Modelling the Swedish wind power production using MERRA reanalysis data

被引:105
|
作者
Olauson, Jon [1 ]
Bergkvist, Mikael [1 ]
机构
[1] Uppsala Univ, Dept Engn Sci, Div Elect, Uppsala, Sweden
关键词
Wind power; Physical model; Wind variability; MERRA reanalysis data; GENERATION; SIMULATION; SYSTEMS;
D O I
10.1016/j.renene.2014.11.085
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The variability of wind power will be an increasing challenge for the power system as wind penetration grows and thus needs to be studied. In this paper a model for generation of hourly aggregated wind power time series is described and evaluated. The model is based on MERRA reanalysis data and information on wind energy converters in Sweden. Installed capacity during the studied period (2007 2012) increased from around 600 to over 3500 MW. When comparing with data from the Swedish TSO, the mean absolute error in hourly energy was 2.9% and RMS error was 3.8%. The model was able to adequately capture step changes and also yielded a nicely corresponding distribution of hourly energy. Two key factors explaining the good results were the use of a globally optimised power curve smoothing parameter and the correction of seasonal and diurnal bias. Because of bottlenecks in the Swedish transmission system it is relevant to model certain areas separately. For the two southern areas the MAE were 3.7 and 4.2%. The northern area was harder to model and had a MAE of 6.5%. This might be explained by a low installed capacity, more complex terrain and icing losses not captured in the model. (C) 2014 The Authors. Published by Elsevier Ltd.
引用
收藏
页码:717 / 725
页数:9
相关论文
共 50 条
  • [1] Modeling of Ethiopian Wind Power Production Using ERA5 Reanalysis Data
    Nefabas, Kena Likassa
    Soder, Lennart
    Mamo, Mengesha
    Olauson, Jon
    [J]. ENERGIES, 2021, 14 (09)
  • [2] Limitations of reanalysis data for wind power applications
    Davidson, Michael R.
    Millstein, Dev
    [J]. WIND ENERGY, 2022, 25 (09) : 1646 - 1653
  • [3] Quantitative synergy assessment of regional wind-solar energy resources based on MERRA reanalysis data
    Zhang, Hengxu
    Cao, Yongji
    Zhang, Yi
    Terzija, Vladimir
    [J]. APPLIED ENERGY, 2018, 216 : 172 - 182
  • [4] Future wind power production variations in the Swedish power system
    Olsson, Jonas
    Skoglund, Lars
    Carlsson, Fredrik
    Bertling, Lina
    [J]. 2010 IEEE PES CONFERENCE ON INNOVATIVE SMART GRID TECHNOLOGIES EUROPE (ISGT EUROPE), 2010,
  • [5] A simple hourly wind power simulation for the South-West region of Western Australia using MERRA data
    Laslett, Dean
    Creagh, Chris
    Jennings, Philip
    [J]. RENEWABLE ENERGY, 2016, 96 : 1003 - 1014
  • [6] Offshore wind resource assessment using reanalysis data
    Ahmad, Sajeer
    Abdullah, Muhammad
    Kanwal, Ammara
    Tahir, Zia ul Rehman
    Bin Saeed, Usama
    Manzoor, Fabia
    Atif, Muhammad
    Abbas, Sabtain
    [J]. WIND ENGINEERING, 2022, 46 (04) : 1173 - 1186
  • [7] Wind Speed Evaluation of MERRA-2, ERA-Interim and ERA-5 Reanalysis Data at a Wind Farm Located in Brazil
    Santos, Jhoseny Souza
    Sakagami, Yoshiaki
    Haas, Reinaldo
    Passos, JUlio Cesar
    Machuca, Monica Nassar
    Radunz, William Correa
    Dias, Eduardo
    Lima, Mayara Miqueletti
    [J]. PROCEEDINGS OF THE ISES SOLAR WORLD CONFERENCE 2019 AND THE IEA SHC SOLAR HEATING AND COOLING CONFERENCE FOR BUILDINGS AND INDUSTRY 2019, 2019, : 2180 - 2189
  • [8] Combination of meteorological reanalysis data and stochastic simulation for modelling wind generation variability
    Koivisto, Matti
    Jonsdottir, Gudrun Margret
    Sorensen, Poul
    Plakas, Konstantinos
    Cutululis, Nicolaos
    [J]. RENEWABLE ENERGY, 2020, 159 : 991 - 999
  • [9] What can reanalysis data tell us about wind power?
    Rose, Stephen
    Apt, Jay
    [J]. RENEWABLE ENERGY, 2015, 83 : 963 - 969
  • [10] Exploring the suitability of MERRA-2 reanalysis data for wind energy estimation, analysis of wind characteristics and energy potential assessment for selected sites in Pakistan
    Rabbani, R.
    Zeeshan, M.
    [J]. RENEWABLE ENERGY, 2020, 154 : 1240 - 1251