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Energy trading strategy for storage-based renewable power plants

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  • Miseta, Tamás
  • Fodor, Attila
  • Vathy-Fogarassy, Ágnes

Abstract

Despite the continuous growth and the widespread support of renewable energy sources, solar and wind power plants pose new challenges for Transmission System Operators and Distribution System Operators. Their uncontrollability limits their applicability; therefore, to encourage their further growth, fundamental modifications are needed. The research presented in this paper focuses on the predictive control of storage-based renewable power plants, and suggests a new model for profit optimization. Profit optimization is based on electricity price prediction and effective trading strategies that match the projected electricity prices. For the electricity price prediction, a recurrent Long Short-Term Memory neural network was developed and fine-tuned. For the optimization of the electricity trading, two trading strategies, namely an adaptive gradient-descent method and a differential evolution method were developed. Both optimization techniques were tested on mathematical models of most commercially available hybrid inverter systems and one year of historical data of electricity prices. As a result, a novel model predictive control workflow and sizing guide is proposed, which may significantly increase the profit generated by the system.

Suggested Citation

  • Miseta, Tamás & Fodor, Attila & Vathy-Fogarassy, Ágnes, 2022. "Energy trading strategy for storage-based renewable power plants," Energy, Elsevier, vol. 250(C).
    • Handle: RePEc:eee:energy:v:250:y:2022:i:c:s0360544222006910
    DOI: 10.1016/j.energy.2022.123788
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    Cited by:

    1. Scheben, Heike & Hufendiek, Kai, 2023. "Modelling power prices in markets with high shares of renewable energies and storages—The Norwegian example," Energy, Elsevier, vol. 267(C).
    2. Pourmoghadam, Peyman & Kasaeian, Alibakhsh, 2023. "Economic and energy evaluation of a solar multi-generation system powered by the parabolic trough collectors," Energy, Elsevier, vol. 262(PA).
    3. Paweł Pełka, 2023. "Analysis and Forecasting of Monthly Electricity Demand Time Series Using Pattern-Based Statistical Methods," Energies, MDPI, vol. 16(2), pages 1-22, January.

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