Quantum-enabled topological optimization of distributed energy storage for resilient black-start operations.

As modern power grids grow increasingly complex with the widespread deployment of renewable energy and distributed energy storage systems (ESS), ensuring robust and resilient black-start capabilities has become a critical challenge. Traditional black-start approaches, which typically rely on centralized hydro or diesel generators, are increasingly inadequate due to rising network complexity, the stochastic nature of renewables, and growing exposure to cyber-physical threats. To overcome these limitations, this study introduces a quantum-enhanced framework for dynamic network reconfiguration and topological optimization of ESS to support black-start restoration. The proposed method leverages quantum graph theory and quantum annealing to dynamically determine optimal ESS connectivity and energy redistribution pathways, enabling rapid grid recovery under diverse failure scenarios, including those involving cyber-physical disruptions. By integrating quantum annealing algorithms, the framework efficiently addresses the combinatorial complexity of large-scale ESS placement and dispatch, outperforming traditional heuristic and classical optimization techniques in both computational speed and solution quality. The approach is formulated through a comprehensive mathematical model that captures key interactions between network topology, energy flow dynamics, and black-start performance indicators such as restoration time, efficiency, and resilience. Simulation results on a 300-bus synthetic power grid with high levels of renewable penetration demonstrate that the proposed quantum-assisted strategy reduces restoration decision time by up to 50%, optimizes energy allocation, and significantly improves system robustness. These findings highlight the transformative potential of quantum computing in enabling intelligent, adaptive black-start planning, offering a powerful tool for enhancing the resilience of future energy systems.