A self-tuning load frequency control strategy for microgrids: Human brain emotional learning

Abstract Micro-grids consist of distributed power generation systems (DGs), distributed energy storage devices (DSs), and loads. Controlling these systems is more difficult than ordinary form of power systems since, in most of them, their energy is provided by renewable energies which have uncertain and varying nature. These fluctuations in the generated power might cause some problems in the function of conventional controllers. As a result, modern power systems require increased intelligence and flexibility in the control and optimization to ensure the capability of maintaining a generation-load balance, following serious disturbances. In this issue, the emotional controller which has a self-tuning nature is used to overcome these difficulties. This controller is based on emotional learning process of the human brain and can provide an appropriate control against changes in the system structure and occurrence of uncertainties. To evaluate the performance of the proposed controller, the results are compared with those obtained by conventional PI and fuzzy controllers, which is the latest research in the problem in hand. Simulation results show the effectiveness of the emotional controller.