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LAPSE:2019.0379
Published Article
LAPSE:2019.0379
Portfolio Decision of Short-Term Electricity Forecasted Prices through Stochastic Programming
Agustín A. Sánchez de la Nieta, Virginia González, Javier Contreras
February 27, 2019
Deregulated electricity markets encourage firms to compete, making the development of renewable energy easier. An ordinary parameter of electricity markets is the electricity market price, mainly the day-ahead electricity market price. This paper describes a new approach to forecast day-ahead electricity market prices, whose methodology is divided into two parts as: (i) forecasting of the electricity price through autoregressive integrated moving average (ARIMA) models; and (ii) construction of a portfolio of ARIMA models per hour using stochastic programming. A stochastic programming model is used to forecast, allowing many input data, where filtering is needed. A case study to evaluate forecasts for the next 24 h and the portfolio generated by way of stochastic programming are presented for a specific day-ahead electricity market. The case study spans four weeks of each one of the years 2014, 2015 and 2016 using a specific pre-treatment of input data of the stochastic programming (SP) model. In addition, the results are discussed, and the conclusions are drawn.
Keywords
ARIMA models, day-ahead electricity market price, forecasting portfolio, stochastic programming
Suggested Citation
Sánchez de la Nieta AA, González V, Contreras J. Portfolio Decision of Short-Term Electricity Forecasted Prices through Stochastic Programming. (2019). LAPSE:2019.0379
Author Affiliations
Sánchez de la Nieta AA: E. T. S. de Ingenieros Industriales, University of Castilla-La Mancha, UCLM, 13071 Ciudad Real, Spain
González V: E. T. S. de Ingenieros Industriales, University of Castilla-La Mancha, UCLM, 13071 Ciudad Real, Spain
Contreras J: E. T. S. de Ingenieros Industriales, University of Castilla-La Mancha, UCLM, 13071 Ciudad Real, Spain [ORCID]
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Journal Name
Energies
Volume
9
Issue
12
Article Number
E1069
Year
2016
Publication Date
2016-12-16
Published Version
ISSN
1996-1073
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Original Submission
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PII: en9121069, Publication Type: Journal Article
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LAPSE:2019.0379
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doi:10.3390/en9121069
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Feb 27, 2019
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CC BY 4.0
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Feb 27, 2019
 
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Feb 27, 2019
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Original Submitter
Calvin Tsay
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