Stabilizing Renewable‐Rich Microgrids and Avoiding Load Shedding by Minimum Size of Energy Storages as Virtual Inertia With Optimum Droop Coefficients: A Realistic Case of Broome City

ABSTRACT This paper proposes a novel method to tackle the growing problem of system instability in microgrids, which is brought on by the widespread adoption of renewable energy sources (RESs) and distributed generators (DGs). Connecting RESs and DGs to microgrids through power electronic interfaces leads to a decrease and fluctuation in the system's inertia. This reduction in inertia leads to increased uncertainty and instability in the system, necessitating either load shedding or the curtailment of renewable generation, which are undesirable for both utilities and customers. To address these challenges, this paper provides a novel approach for enhancing inertia in microgrids to avoid any instability or load shedding. This approach introduces a multi‐objective optimization algorithm based on the non‐dominated sorting genetic algorithm II (NSGA‐II), implemented in a co‐simulation platform combining MATLAB and DIgSILENT PowerFactory. The algorithm simultaneously determines the minimum required capacity of battery energy storage systems (BESSs) acting as virtual inertia and their optimum droop coefficients to stabilize the grid and prevent load shedding. The proposed approach is formulated within the context of a multi‐objective optimization algorithm, by utilizing the NSGA‐II in an integrated DIgSILENT and MATLAB framework. The simulation results show the positive results of the proposed approach in stabilizing the microgrid of Broome city and avoiding any load shedding. The method is validated under three critical scenarios: a 0.97 MW step load increase, a cloud event reducing PV output by 49%, and a synchronous generator outage. Results show that the optimized BESS configuration successfully maintains frequency stability and avoids any load shedding. The minimum sizing of BESSs and their optimum droop coefficient are obtained for different scenarios including step load change, cloud event, and synchronous generator outage. Compared to conventional approaches, the proposed method significantly reduces the required BESS capacity while ensuring compliance with frequency nadir and RoCoF constraints. This approach provides a cost‐effective and scalable decision‐making tool for microgrid operators to enhance system resilience and customer satisfaction. The outcome of this research is a critical decision‐making tool for the microgrid owner to cost‐effectively decide the virtual inertia sizing and their parameters for stabilization of microgrid and to improve customer satisfaction by avoiding load shedding. This contribution bridges the gap between academic research and field‐level implementation, offering a scalable and adaptable strategy for future renewable‐rich microgrids.