Frequency Regulation Characteristics of Hybrid Microgrid Supported by Bio-Renewable Cogeneration with Modified Control Strategy

This paper explores the coordinated operation of an airport microgrid with a ship-based microgrid incorporating several sources of renewable energy along with various types of energy storage systems (ESS) and adjustable load elements. A FOPID controller is used in the paper for the frequency regulation, load demand, area control error, and power generation in different areas of load. The FOPID controller gains were optimized using the proposed Mountain Gazelle Optimisation (MGO) algorithm, which was chosen for its accuracy and fast convergence. Along with MGO, the performance of the controller is tested against a number of algorithms, such as the Artificial Electric Field Algorithm (AEFA), Swarm Optimization Algorithm (SO), and Chimp Optimization Algorithm (Choa). Performance indicators the system's resilience and efficiency were assessed using various metrics, including Integrated Square Error (ISE), Integrated Absolute Time Error (IATE), Integrated Absolute Error (IAE), and Integrated Squared Time Error (ISTE). Simulation results confirm the stability and reliability of the systems in the presence of dynamic load demand variation, thus proving its capability as an efficient microgrid. This comparative study shows that MGO is more effective than any other algorithm in managing uncertainties in renewable energy sources and maximizing performance in a system.