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Exploiting Artificial Neural Networks for the Prediction of Ancillary Energy Market Prices

Author

Listed:
  • Christian Giovanelli

    (Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, FI-00076 Espoo, Finland)

  • Seppo Sierla

    (Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, FI-00076 Espoo, Finland)

  • Ryutaro Ichise

    (National Institute of Informatics, Tokyo 100-0003, Japan)

  • Valeriy Vyatkin

    (Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, FI-00076 Espoo, Finland
    SRT, Luleå University of Technology, Luleå 97187, Sweden
    International Research Laboratory of Computer Technologies, ITMO University, St. Petersburgh 197101, Russia)

Abstract

The increase of distributed energy resources in the smart grid calls for new ways to profitably exploit these resources, which can participate in day-ahead ancillary energy markets by providing flexibility. Higher profits are available for resource owners that are able to anticipate price peaks and hours of low prices or zero prices, as well as to control the resource in such a way that exploits the price fluctuations. Thus, this study presents a solution in which artificial neural networks are exploited to predict the day-ahead ancillary energy market prices. The study employs the frequency containment reserve for the normal operations market as a case study and presents the methodology utilized for the prediction of the case study ancillary market prices. The relevant data sources for predicting the market prices are identified, then the frequency containment reserve market prices are analyzed and compared with the spot market prices. In addition, the methodology describes the choices behind the definition of the model validation method and the performance evaluation coefficient utilized in the study. Moreover, the empirical processes for designing an artificial neural network model are presented. The performance of the artificial neural network model is evaluated in detail by means of several experiments, showing robustness and adaptiveness to the fast-changing price behaviors. Finally, the developed artificial neural network model is shown to have better performance than two state of the art models, support vector regression and ARIMA, respectively.

Suggested Citation

  • Christian Giovanelli & Seppo Sierla & Ryutaro Ichise & Valeriy Vyatkin, 2018. "Exploiting Artificial Neural Networks for the Prediction of Ancillary Energy Market Prices," Energies, MDPI, vol. 11(7), pages 1-22, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1906-:d:159229
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    References listed on IDEAS

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    Cited by:

    1. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    2. Kempitiya, Thimal & Sierla, Seppo & De Silva, Daswin & Yli-Ojanperä, Matti & Alahakoon, Damminda & Vyatkin, Valeriy, 2020. "An Artificial Intelligence framework for bidding optimization with uncertainty in multiple frequency reserve markets," Applied Energy, Elsevier, vol. 280(C).
    3. Harri Aaltonen & Seppo Sierla & Rakshith Subramanya & Valeriy Vyatkin, 2021. "A Simulation Environment for Training a Reinforcement Learning Agent Trading a Battery Storage," Energies, MDPI, vol. 14(17), pages 1-20, September.
    4. Niko Karhula & Seppo Sierla & Valeriy Vyatkin, 2021. "Validating the Real-Time Performance of Distributed Energy Resources Participating on Primary Frequency Reserves," Energies, MDPI, vol. 14(21), pages 1-19, October.
    5. Emil Kraft & Dogan Keles & Wolf Fichtner, 2020. "Modeling of frequency containment reserve prices with econometrics and artificial intelligence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1179-1197, December.
    6. Harri Aaltonen & Seppo Sierla & Ville Kyrki & Mahdi Pourakbari-Kasmaei & Valeriy Vyatkin, 2022. "Bidding a Battery on Electricity Markets and Minimizing Battery Aging Costs: A Reinforcement Learning Approach," Energies, MDPI, vol. 15(14), pages 1-19, July.
    7. Rakshith Subramanya & Matti Yli-Ojanperä & Seppo Sierla & Taneli Hölttä & Jori Valtakari & Valeriy Vyatkin, 2021. "A Virtual Power Plant Solution for Aggregating Photovoltaic Systems and Other Distributed Energy Resources for Northern European Primary Frequency Reserves," Energies, MDPI, vol. 14(5), pages 1-23, February.
    8. Jiaqi Liu & Hongji Hu & Samson S. Yu & Hieu Trinh, 2023. "Virtual Power Plant with Renewable Energy Sources and Energy Storage Systems for Sustainable Power Grid-Formation, Control Techniques and Demand Response," Energies, MDPI, vol. 16(9), pages 1-28, April.
    9. Fraunholz, Christoph & Kraft, Emil & Keles, Dogan & Fichtner, Wolf, 2021. "Advanced price forecasting in agent-based electricity market simulation," Applied Energy, Elsevier, vol. 290(C).

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