Research on Adaptive Control Strategy for Black Start of Grid-forming Photovoltaic Battery Energy Storage System Based on Soft Actor Critic Algorithm

Abstract

During black-start restoration, grid-forming photovoltaic and battery energy storage systems are subject to weak grid support, frequent operating-condition transitions, and uncertain disturbances, which may lead to frequency deviations, active-power oscillations, and prolonged recovery. To address these challenges, this study develops an adaptive virtual synchronous generator control strategy based on the soft actor-critic algorithm to improve the coordination between transient stability and restoration speed. The online tuning of virtual inertia and damping coefficients is formulated as a continuousaction reinforcement learning problem. Parameter safety bounds, an action-smoothing constraint, and a restoration-time-related reward term are introduced to improve control continuity and balance oscillation suppression with recovery speed. Comparative simulations under load pickup, load shedding, and slave-unit paralleling conditions show that the proposed improved SAC-based strategy achieves smaller frequency deviation, lower active-power overshoot, and shorter recovery time than the fixed-parameter VSG, DDPG, and TD3 methods. The results suggest that adaptive tuning of VSG parameters can help balance disturbance suppression and recovery speed in grid-forming photovoltaic and battery energy storage systems during black-start restoration. Since the present validation is based on simulation studies, further investigations considering practical uncertainties, hardware constraints, and more complex operating conditions are needed before engineering application. The simulation results indicate that the proposed strategy can improve the transient regulation and restoration performance of grid-forming photovoltaic and battery energy storage systems during black-start restoration.