Coordinated Multilayer Control for Energy Management of Grid-Connected AC Microgrids

This paper proposes a coordinated multilayer control strategy for the energy management (EM) of grid-connected ac microgrids. The strategy predicts the customer's load demand and the photovoltaics (PV) power generation for a day-ahead EM. It utilizes the PV power generations and the bidirectional energy transactions from electric vehicles and battery storage to provide a combined response for load support. The system also predicts any uncertainties in customers demand and power generations and implements day-ahead precautionary measures to tackle that uncertainty. Two different prediction strategies are used, autoregressive moving average and artificial neural networks, and their performances for a day-ahead EM are investigated. The reference power from the tertiary layer EM is sent to the local controllers for power regulation at the inverter level. Additionally, the varying power output reference obtained from a day-ahead EM is classified into the slow, medium, and fast variations. The performance of the local controller employed in the interfacing grid-connected three-phase inverter is tested during the above-mentioned power reference variations. The total harmonic distortion incorporating a moving window technique is calculated for the ac output current during each class of power variations over a day.