Microgrid Multi-Level Control System for Photovoltaic-Wind Hybrid Generation System

Abstract

The concept of a microgrid is increasingly attracting interest among researchers and investors. This is because it offers a promising technology for utilizing distributed renewable energy generation resources, notably Photovoltaic (PV) and wind generation systems. However, the off-grid or on-grid utilization of a microgrid with PV and wind generation systems presents power quality challenges due to their intermittency in power outputs and voltage variations. This problem is mainly addressed within the converter section of the microgrid using Maximum Power Point Tracking (MPPT) algorithms and voltage regulation strategies using a Microgrid Control System (MCS). A majority of the existing MCSs still depict some inadequacies in their ability to optimize voltage regulation with intelligence while working for the non-linearities in the microgrid. This research aimed at developing a Microgrid Multi-level Control System (MMCS) for a PV-Wind hybrid microgrid system based on Genetic Algorithm-Adaptive Neuro-Fuzzy Inference System-Model Predictive Control-(GA-ANFIS-MPC). To achieve this, the PV-Wind hybrid microgrid model that incorporates a Battery Energy Storage System (BESS) was first created in MATLAB/SIMULINK. Two microgrid models have been developed: a scalable Simulink Case Study Model from the underlying mathematical equations and a nested voltage-current loop-based Transfer Function model. Next, the GA-ANFIS-MPC-based MCS was designed in two hierarchical levels. The first level is the GA-ANFIS primary controller, which has been used as an MPPT algorithm to optimize the converter outputs and regulate the microgrid output voltage amid power generation variations. The second level of control is the MPC secondary controller, which controls BESS's charging and discharging. In addition, the Proportional plus Integral plus Derivative (PID) regulator control method and the Search Space Restricted-Perturb and Observe (SSR-P&O) were developed to validate the performance of the GA-ANFIS-MPC controller using the simulation model built in MATLAB/SIMULINK. The results and performance obtained indicated that both the GA-ANFIS primary controller and the MPC secondary controller are superior to the SSR-P&O and PID in terms of reduced rise time, settling time, overshoot, and the ability to handle non-linearities in the microgrid. The GA-ANFIS primary controller recorded the best performance followed by the MPC secondary controller. The MPC controller, though with an increased computation time, was seen to have a better response than the GA-ANFIS controller in terms of reduced overshoot in voltage regulation. However, the GA-ANFIS controller has a better response than the MPC controller in terms of reduced rise time and settling time. The main contribution of this study is the designed GA-ANFIS-MPC based MCS that improves voltage responses and also charging and discharging of BESS in the PV-Wind Hybrid microgrid system. Consequently, this improves microgrids' performance in supplying power to targeted remote locations and local communities not connected to the main grid. It also acts as a stepping stone towards realizing the smart grid. Keywords - BESS, GA-ANFIS-MPC, Microgrid, Perturb and Observe (P&O), Photovoltaic, PV-Wind hybrid system.