• • The proposed method achieves a minimum power angle deviation and maximum transient stability margin, as validated by simulations, directly enhancing grid stability under high renewable penetration.
• • By optimizing renewable output with capacity reserve, the method increases renewable energy utilization while reducing system power angle oscillations, addressing the intermittency bottleneck.
• • The virtual power angle model accurately captures the dynamic characteristics of renewable generators, enabling precise stability assessment under disturbances such as N-1 faults.
• • The neural network-based multi-objective optimization algorithm effectively solves the trade-off between power angle deviation and stability margin, providing a computational framework for real-time control.