY REPORTS, 2020, 6 : 250 - 267
  • [6] Improved wind resource modeling using bimodal Weibull distribution
    Aldaoudeyeh, Al-Motasem
    JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY, 2024, 16 (05)
  • [7] Wind Energy Resource Assessment for Tokelau with Accurate Weibull Parameters
    Singh, Krishneel A.
    Kutty, Saiyad S.
    Khan, Mohamed G. M.
    Ahmed, Mohammed R.
    2019 9TH INTERNATIONAL CONFERENCE ON POWER AND ENERGY SYSTEMS (ICPES), 2019,
  • [8] Potential of wind energy in Medina, Saudi Arabia based on Weibull distribution parameters
    AlQdah, Khaled S.
    Alahmdi, Raed
    Alansari, Abdulrahman
    Almoghamisi, Abdulrahman
    Abualkhair, Mohanad
    Awais, Muhammad
    WIND ENGINEERING, 2021, 45 (06) : 1652 - 1661
  • [9] Weibull distribution for determination of wind analysis and energy production
    Oral, Faruk
    Ekmekci, Ismail
    Onat, Nevzat
    WORLD JOURNAL OF ENGINEERING, 2015, 12 (03) : 215 - 220
  • [10] Wind resource clustering based on statistical Weibull characteristics
    van Vuuren, Chantelle Y. Janse
    Vermeulen, Hendrik J.
    WIND ENGINEERING, 2019, 43 (04) : 359 - 376