Power management in hybrid ANFIS PID based AC–DC microgrids with EHO based cost optimized droop control strategy

Abstract

One of the most critical operations aspects is power management strategies for hybrid AC/DC microgrids. This work presented power management in hybrid AC–DC microgrids with a droop control strategy. At first, photovoltaic, Wind, and battery are used as the power sources, which supply the power with uncertainties. The AC and DC microgrids are controlled by an Adaptive neuro-fuzzy inference system (ANFIS) controller and Proportional Integral Derivative (PID) controller. Simultaneously we calculate the running cost for photovoltaic, Wind, and Battery. Moreover, an optimizer based on the elephant herding optimization algorithm is formulated to reduce the cost price. This method utilizes two stages like clan updating operator and separating operator. This cost value is used to calculate the Droop Coefficients in Droop Control Strategy. The autonomous droop control strategy is utilized in the interlinking converter to share the load between AC and DC. This proposed concept is implemented in the MATLAB tool, and the performance is taken in terms of voltage, power, and current for PV and wind, DC link voltage and load current. The bidirectional ac/dc interlinking converter power flow was subsequently changed from 2.2k W to −2k W. The effectiveness of the power dispatch mode under uniform control has been verified.

Keywords

• Photovoltaic cell
• Wind turbine
• Battery
• Microgrid
• Droop control method
• Elephant Herding Optimization