A Statistical Analysis of Wind Speed Probabilistic Distributions for the Wind Power Assessment in Different Regions

被引:1
|
作者
Bay, Yuly [1 ]
Ruban, Nikolay [2 ]
Andreev, Mikhail [2 ]
Gusev, Alexandr [2 ]
机构
[1] Tomsk Polytech Univ, Div Power & Elect Ngineering, 30 Lenin Ave, Tomsk, Russia
[2] Tomsk Polytech Univ, Div Power & Elect Engn, 30 Lenin Ave, Tomsk, Russia
来源
PRZEGLAD ELEKTROTECHNICZNY | 2021年 / 97卷 / 12期
关键词
power system; wind speed time series; probability density function; cumulative distribution function;
D O I
10.15199/48.2021.12.14
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The penetration of renewable energy sources (RES) into the electricity supply is gaining popularity all over the world, including countries that have large oil and gas reserves, since only the development of alternative energy will help avoid regression and take a green path development, reducing the damage to the environment. According to estimates of the International Energy Agency (IEA), the capacity of RES units built in China in 2016 was 34 GW, and Australia is one of the world leaders in the photovoltaic power plants installation, the share of which in the Australian electricity production exceeds 3%. It should be noted, that the final power generation capacity and stability are stochastic (probabilistic) in nature. Unlike the classical type generator, the output RES characteristics depend on the geographical features of the installation area, the season, and prevailing winds. Risks associated with inaccurate knowledge of the cumulative distribution function (CDF) describing these sources, as well as environmental uncertainties, are the reasons why it is more difficult for distribution network operators (DNO) to take RES into account in the power balance calculations. The wind speed CDF clarification can provide significant assistance in predicting the RES power production.
引用
收藏
页码:82 / 85
页数:4
相关论文
共 50 条
  • [41] Dynamic non-constraint ensemble model for probabilistic wind power and wind speed forecasting
    Wang, Yun
    Xu, Houhua
    Zou, Runmin
    Zhang, Fan
    Hu, Qinghua
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2024, 204
  • [42] Longterm Analysis of Wind Speed and Wind Power Resource Assessment for the Site Vijayawada, Andhra Pradesh, India
    Murthy, K. S. R.
    Rahi, O. P.
    Sonkar, Preeti
    Ram, Sita
    [J]. 2017 6TH INTERNATIONAL CONFERENCE ON COMPUTER APPLICATIONS IN ELECTRICAL ENGINEERING - RECENT ADVANCES (CERA), 2017, : 140 - 145
  • [43] Five different distributions and metaheuristics to model wind speed distribution
    Wadi, Mohammed
    [J]. JOURNAL OF THERMAL ENGINEERING, 2021, 7 (08): : 1898 - 1920
  • [44] Effects of different wind data sources in offshore wind power assessment
    Soukissian, Takvor H.
    Papadopoulos, Anastasios
    [J]. RENEWABLE ENERGY, 2015, 77 : 101 - 114
  • [45] Statistical model of the dynamics of wind speed and wind direction
    Kaplya, E. V.
    [J]. RUSSIAN METEOROLOGY AND HYDROLOGY, 2014, 39 (12) : 804 - 808
  • [46] THE COMPARATIVE ANALYSIS OF TWO DIFFERENT STATISTICAL DISTRIBUTIONS USED TO ESTIMATE THE WIND ENERGY POTENTIAL
    Kurban, Mehmet
    Hocaoglu, Fatih Onur
    Kantar, Yeliz Mert
    [J]. PAMUKKALE UNIVERSITY JOURNAL OF ENGINEERING SCIENCES-PAMUKKALE UNIVERSITESI MUHENDISLIK BILIMLERI DERGISI, 2007, 13 (01): : 103 - 109
  • [47] Statistical model of the dynamics of wind speed and wind direction
    E. V. Kaplya
    [J]. Russian Meteorology and Hydrology, 2014, 39 : 804 - 808
  • [48] Analysis of some flexible families of distributions for estimation of wind speed distributions
    Usta, Ilhan
    Kantar, Yeliz Mert
    [J]. APPLIED ENERGY, 2012, 89 (01) : 355 - 367
  • [49] Joint Probabilistic Modeling of Wind Speed and Wind Direction for Wind Energy Analysis: A Case Study in Humansdorp and Noupoort
    Arashi, Mohammad
    Nagar, Priyanka
    Bekker, Andriette
    [J]. SUSTAINABILITY, 2020, 12 (11)
  • [50] Economic assessment of renewable power generation based on wind speed and solar radiation in urban regions
    Kassem, Y.
    Gokcekus, H.
    Camur, H.
    [J]. GLOBAL JOURNAL OF ENVIRONMENTAL SCIENCE AND MANAGEMENT-GJESM, 2018, 4 (04): : 465 - 482