| 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 |
2.262 |
| SJR Impact |
0.662 |
| 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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