A MODEL PREDICTIVE CONTROL STRATEGY FOR RENEWABLE ENERGY BASED AC MICROGRIDS TO IMPROVE POWER QUALITY

The development of Micro Grid (MG) is an advantageous option for integrating rapidly growing renewable energy. However, the stochastic nature of renewable energy and variable power demand has created many challenges like unstable voltage/frequency and complicated power management and interaction with utility grid. Traditional cascade control has major drawbacks like the control structure is complicated with multiple feedback loops and Pulse Width Modulation (PWM) modulation, which leads to slow dynamic response. Further, the tuning of the proportional Integral Differential (PID) parameters is time consuming, which makes the controller not easy to implement. As a result, traditional cascade control with its fast transient response and flexibility to accommodate different constraints has huge potential in MG applications. This project was implemented by novel model predictive control strategy without involving any PID regulators for practical renewable energy based AC MGs. By controlling the bidirectional Buck-Boost converters of the Battery Energy Storage System (BESS) based on the Model Predictive Power Control (MPPC) algorithm, the fluctuating output from the renewable energy sources can be smoothed, while stable DC-bus voltages can be maintained as the inverters inputs. Further, the parallel inverters can be controlled by using a combination of the Model Predictive Voltage Control (MPVC) scheme and the droop method to ensure stable AC voltage output and proper power sharing. The proposed methods have tested on MATLAB/Simulink platform.