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

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 alternatives, Demand Response (DR) is presented as a tool improve balance between supply demand by adapting consumption available production. In this sense, work focuses on developing DR model that combines price incentive-based response models (P-B I-B) efficiently manage consumer with data from real San Juan—Argentina distribution network. addition, scheme proposed in time use relation consumers’ influence peak system. The schemes increase load factor displacement compared reference model. reinforcement learning improves short-term long-term search. Finally, description formulation where was implemented presented.