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

Author

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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)

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.

Suggested Citation

  • Agustín A. Sánchez de la Nieta & Virginia González & Javier Contreras, 2016. "Portfolio Decision of Short-Term Electricity Forecasted Prices through Stochastic Programming," Energies, MDPI, vol. 9(12), pages 1-19, December.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:12:p:1069-:d:85406
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    References listed on IDEAS

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    Cited by:

    1. Sánchez de la Nieta, A.A. & Contreras, J., 2020. "Quantifying the effect of renewable generation on day–ahead electricity market prices: The Spanish case," Energy Economics, Elsevier, vol. 90(C).
    2. Zoran Gligorić & Svetlana Štrbac Savić & Aleksandra Grujić & Milanka Negovanović & Omer Musić, 2018. "Short-Term Electricity Price Forecasting Model Using Interval-Valued Autoregressive Process," Energies, MDPI, vol. 11(7), pages 1-17, July.
    3. Tao Chen & Qais Alsafasfeh & Hajir Pourbabak & Wencong Su, 2017. "The Next-Generation U.S. Retail Electricity Market with Customers and Prosumers—A Bibliographical Survey," Energies, MDPI, vol. 11(1), pages 1-17, December.
    4. Rodrigo A. de Marcos & Antonio Bello & Javier Reneses, 2019. "Short-Term Electricity Price Forecasting with a Composite Fundamental-Econometric Hybrid Methodology," Energies, MDPI, vol. 12(6), pages 1-15, March.
    5. Dumiter Florin Cornel & Turcaș Florin Marius & Boiţă Marius, 2023. "Oil Shock Impact Upon Energy Companies Investment Portfolios. Trends and Evolutions in the Energy Consumption Sector," Studia Universitatis „Vasile Goldis” Arad – Economics Series, Sciendo, vol. 33(1), pages 1-27, March.
    6. Simon Pezzutto & Gianluca Grilli & Stefano Zambotti & Stefan Dunjic, 2018. "Forecasting Electricity Market Price for End Users in EU28 until 2020—Main Factors of Influence," Energies, MDPI, vol. 11(6), pages 1-18, June.
    7. Luo, Shuman & Weng, Yang, 2019. "A two-stage supervised learning approach for electricity price forecasting by leveraging different data sources," Applied Energy, Elsevier, vol. 242(C), pages 1497-1512.
    8. Kun Li & Joseph D. Cursio & Yunchuan Sun, 2018. "Principal Component Analysis of Price Fluctuation in the Smart Grid Electricity Market," Sustainability, MDPI, vol. 10(11), pages 1-16, November.
    9. Bento, P.M.R. & Pombo, J.A.N. & Calado, M.R.A. & Mariano, S.J.P.S., 2018. "A bat optimized neural network and wavelet transform approach for short-term price forecasting," Applied Energy, Elsevier, vol. 210(C), pages 88-97.
    10. Huiru Zhao & Hao Lu & Bingkang Li & Xuejie Wang & Shiying Zhang & Yuwei Wang, 2020. "Stochastic Optimization of Microgrid Participating Day-Ahead Market Operation Strategy with Consideration of Energy Storage System and Demand Response," Energies, MDPI, vol. 13(5), pages 1-16, March.
    11. Pedro Bento & Hugo Nunes & José Pombo & Maria do Rosário Calado & Sílvio Mariano, 2019. "Daily Operation Optimization of a Hybrid Energy System Considering a Short-Term Electricity Price Forecast Scheme," Energies, MDPI, vol. 12(5), pages 1-25, March.
    12. Javier Contreras, 2017. "Forecasting Models of Electricity Prices," Energies, MDPI, vol. 10(2), pages 1-2, January.

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