Multi-agent based energy community cost optimization considering high electric vehicles penetration

This paper proposes a multi-agent based solution to minimize the energy cost of an energy community, in dayahead, that includes high penetration of electric vehicles. The proposed approach employs a structure of agents, including a central coordinator and energy management agents. To minimize the energy cost and to optimize the energy balance of the energy community, the proposed approach considers the energy demand and supply (i.e., photovoltaic generation), the battery storage systems' charge and discharge actions, and the charging and discharging schedules of electric vehicles, including the possibility of vehicles to charge and discharge energy in public charging stations. The optimization problem is formulated as a mixed-integer linear programming problem, which is solved by an open-source solver and compared with the use of a commercial solver. The simulation results show that the proposed day-ahead approach can significantly reduce the cost of the energy community while ensuring a reliable and stable operation. Comparing the proposed solution with a centralized implementation, it is possible to significantly reduce the optimization time from hours to few seconds. Overall, the proposed multi-agent based solution provides a promising solution for the optimization of energy communities with high penetration of electric vehicles.