Wind Power Economic Feasibility under Uncertainty and the Application of ANN in Sensitivity Analysis

被引：24

作者：

Rotela Junior, Paulo ^{[1
]}

Fischetti, Eugenio ^{[1
]}

Araujo, Victor G. ^{[1
]}

Peruchi, Rogerio S. ^{[1
]}

Aquila, Giancarlo ^{[2
]}

Rocha, Luiz Celio S. ^{[3
]}

Lacerda, Liviam S. ^{[1
]}

机构：

[1] Univ Fed Paraiba, Dept Prod Engn, BR-58051970 Joao Pessoa, Paraiba, Brazil

[2] Univ Fed Itajuba, Inst Ind Engn & Management, BR-37500000 Itajuba, Brazil

[3] Fed Inst Educ Sci & Technol Northern Minas Gerais, Dept Management, BR-39900000 Almenara, Brazil

来源：

ENERGIES | 2019年 / 12卷 / 12期

关键词：

economic feasibility; net present value; artificial neural networks; wind power; sensitivity analysis; ARTIFICIAL NEURAL-NETWORKS; FEED-IN TARIFFS; VIABILITY ANALYSIS; RISK ANALYSIS; ENERGY; GENERATION; PROFITABILITY; PREDICTION; SYSTEMS; IMPACT;

D O I：

10.3390/en12122281

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：

Wind power has grown popular in past recent years due to environmental issues and the search for alternative energy sources. Thus, the viability for wind power generation projects must be studied in order to attend to the environmental concerns and still be attractive and profitable. Therefore, this article aims to perform a sensitive analysis in order to identify the variables that influence most in the viability of a wind power investment for small size companies in the Brazilian northeast. For this, a stochastic analysis of viability through Monte Carlo Simulation (MCS) will be made and afterwards, Artificial Neural Networks (ANN) models will be applied for the most relevant variables identification. Through the sensitivity, it appears that the most relevant factors in the analysis are the speed of wind, energy tariff and the investment amount. Thus, the viability of the investment is straightly tied to the region where the wind turbine is installed, and the government incentives may allow decreasing in the investment amount for wind power. Based on this, incentives programs for the production of clean energy include cheaper purchase of wind turbines, lower taxing and financing rates, can make wind power more profitable and attractive.

引用

页数：10

共 33 条

[11] Economic viability of biogas and green self-employment opportunities
Chakrabarty, Sayan
Boksh, F. I. M. Muktadir
Chakraborty, Arpita
[J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2013, 28 : 757 - 766
[12] The artificial neural network used in the study of sensitivities in the IRIS reactor pressurizer
Costa, Samuel Pimentel
de Andrade Lima, Fernando Roberto
Franklin Lapa, Celso Marcelo
de Abreu Mol, Antonio Carlos
Brayner de Oliveira Lira, Carlos Alberto
[J]. PROGRESS IN NUCLEAR ENERGY, 2013, 69 : 64 - 70
[13] Wind energy in Brazil: From the power sector's expansion crisis model to the favorable environment
da Silva, Neilton Fidelis
Rosa, Luiz Pinguelli
Vasconcelos Freitas, Marcos Aurelio
Pereira, Marcio Giannini
[J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2013, 22 : 686 - 697
[14] Damodaran A., 2018, BETAS SECTOR US
[15] Empresa de Pesquisa Energetica (EPE), 2016, AV GER US HIBR EOL F, P32
[16] The evaluation of feed-in tariff regulation of Turkey for onshore wind energy based on the economic analysis
Erturk, Mehmet
[J]. ENERGY POLICY, 2012, 45 : 359 - 367
[17] Auctions for renewable energy support - Suitability, design, and first lessons learned
Haufe, Marie-Christin
Ehrhart, Karl-Martin
[J]. ENERGY POLICY, 2018, 121 : 217 - 224
[18] Hawawini G., 2009, FINANCE EXECUTIVES M, P190
[19] Haykin S., 2009, Neural Networks and Learning Machines, V3
[20] Distributed photovoltaic generation in Brazil: An economic viability analysis of small-scale photovoltaic systems in the residential and commercial sectors
Holdermann, Claudius
Kissel, Johannes
Beigel, Juergen
[J]. ENERGY POLICY, 2014, 67 : 612 - 617

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