Supervisory Controller for Power Management of Microgrid Using Hybrid Technique

Abstract

Power management in Microgrid (MG) is a major issue while utilizing generation elements like photovoltaic, wind turbine, distributed generator, and battery bank with loads and non-linear loads. In this paper, power management is achieved through the deployment of the supervisory controller with hybrid power management algorithm named Artificial Neuro-Fuzzy Interference System (ANFIS) with Elephant Herd Optimization (EHO) algorithm. The objective of the new control technique is to keep a stable power flow among all renewable energy sources and the load, thereby ensuring that the battery power does not exceed the design limits. For an optimal control mapping of the nonlinear relation, ANFIS considers two input error values such as voltage and current value to obtain a minimized error value. The EHO is used to attain the learning function of the ANFIS. Here, the generated power, load demand power and State of Charge are taken as an objective function in EHO to compensate the load with the battery service during unavailability of generated power for meeting the load demand. The supervisory control structure achieves better power management in the MG with the help of the ANFIS–EHO. The proposed method is implemented in Simulink/MATLAB platform. The simulation and the experimental results are investigated with four test cases with some existing methods to analyze the efficiency of the proposed method.