Winds of Change: How Up-To-Date Forecasting Methods Could Help Change Brazilian Wind Energy Policy and Save Billions of US$

被引:4
作者
Bernardes, Fernando G., Jr. [1 ,2 ]
Vieira, Douglas A. G. [3 ]
Palade, Vasile [2 ]
Saldanha, Rodney R. [1 ,4 ]
机构
[1] Univ Fed Minas Gerais, Grad Program Elect Engn, Ave Antonio Carlos 6627, BR-31270901 Belo Horizonte, MG, Brazil
[2] Coventry Univ, Fac Engn Environm & Comp, Coventry CV1 5FB, W Midlands, England
[3] CEFETMG, PPMMC, Ave Amazonas 7675, BR-30510000 Belo Horizonte, MG, Brazil
[4] UFMG Fed Univ Minas Gerais, PPGEE, Ave Antonio Carlos 6627, BR-31270901 Belo Horizonte, MG, Brazil
关键词
energy policy-framework; wind energy; renewable energy; energy auctions; forecasting; electricity market; fuzzy time series; FUZZY TIME-SERIES; SOLAR; INFORMATION; POWER; ALGORITHM; INTERVALS; INDEX; STATE;
D O I
10.3390/en11112952
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
This paper proposes a revaluation of the Brazilian wind energy policy framework and the energy auction requirements. The proposed model deals with the four major issues associated with the wind policy framework that are: long-term wind speed sampling, wind speed forecasting reliability, energy commercialization, and the wind farm profitability. Brazilian wind policy, cross-checked against other countries policies, showed to be too restrictive and outdated. This paper proposes its renewal, through the adoption of international standards by Brazilian policy-makers, reducing the wind time sampling necessary to implement wind farms. To support such a policy change, a new wind forecasting method is designed. The method is based on fuzzy time series shaped with a statistical significance approach. It can be used to forecast wind behavior, by drawing the most-likely wind energy generation intervals given a confidence degree. The proposed method is useful to evaluate a wind farm profitability and design the biding strategy in auctions.
引用
收藏
页数:22
相关论文
共 53 条
  • [31] Integrating a wind- and solar-powered hybrid to the power system by coupling it with a hydroelectric power station with pumping installation
    Jurasz, Jakub
    Mikulik, Jerzy
    Krzywda, Magdalena
    Ciapala, Bartlomiej
    Janowski, Miroslaw
    [J]. ENERGY, 2018, 144 : 549 - 563
  • [32] Evolving granular analytics for interval time series forecasting
    Maciel L.
    Ballini R.
    Gomide F.
    [J]. Granular Computing, 2016, 1 (4) : 213 - 224
  • [33] Enhancing information for solar and wind energy technology deployment in Brazil
    Martins, Fernando Ramos
    Pereira, Enio Bueno
    [J]. ENERGY POLICY, 2011, 39 (07) : 4378 - 4390
  • [34] The Value of Volatile Resources in Electricity Markets
    Meyn, Sean
    Negrete-Pincetic, Matias
    Wang, Gui
    Kowli, Anupama
    Shafieepoorfard, Ehsan
    [J]. 49TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2010, : 1029 - 1036
  • [35] National Renewable Energy Laboratory, 2018, WIND PROSP
  • [36] Ozkan I, 2007, STUD FUZZ SOFT COMP, V215, P99
  • [37] A possibilistic fuzzy c-means clustering algorithm
    Pal, NR
    Pal, K
    Keller, JM
    Bezdek, JC
    [J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2005, 13 (04) : 517 - 530
  • [38] Abstraction and specialization of information granules
    Pedrycz, W
    Vukovich, G
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2001, 31 (01): : 106 - 111
  • [39] The Good, the Bad and the Uncertain: Bioenergy Use in the European Union
    Philippidis, George
    Bartelings, Heleen
    Helming, John
    M'barek, Robert
    Smeets, Edward
    van Meijl, Hans
    [J]. ENERGIES, 2018, 11 (10)
  • [40] Polzin F., 2015, SSRN ELECT J, DOI [10.2139/ssrn.2690477, DOI 10.2139/SSRN.2690477]