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

Agustín A. Sánchez de la Nieta, Virginia González and Javier Contreras
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Agustín A. Sánchez de la Nieta: E. T. S. de Ingenieros Industriales, University of Castilla-La Mancha, UCLM, 13071 Ciudad Real, Spain
Virginia González: E. T. S. de Ingenieros Industriales, University of Castilla-La Mancha, UCLM, 13071 Ciudad Real, Spain
Javier Contreras: E. T. S. de Ingenieros Industriales, University of Castilla-La Mancha, UCLM, 13071 Ciudad Real, Spain

Energies, 2016, vol. 9, issue 12, 1-19

Abstract: 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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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