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

Eduardo J. Salazar (), Mauro Jurado and Mauricio E. Samper
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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

Energies, 2023, vol. 16, issue 3, 1-33

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.

Keywords: price-based demand response; incentive-based demand response; reinforcement Q-learning; demand coincidence factor; replay memory exchange (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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