A novel rule-based computational strategy for a fast and reliable energy management in isolated microgrids

The Energy Management System (EMS) is a strong need for achieving cost-effective, reliable, and pollution-free operation of microgrids operating in isolated areas. Commonly, the EMS approaches use optimization and rolling-horizon predictions. However, the inclusion of very short-term predictions increases the complexity of the optimization, compromising its ability and reliability to operate online. In response to this problem, this paper proposes a rule-based EMS (RB-EMS) which uses neither optimization nor prediction horizons. Instead, the RB-EMS evaluates historical data, and very short-term predictions to calculate the setpoints of distributed energy resources, and schedulable loads in a cost-effective, reliable, and sustainable manner. As a form of self-regulation, the RB-EMS introduces the Reliability Index to calculate the maximum state of charge of the energy storage system. Given the speed imposed by the very short-term predictions, the RB-EMS also introduces a start-up manager for diesel units that present a dead time in their enlistment. The isolated ESUSCON-HUATACONDO microgrid was used as a testbed for performance validation. The work shows a first scenario comparing the RB-EMS against a rolling horizon EMS based on MILP optimization. A second scenario shows the energy management performance with 1 minute predictions. The better results obtained, and the computational simplicity of the RB-EMS allows propose it to achieve faster and more reliable energy management in other isolated microgrids.