Quantum-accelerated post-event restoration through quantum surrogate absolute-value Lagrangian relaxation

Rapidly formulating post-disaster restoration strategies is crucial for ensuring power system security, yet increasing system scale and complexity pose significant challenges. Quantum computing, as a disruptive methodology, offers the potential to accelerate solutions to these complex problems. To tackle this difficulty and fully leverage the advantages of quantum computing, this paper proposes a quantum-accelerated approach for post-event restoration. First, the framework of the proposed approach based on photonic quantum computing is introduced. Towards quantum resource limitations and better scalability, post-disaster restoration model is established, and further decomposed into node-wise subproblems based on surrogate absolute value Lagrangian relaxation (SAVLR) method. Then, quantum-encoded models are further formulated for discrete subproblems. The quantum-SAVLR algorithm (Q-SAVLR) is proposed, with its proof and convergence theory analyzed. Finally, the effectiveness and performance of the proposed Q-SAVLR algorithm are validated through simulators and QBosoN photonic quantum computer achieving over 30% efficiency improvement on the IEEE 39-bus system compared to the Gurobi solver and heuristic algorithms, while ensuring accuracy and consistency.