This paper introduces a distributed sensitivity-conditioning approach for bilevel optimization in networked microgrids. The proposed method enhances the coordination between subsystems by embedding sensitivity-based predictive terms into the dynamic updates, thereby improving convergence stability without requiring strict time-scale separation. Unlike conventional singular perturbation techniques, the sensitivity-conditioning formulation enables faster and more robust convergence of the distributed dynamics under heterogeneous subsystem speeds. The approach is applied to a networked microgrid scenario where local agents perform decentralized optimization considering both internal generation and energy exchange with neighboring microgrids. Simulation results demonstrate that the proposed algorithm achieves efficient coordination, reduces convergence time, and maintains stability under diverse operating conditions. The results highlight the method’s potential as a scalable and computationally efficient alternative for real-time distributed energy management and bilevel control in power network applications.
Arevalo‐Castiblanco et al. (Wed,) studied this question.