Voltage Control and Reactive Power Support Optimization Using (SA-STO) Algorithm in Virtual Power Plants (VPPs)

The complexity of voltage control and reactive power support has risen due to the increasing integration of renewable energy sources in Virtual Power Plants (VPPs). Therefore, effective management is vital for grid stability and energy optimization. To improve voltage control and reactive power management in VPPs, this research introduces a new method that uses the SA-STO algorithm. The proposed Self-Adaptive Siberian Tiger Optimization (SA-STO) algorithm continuously modifies its search parameters according to the power grid's operating circumstances to achieve adaptive and efficient optimization. The approach increases the stability of voltage profiles, speeds up convergence, and prevents local optima by using the self-adaptive mechanism. Many constraints are considered to optimize for both efficient energy use and grid dependability, such as reactive power compensation, voltage deviation reduction, and power flow balancing. Key results include a reduction in average voltage deviation by ~35%, a decrease in power loss from 12 kW to 1–2 kW, and convergence within ~100 iterations—outperforming PI, PID, and static FOPID controllers. Compared to traditional optimization approaches, the SA-STO algorithm achieves better simulation results for voltage regulation accuracy, computational resilience, and reactive power support efficiency. The results show that it might be an effective optimization tool for making current VPPs more efficient, leading to a more stable and renewable energy grid.