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 have posed many challenges, particularly in terms of system coordination, reliability, and scalability. Among them, start-up is one of the most challenging, as the activation of one device often depends on others. In such a scenario, traditional pre-configured start-up methods become impractical and inflexible. In this context, this paper proposes a resilient, optimization-based framework for the start-up of networks populated with power electronic systems. To address this challenge, a linear programming optimization methodology is proposed to determine the sequential activation of devices based on system topology, available control modes (e.g., bus-forming 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 start-up 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 validate the proposed framework, four use cases are evaluated: i) grid-based activation using AC/DC converters, ii) energy storage-initiated start-up with bus-forming capability, iii) a faulted converter case that triggers re-optimization, iv) a fault occurs in a partially started system considering the worst case impact. Results show that the framework can dynamically adapt to changing conditions, accommodate new converter capabilities, and maintain reliable start-up 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.