SFOA-Optimized Fractional-Order Super-Twisting Sliding Mode Control for Sustainable Operation of a Wind–PV–ESS Microgrid Supplying a Fast EV Charging Station

Renewable-powered electric vehicle charging can support sustainable transport electrification by coupling low-carbon electricity generation with charging demand. Autonomous microgrids (MGs) combining wind, solar, and energy storage offer a pathway to this integration, including at locations with limited grid access. However, it can be challenging to monitor and manage the energy of standalone microgrids because they are time-varying and nonlinear. Solar and wind energy were the main sources of power for the microgrid. A microgrid’s primary load is thought to be an electric vehicle charging station (EVCS). The EVCS can charge quickly and uses a lot of power. Additionally, an energy storage system (ESS) is incorporated into the microgrid. This research evaluates a fractional-order-super-twisting sliding mode controller (FOSTSMC) for DC-bus regulation and ESS-assisted power balancing to support reliable renewable-powered fast EV charging. The FOSTSMC scheme includes three key parameters that are optimally tuned using the starfish optimization algorithm (SFOA). Regarding changes in wind velocity and solar irradiance, the response of the FOSTSMC was contrasted to that of a conventional proportional-integral (PI), super-twisting sliding mode controller (STSMC), and the fractional-order-PI (FOPI) regulators. MATLAB/Simulink (R2023a version 9.14) was used to simulate and model the MG. The findings show that the introduced FOSTSMC enhanced the MG’s transient response when compared to the other controllers. The proposed optimal FOSTSMC provides an improvement in the peak overshoot of 41.8% and 47.6% in the settling times over the best values of the other controllers. Moreover, simulation-based evaluation using NASA POWER weather profiles for solar irradiance and wind speed are applied to the proposed system to validate energy management effectiveness. Despite changes in wind velocity, solar intensity, and other parameters, the EVCS charging process and the DC-bus voltage tracked the set point with the least amount of disruption. To prove the effectiveness of SFOA, it is compared to particle swarm optimization (PSO).