A Resilient, Optimization-Based Framework for Starting Networks With Integrated Power Electronics

Complex modern energy systems with multiple converters, distributed energy resources, and dynamic control modes pose significant challenges, particularly in terms of system coordination, reliability, and scalability. Among them, startup is one of the most challenging, as the activation of one device often depends on others. In such a scenario, traditional preconfigured startup methods become impractical and inflexible. To address this issue, this article proposes a resilient, optimization-based framework for the startup of networks populated with power electronic systems (PESs). A linear programming-based optimization methodology is proposed to determine the sequential activation of devices based on system topology, available control modes (e.g., bus forming (BFM) or grid-following), and the presence of faults. The framework supports systems with shared buses and integrates converter-level information via resource integration controllers and a centralized resource management controller. Device startup is modeled through time-step-based formulations that reflect bus energization constraints, converter capabilities, and interdependencies between subsystems. The proposed solution is implemented and validated on a real-time controller hardware-in-the-loop platform. To demonstrate the framework's effectiveness, four use cases are evaluated: first, grid-based activation using AC-DC converters, second, energy storage-initiated startup with BFM capability, third, a faulted converter case that triggers reoptimization, and fourth, a fault occurring in a partially started system to evaluate worst-case impact. Results show that the framework can dynamically adapt to changing conditions, accommodate new converter capabilities, and maintain reliable startup even with failed devices. This approach enhances the flexibility and resiliency of PES-integrated systems and offers a scalable path forward for autonomous system activation in complex electrical networks.