Implementation of various control methods for the efficient energy management in hybrid microgrid system

Abstract

A hybrid microgrid is an energy system composed of multiple power sources such as photovoltaic panels, wind turbines, fossil-fuel generators, converters, battery storage systems, and an energy management system that guarantees stability and balance of the entire system. According to the system operation and the meteorological conditions, the microgrid can be linked to the electrical grid or work as a standalone system. The battery storage system is the key element of the microgrid and is integrated to maintain the load demand and better energy management using various controllers. In this paper, we presented an overview of energy management and control of the hybrid microgrid by proposing the implementation of the most cited control methods such as artificial neural network, fuzzy logic, sliding mode controller, and proportional integral derivative. Further, we addressed the solar power maximization using maximum power point tracking based on a developed artificial neural controller and compared it with the other controllers. Finally, implementing the proposed four controllers for the developed hybrid microgrid offers to find the best control strategy with multiple aspects and implications. Matlab/Simulink software was used to design a hybrid microgrid with two buses, AC Bus and DC Bus, to supply the power and fulfill the investigated load demands. The simulation is carried out for complicated system operation scenarios with multiple changes in system variables such as weather conditions and load demand to test the robustness of each controller. The simulation results and comparison show the performance of each control method in terms of voltage control, frequency stabilization and energy saving.

Keywords

• Microgrid
• Energy management system
• Artificial neural network
• Fuzzy logic
• Sliding mode controller
• Maximum power point tracking