Dynamic adjustment strategy for an integrated 'wind-solar-storage' power station based on digital twin

As the share of new energy generation increases, its intermittent and uncertain nature threatens the stability of power systems. This study introduces a dynamic scheduling approach for wind-solar storage-charging hybrid power stations utilizing digital twin technology. By constructing an accurate virtual model of physical entities, the approach enables real-time monitoring, simulation analysis, and intelligent optimal control of the power system, providing a new solution for improving power station operation efficiency and power grid stability. In this application scenario, the digital twin system integrates and analyzes the operation data of the power station and optimizes the node voltage control and peak shaving control strategies of the energy storage power station. Regarding node voltage regulation, the reactive power output is dynamically fine-tuned in line with energy storage's charge-discharge attributes, with the ideal charge-discharge power identified via the particle swarm optimization (PSO) algorithm and digital twin simulation capability. Simulation results show that the node voltage fluctuation of the traditional strategy is ± 5% with 8 over-limits, while the proposed strategy reduces it to ± 2% with 1 over-limit. In peak shaving control, through digital twin simulation and PSO for energy storage capacity configuration, the optimal charging and discharging plan is determined, reducing the grid peak-valley difference from 30% of the traditional strategy to 15%. In conclusion, this strategy has significant advantages in improving the efficiency of new energy power stations and power grid stability, providing technical support for building a clean and efficient energy system.