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Price Forecasting of Electricity Markets in the Presence of a High Penetration of Wind Power Generators

Author

Listed:
  • Saber Talari

    (C-MAST, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilhã, Portugal)

  • Miadreza Shafie-khah

    (C-MAST, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilhã, Portugal)

  • Gerardo J. Osório

    (C-MAST, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilhã, Portugal)

  • Fei Wang

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China
    Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA)

  • Alireza Heidari

    (Australian Energy Research Institute (AERI), School of Electrical Engineering and Telecommunications, The University of New South Wales (UNSW), Sydney, NSW 2052, Australia)

  • João P. S. Catalão

    (C-MAST, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilhã, Portugal
    INESC TEC, Faculty of Engineering, University of Porto, R. Dr. Roberto Frias, 4200-465 Porto, Portugal
    INESC-ID, Instituto Superior Técnico, University of Lisbon, Av. Rovisco Pais, 1, 1049-001 Lisbon, Portugal)

Abstract

Price forecasting plays a vital role in the day-ahead markets. Once sellers and buyers access an accurate price forecasting, managing the economic risk can be conducted appropriately through offering or bidding suitable prices. In networks with high wind power penetration, the electricity price is influenced by wind energy; therefore, price forecasting can be more complicated. This paper proposes a novel hybrid approach for price forecasting of day-ahead markets, with high penetration of wind generators based on Wavelet transform, bivariate Auto-Regressive Integrated Moving Average (ARIMA) method and Radial Basis Function Neural Network (RBFN). To this end, a weighted time series for wind dominated power systems is calculated and added to a bivariate ARIMA model along with the price time series. Moreover, RBFN is applied as a tool to correct the estimation error, and particle swarm optimization (PSO) is used to optimize the structure and adapt the RBFN to the particular training set. This method is evaluated on the Spanish electricity market, which shows the efficiency of this approach. This method has less error compared with other methods especially when it considers the effects of large-scale wind generators.

Suggested Citation

  • Saber Talari & Miadreza Shafie-khah & Gerardo J. Osório & Fei Wang & Alireza Heidari & João P. S. Catalão, 2017. "Price Forecasting of Electricity Markets in the Presence of a High Penetration of Wind Power Generators," Sustainability, MDPI, vol. 9(11), pages 1-13, November.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:11:p:2065-:d:118284
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    References listed on IDEAS

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    Cited by:

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    2. Wei Sun & Ming Duan, 2019. "Analysis and Forecasting of the Carbon Price in China’s Regional Carbon Markets Based on Fast Ensemble Empirical Mode Decomposition, Phase Space Reconstruction, and an Improved Extreme Learning Machin," Energies, MDPI, vol. 12(2), pages 1-27, January.

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