Managing Energy in a Network

A computer-implemented method for managing a plurality of energy storages at a plurality of sites in a network, the method comprising:

• generating a first simulated environment of the network based on a first dataset including power consumption data of at least a subset of the plurality of energy storages over a predetermined amount of time;
• and
• training a first reinforcement learning system by performing the following operations iteratively until a termination condition is met:
• selecting an action from a set of feasible actions, wherein each action in the set of feasible actions is bounded by a set of constraints;
• calculating a reward of the selected action based on the generated first simulated environment of the network;
• and
• training the first reinforcement learning system to maximize reward for a given state of the network, based on the calculated reward for the selected action.

A system configured to manage a plurality of energy storages at a plurality of sites in a network, the system comprising:

• processing circuitry, and
• memory operably coupled to the processing circuitry and storing computer readable instructions that, when executed by the processing circuitry, cause the system to:
• generate a first simulated environment of the network based on a first dataset including power consumption data of at least a subset of the plurality of energy storages over a predetermined amount of time;
• and
• train a first reinforcement learning system by performing the following operations iteratively until a termination condition is met:
• select an action from a set of feasible actions, wherein each action in the set of feasible actions is bounded by a set of constraints;
• calculate a reward of the selected action based on the generated first simulated environment of the network;
• and
• train the first reinforcement learning system to maximize reward for a given state of the network, based on the calculated reward for the selected action.