Title Portfolio Decision of Short-Term Electricity Forecasted Prices through Stochastic Programming
Authors SÁNCHEZ DE LA NIETA LÓPEZ, AGUSTÍN ALEANDRO, Gonzalez, Virginia , Contreras, Javier
External publication Si
Means Energies
Scope Article
Nature Científica
JCR Quartile 2
SJR Quartile 1
JCR Impact 2.26200
SJR Impact 0.66200
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020497999&doi=10.3390%2fen9121069&partnerID=40&md5=e9040685f7a8ec11e6053f46fc718246
Publication date 01/12/2016
ISI 000392402700070
Scopus Id 2-s2.0-85020497999
DOI 10.3390/en9121069
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
Universidad Loyola members

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