Coordinated PI-based frequency deviation control of isolated hybrid microgrid: An online multi-agent tuning approach via reinforcement learning

Abstract

Numerous remote area applications welcome standalone renewable energy power generation systems or isolated microgrids (MGs). Due to the nature of solar and wind energy, the frequency deviation control (FDC) in hybrid MGs has become more complicated and critical than the conventional grid for power quality purposes. By using a coordination control strategy between a double-layered capacitor and a fuel cell, our mission here is to design a FDC system based on the PI controller which is tuned by an artificial neural network (ANN) in a multi-agent structure. To achieve this aim, a reinforcement learning technique is applied to train the ANN-based tuners. The performance of the proposed FDC system has been verified under different conditions by using real data to demonstrate the stability and robustness of the proposed controller.