Real-time carbon footprint reduction controller

Abstract

Systems and methods are provided for optimizing energy consumption, flexible load shifting, and battery operation decisions simultaneously in real-time through Reinforcement Learning (RL). Examples include obtaining states of a system comprising a plurality of subsystems and receiving, by RL agents, rewards from a digital twin of the system, the rewards comprising a plurality of rewards each associated with a subsystem. The example also include determining actions, by the RL agents, based on the states and each of the rewards. Each RL agent is associated with a subsystem and assigns a weight to a reward corresponding to the associated subsystem that is greater than weights assigned to rewards of the other subsystems. The system can then be controlled according to the actions to transition the system to updated states.