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The contribution of power generation from converter-dominated renewable energy sources (RES) has increased enormously. Consequently, the system inertia is decreasing, and it impacts the frequency of the system. With large-scale integration of power electronic inverter-based power generation from RES, inertia from energy storage devices would be unavoidable in future power grids. In this paper, the inertia emulator is formed with a supercapacitor (IE–SC) to improve inertia in a low inertia grid. To emulate the inertia in a low inertia grid, this paper proposes a fuzzy logic controller-based emulated inertia controller (FL-EIC) for an inverter attached to IE–SC. The proposed fuzzy logic controller estimates the inertial power required based on the frequency deviation and rate of change of frequency (ROCOF). The output of the fuzzy controller adds to the conventional emulated inertia control (EIC) technique to alter the load angle for the power electronic inverter of IE–SC. Specifically, the proposed FL-EIC achieves inertia emulation by proportionally linking the time derivative of the grid frequency and frequency deviation to active power references of IE–SC. A comparison of the conventional EIC and FL-EIC is carried out to prove the effectiveness of the proposed FL-EIC. Furthermore, real-time simulations with the help of the OPAL-RT real-time simulator (OP 5700) are presented to validate the advantage of the FL-EIC. [ABSTRACT FROM AUTHOR]

This paper proposes an intelligent battery management system (BMS) implementing two large Vanadium Redox Battery (VRB) flow batteries in a master-slave mode to provide grid-level energy storage for a wind-solar hybrid power system. The proposed BMS is formulated to effectively meet a predetermined power dispatch formulated based on forecasted wind and solar data while incorporating features like peak shaving and ramp rate limiting. It is compared to a single battery module operated system to showcase the advantages of the proposed intelligent dual battery module in terms of appreciable reduction in battery size and costs while exhibiting improved lifecycle performance. The battery size is optimized based on heuristic optimization algorithms and modelled in Matlab/Simulink environment. An intelligent fuzzy-based BMS is used to control the dual VRB model to ensure optimized power sharing between batteries. The simulations were carried out and an in-depth economic analysis conducted to analyze the costs and other financial metrics of the hybrid project. Results proved the advantages of the dual battery with the proposed BMS and fortify that the introduction of time-based tariffs and other incentives will further make investments in VRB highly attractive for renewable applications. [ABSTRACT FROM AUTHOR]