Deployment of flywheel energy storage system and dstatcom for improving peneteration of renewable energy sources

The growing adoption of renewable energy sources (RES) has garnered global interest, driven by environmental concerns such as the imperative to reduce 𝐶𝑂2 and greenhouse gas (GHG) emissions are known contributors to global warming. However, integrating these RES into microgrids (MGs) can potentially give rise to numerous stability issues during operation. This research introduces a pioneering approach centered on the Nonlinear Autoregressive with Exogenous (NARX) Neural Network and grey box system identification for MG, aiming at predicting short-term stability during its operation. The effectiveness of the proposed technique is evaluated in a hypothetical case study involving a tied grid subjected to a three-phase fault. A static var compensator (DSTATCOM) is connected at the point of common coupling (PCC) on the distribution system's AC bus. Additionally, the method is scrutinized during the short-term transition to islanding mode operation of MG, incorporating a flywheel energy storage system (FESS). The FESS, recognized for its eco-friendly attributes, high energy density, and fast response, is strategically employed in this research. The FESS is connected to the DC bus of the DC/DC boost converter, effectively stabilizing the DC voltage level during the event. The developed prediction methodology was examined to enhance MG's stability, and the robustness was validated based on simulation using MATLAB software packages. The results are compared with traditional control strategies to validate the proposed method's effectiveness.