PSO-Based Supervisory Adaptive Controller for BESS-VSG Frequency Regulation Under High PV Penetration

High photovoltaic (PV) penetration challenges grid frequency stability due to reduced system inertia. Virtual Synchronous Generators (VSGs), particularly when paired with Battery Energy Storage Systems (BESSs), can mitigate this by emulating synchronous machine dynamics. This study focuses on improving frequency response during PV power reductions through the adaptive tuning of an extensive set of VSG parameters. A double-phase Supervisory Controller is developed: in the first phase, Particle Swarm Optimization (PSO) computes multiple sets of optimal VSG parameters for various PV curtailment and load demand change scenarios; in the second phase, the system determines the most appropriate parameters based on current operating conditions to minimize frequency deviations, using the first phase as a foundation for adaptive decision making. The proposed Supervisory Controller reduced the Integral of the Absolute Error (IAE) of 151.55% in the case of a 65% irradiance drop. At 55%, the IAE decreased from 0.4605 to 0.2227, and at 25% from 0.0791 to 0.0546. In the low-disturbance scenario at a 25% drop, the IAE was maintained below 0.06. Supervisory Controller performance led to a reduced settling time and improved frequency recovery. These results demonstrate that the Supervisory Controller improves frequency regulation in both mild and severe irradiance reduction events.