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Energies 2016, 9(12), 1069; doi:10.3390/en9121069

Portfolio Decision of Short-Term Electricity Forecasted Prices through Stochastic Programming

E. T. S. de Ingenieros Industriales, University of Castilla-La Mancha, UCLM, 13071 Ciudad Real, Spain
Author to whom correspondence should be addressed.
Academic Editor: Tapas Mallick
Received: 29 August 2016 / Revised: 12 November 2016 / Accepted: 11 December 2016 / Published: 16 December 2016
(This article belongs to the Special Issue Forecasting Models of Electricity Prices)
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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. View Full-Text
Keywords: ARIMA models; day-ahead electricity market price; forecasting portfolio; stochastic programming ARIMA models; day-ahead electricity market price; forecasting portfolio; stochastic programming

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Sánchez de la Nieta, A.A.; González, V.; Contreras, J. Portfolio Decision of Short-Term Electricity Forecasted Prices through Stochastic Programming. Energies 2016, 9, 1069.

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