This work presents a novel approach to wide-area damping control (WADC) for clustered microgrids, addressing inter-area oscillations and enhancing system stability.
This paper offers a detailed review of the literature regarding three important aspects: (i) Power-quality issues generated in MGs both in islanded mode and grid-connected mode; (ii) Optimization techniques used in the MGs to achieve the optimal operating conditions of the Energy.
Microgrids (MGs) provide a promising solution by enabling localized control over energy generation, storage, and distribution. This paper presents a novel reinforcement learning (RL)-based methodology for optimizing microgrid energy management.
In centralized approach, the microgrid central controller (MGCC) is mainly responsible for the maximization of the microgrid value and optinization of its operation, and the MGCC determines the amount of power that the microgrid should import or export from the upstream distribution.
The framework adopts VSGs with dynamically adjustable inertia, combined with adaptive Q–V droop control, to coordinately regulate frequency and voltage while compensating for communication delays using predictive feedback and event-triggered mechanisms.