Active Power Optimization Allocation Strategy of Multiple Wind Turbines Considering the Improvement of Grid Connection Stability of Wind Farms

As wind power is a core component of sustainable energy systems, ensuring its stable grid integration is critical to advancing the 2030 Agenda for Sustainable Development, particularly in increasing the share of renewable energy, reducing carbon emissions, and promoting energy system sustainability. During the system frequency stability regulation, the active power output of wind farms undergoes continuous changes, which can affect the stable operation of the grid-connected system. To address this issue, this paper proposes an active power optimization allocation strategy of multiple wind turbines considering stability improvement. First, an equivalent impedance model of the wind farm grid-connected system was established, taking into account the differences in active power output and terminal impedance of wind turbines. Based on this model, the mechanisms by which different active power outputs and terminal impedances affect the system’s stability margin were analyzed, revealing the matching mechanism between wind turbine output and terminal impedance required to meet stability requirements; second, with the objective of maximizing the system damping ratio stability margin while balancing power constraints and wind turbine frequency regulation capability constraints, a multi-turbine frequency regulation power optimization model considering stability enhancement was established. The particle swarm optimization algorithm is employed to solve for the optimal frequency regulation power allocation scheme for each wind turbine. Finally, the effectiveness of the proposed strategy in improving the stability of the frequency regulation process in wind farms was verified through simulation examples. The proposed strategy enhances the reliability of wind power integration, reduces the risk of curtailment or disconnection of clean energy, and provides a technical tool for sustainable energy transition.