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Reinforcement Learning-Based Pricing and Incentive Strategy for Demand Response in Smart Grids

Author

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  • Eduardo J. Salazar

    (Doctoral Program in Electrical Engineering, Institute of Electrical Energy (IEE), National University of San Juan (UNSJ), National Scientific and Technical Research Council (CONICET), Libertador General San Martin Avenue 1109, San Juan 5400, Argentina)

  • Mauro Jurado

    (Doctoral Program in Electrical Engineering, Institute of Electrical Energy (IEE), National University of San Juan (UNSJ), National Scientific and Technical Research Council (CONICET), Libertador General San Martin Avenue 1109, San Juan 5400, Argentina)

  • Mauricio E. Samper

    (Doctoral Program in Electrical Engineering, Institute of Electrical Energy (IEE), National University of San Juan (UNSJ), National Scientific and Technical Research Council (CONICET), Libertador General San Martin Avenue 1109, San Juan 5400, Argentina)

Abstract

International agreements support the modernization of electricity networks and renewable energy resources (RES). However, these RES affect market prices due to resource variability (e.g., solar). Among the alternatives, Demand Response (DR) is presented as a tool to improve the balance between electricity supply and demand by adapting consumption to available production. In this sense, this work focuses on developing a DR model that combines price and incentive-based demand response models (P-B and I-B) to efficiently manage consumer demand with data from a real San Juan—Argentina distribution network. In addition, a price scheme is proposed in real time and by the time of use in relation to the consumers’ influence in the peak demand of the system. The proposed schemes increase load factor and improve demand displacement compared to a demand response reference model. In addition, the proposed reinforcement learning model improves short-term and long-term price search. Finally, a description and formulation of the market where the work was implemented is presented.

Suggested Citation

  • Eduardo J. Salazar & Mauro Jurado & Mauricio E. Samper, 2023. "Reinforcement Learning-Based Pricing and Incentive Strategy for Demand Response in Smart Grids," Energies, MDPI, vol. 16(3), pages 1-33, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1466-:d:1055062
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    References listed on IDEAS

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    Cited by:

    1. António Gomes Martins & Luís Pires Neves & José Luís Sousa, 2023. "Electricity Demand Side Management," Energies, MDPI, vol. 16(16), pages 1-3, August.
    2. Mauro Jurado & Eduardo Salazar & Mauricio Samper & Rodolfo Rosés & Diego Ojeda Esteybar, 2023. "Day-Ahead Operational Planning for DisCos Based on Demand Response Flexibility and Volt/Var Control," Energies, MDPI, vol. 16(20), pages 1-20, October.

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