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Stochastic Modelling Applied to Prediction of Electricity Saving by using Solar Water Heating Systems for Low-Income Families

B. G. Menita, J. L. Domingos, E. G. Domingues, A. J. Alves, W. P. Calixto

2016/5/20

Abstract

Solar water heating systems for low-income families as Energy Efficiency Action bring energetic benefits for the consumers and the Brazilian Electrical System and also contribute for the reduction of the environmental impacts associated with generation, transmission and distribution of electricity. This paper presents the stochastic modelling for the generation of future scenarios of electricity saving of Energy Efficiency Projects that involves solar water heating systems for low-income families. The model is developed by using the Geometric Brownian Motion Stochastic Process with Mean Reversion (GBM-MR) associated with the Monte Carlo simulation technique. As a result it is possible to obtain the time series and the probability distribution function of the energy saving for each year of the simulation period. Once there is no historical data available for obtaining the standard deviation and the mean reversion speed of the stochastic process, it is presented a sensitivity analysis in order to verify how these parameters influence on the results.

Published in: Renewable Energy & Power Quality Journal (RE&PQJ, Nº. 14)
Pages:204-209 Date of Publication: 2016/5/20
ISSN: 2172-038X Date of Current Version:2016/05/04
REF:268-16 Issue Date: May 2016
DOI:10.24084/repqj14.268 Publisher: EA4EPQ

Authors and affiliations

B. G. Menita, J. L. Domingos, E. G. Domingues, A. J. Alves, W. P. Calixto
Graduate Program in Technology of Sustainable Processes. Federal Institute of Education, Science and Technology of Goiás (IFG). Brazil

Key words

Solar water heating, energy efficiency, Geometric Brownian Motion, Monte Carlo simulation, sensitivity analysis.

References

[1] Centrais Elétricas Brasileiras S/A, Energia solar para aquecimento de água do Brasil: contribuições da Eletrobrás Procel e parceiros, Rio de Janeiro: Eletrobrás, 2012.
[2] Ministério de Minas e Energia, Plano Nacional de Eficiência Energética,Brasília: MME, 2011.
[3] Ministério de Minas e Energia, Plano Nacional de Energia 2030, Brasília: MME, 2008.
[4] E. Domingues, Análise de risco para otimizar carteiras de ativos físicos em geração de energia elétrica, Itajubá: Tese de Doutorado, Curso de Pós-Graduação em Engenharia Elétrica, Universidade Federal de Itajubá, 2003.
[5] J. Hull, Options, Futures and Other Derivatives, Prentice-Hall, second edition, 1993.
[6] E. Fonseca, Comparação entre simulações pelo Movimento Geométrico Browniano e Movimento de Reversão à Média no cálculo do Fluxo de Caixaat Risk do departamento de downstream de uma empresa de petróleo, Rio de Janeiro : Dissertação de Mestrado, Instituto COPPEAD de Administração, Universidade Federal do Rio de Janeiro, 2006.
[7] A. K.Dixit and R. S.Pindyck, Investment Under Uncertainty, Princeton University Press, Princeton, New Jersey, 1993.
[8] G. S.Fishman, Monte Carlo – Concepts, Algorithms and Applications, IE-Springer-Verlag, New York, 1996.

 
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