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

SALAZAR, EDUARDO; JURADO, MAURO; SAMPER, MAURICIO E.

energies

Multidisciplinary Digital Publishing Institute (MDPI)

Año: 2023 vol. 16

International agreements support the modernization of electricity networks and renewableenergy 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 balancebetween electricity supply and demand by adapting consumption to available production. In thissense, this work focuses on developing a DR model that combines price and incentive-based demandresponse models (P-B and I-B) to efficiently manage consumer demand with data from a real SanJuan—Argentina distribution network. In addition, a price scheme is proposed in real time and by thetime of use in relation to the consumers’ influence in the peak demand of the system. The proposedschemes increase load factor and improve demand displacement compared to a demand responsereference model. In addition, the proposed reinforcement learning model improves short-term andlong-term price search. Finally, a description and formulation of the market where the work wasimplemented is presented.