Short-term Wind Variability Analysis of Afe Babalola University

被引:0
|
作者
Ayamolowo, Oladimeji Joseph [1 ]
Omo-Irabor, Benedict [1 ]
Buraimoh, Elutunji [2 ]
Davidson, Innocent E. [2 ]
机构
[1] Afe Babalola Univ, Dept Elect & Comp Engn, Ado Ekiti, Nigeria
[2] Durban Univ Technol, Dept Elect Power Engn, Durban, South Africa
来源
2020 CLEMSON UNIVERSITY POWER SYSTEMS CONFERENCE (PSC) | 2020年
关键词
Wind speed; Wind Power Density Renewable Energy Sources (RES); anemometer; wind power plants (WPPs); Afe Babalola University(ABUAD); PREDICTION; OUTPUT; SPEED;
D O I
暂无
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 increasing damaging effect of fossil fueled generators has necessitated the need for diversification into renewable energy sources. However, these abundant renewable sources are limited by their variability at various seasons which often results in stochastic output power. In this paper, short term variability analysis of wind resource in Afe Babalola University is presented from January 2018 to December 2018. Wind speed data were collected using the anemometer at various times and sites within this period and used to estimate the potential of wind resource in ABUAD. The results showed that the wind energy resource is higher between the months of April to September, but insufficient to meet the energy need of ABUAD, hence necessitating the need of a hybrid generating system. An optimized hybrid system comprising of one, 1650kW wind turbine, 2000kW PV panels, 1900kW Diesel generator, 2000kW Converter, and one Trojan T-105 battery was obtained from HOMER software, with levelized cost of energy (COE) of 0.414$/yr and Net Present Cost(NPC) of $41,353,948.
引用
收藏
页数:7
相关论文
共 50 条
  • [1] The impact of wind field spatial heterogeneity and variability on short-term wind power forecast errors
    Yang, Mao
    Zhang, Luobin
    Cu, Yang
    Yang, Qiongqiong
    Huang, Binyang
    JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY, 2019, 11 (03)
  • [2] Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model
    Zhang, Jinhua
    Yan, Jie
    Infield, David
    Liu, Yongqian
    Lien, Fue-sang
    APPLIED ENERGY, 2019, 241 : 229 - 244
  • [3] Study on Modeling of Short-term Wind speed Forecasting based on Time Series Analysis
    Zhang, Yu
    PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON SOCIAL SCIENCE AND TECHNOLOGY EDUCATION (ICSSTE 2015), 2015, 18 : 426 - 429
  • [4] Short-Term Power Forecasting and Uncertainty Analysis of Wind Farm at Multiple Time Scales
    Zhang, Tianren
    Huang, Yuping
    Liao, Hui
    Gong, Xianfu
    Peng, Bo
    IEEE ACCESS, 2024, 12 : 25129 - 25145
  • [5] Spatio-temporal analysis and modeling of short-term wind power forecast errors
    Tastu, Julija
    Pinson, Pierre
    Kotwa, Ewelina
    Madsen, Henrik
    Nielsen, Henrik Aa.
    WIND ENERGY, 2011, 14 (01) : 43 - 60
  • [6] Short-term temporal variability of atmospheric surface pressure and wind speed in the Canadian Arctic
    Nikolai Nawri
    Ronald E. Stewart
    Theoretical and Applied Climatology, 2009, 98 : 151 - 170
  • [7] Mitigation of Short-Term Wind Power Ramps through Forecast-Based Curtailment
    Probst, Oliver
    Minchala, Luis, I
    APPLIED SCIENCES-BASEL, 2021, 11 (10):
  • [8] Adaptive short-term wind power forecasting with concept drifts
    Li, Yanting
    Wu, Zhenyu
    Su, Yan
    RENEWABLE ENERGY, 2023, 217
  • [9] A review of very short-term wind and solar power forecasting
    Tawn, R.
    Browell, J.
    RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2022, 153
  • [10] Comparison of Three Methods for Short-Term Wind Power Forecasting
    Chen, Qin
    Folly, Komla A.
    2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2018,