Evolutionary Agent Based Microstorage Management for a Hybrid Power System

In the present paper, and agent based model is developed to balance the electric storage in a Hybrid Power Grid, where each house (end user) is modeled as an agent that can choose to store/release electricity at given times in order to tail the demand minimizing its own cost. The overall interaction of the agents causes the demand curve to change according to the decisions taken by the agents in the system. The system is solved by pursuing to reach a Nash-Equilibrium with the inclusion of a genetic algorithm that minimizes the cost of electricity for the overall Hybrid Power System, and hence the solution obtained is a best- response for all agents. The solution is a schedule of automated store/release electric policies for each time-step during the project length for each of the agents involved (end users) that minimizes the cost of electricity for all agents involved in the system.