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Exponential Smoothing Approaches for Prediction in Real-Time Electricity Markets

Author

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  • Tryggvi Jónsson

    (Department of Applied Mathematics,Technical University of Denmark, Matematiktorvet 303, 2800 Kgs. Lyngby, Denmark
    ENFOR A/S, Lyngsø Allé 3, 2970 Hørsholm, Denmark)

  • Pierre Pinson

    (Department of Electrical Engineering, Technical University of Denmark, Elektrovej 325,2800 Kgs. Lyngby, Denmark)

  • Henrik Aa. Nielsen

    (ENFOR A/S, Lyngsø Allé 3, 2970 Hørsholm, Denmark)

  • Henrik Madsen

    (Department of Applied Mathematics,Technical University of Denmark, Matematiktorvet 303, 2800 Kgs. Lyngby, Denmark)

Abstract

The optimal design of offering strategies for wind power producers is commonly based on unconditional (and, hence, constant) expectation values for prices in real-time markets, directly defining their loss function in a stochastic optimization framework. This is why it may certainly be advantageous to account for the seasonal and dynamic behavior of such prices, hence translating to time-varying loss functions. With that objective in mind, forecasting approaches relying on simple models that accommodate the seasonal and dynamic nature of real-time prices are derived and analyzed. These are all based on the well-known Holt–Winters model with a daily seasonal cycle, either in its conventional form or conditioned upon exogenous variables, such as: (i) day-ahead price; (ii) system load; and (iii) wind power penetration. The superiority of the proposed approach over a number of common benchmarks is subsequently demonstrated through an empirical investigation for the Nord Pool, mimicking practical forecasting for a three-year period over 2008–2011.

Suggested Citation

  • Tryggvi Jónsson & Pierre Pinson & Henrik Aa. Nielsen & Henrik Madsen, 2014. "Exponential Smoothing Approaches for Prediction in Real-Time Electricity Markets," Energies, MDPI, vol. 7(6), pages 1-23, June.
  • Handle: RePEc:gam:jeners:v:7:y:2014:i:6:p:3710-3732:d:37102
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    References listed on IDEAS

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    Cited by:

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    2. Berrisch, Jonathan & Pappert, Sven & Ziel, Florian & Arsova, Antonia, 2023. "Modeling volatility and dependence of European carbon and energy prices," Finance Research Letters, Elsevier, vol. 52(C).
    3. Jannik Schütz Roungkvist & Peter Enevoldsen & George Xydis, 2020. "High-Resolution Electricity Spot Price Forecast for the Danish Power Market," Sustainability, MDPI, vol. 12(10), pages 1-19, May.
    4. Ping Jiang & Feng Liu & Yiliao Song, 2016. "A Hybrid Multi-Step Model for Forecasting Day-Ahead Electricity Price Based on Optimization, Fuzzy Logic and Model Selection," Energies, MDPI, vol. 9(8), pages 1-27, August.
    5. Yelena Vardanyan & Henrik Madsen, 2019. "Stochastic Bilevel Program for Optimal Coordinated Energy Trading of an EV Aggregator," Energies, MDPI, vol. 12(20), pages 1-18, October.
    6. Sylvia Mardiana & Ferdinand Saragih & Martani Huseini, 2020. "Forecasting Gasoline Demand in Indonesia Using Time Series," International Journal of Energy Economics and Policy, Econjournals, vol. 10(6), pages 132-145.
    7. Alexandre Lucas & Konstantinos Pegios & Evangelos Kotsakis & Dan Clarke, 2020. "Price Forecasting for the Balancing Energy Market Using Machine-Learning Regression," Energies, MDPI, vol. 13(20), pages 1-16, October.
    8. Jethro Browell, 2017. "Risk Constrained Trading Strategies for Stochastic Generation with a Single-Price Balancing Market," Papers 1708.02625, arXiv.org.
    9. Jethro Browell, 2018. "Risk Constrained Trading Strategies for Stochastic Generation with a Single-Price Balancing Market," Energies, MDPI, vol. 11(6), pages 1-17, May.

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