Modelling Component Hardening Strategy for Power Network Resilience Optimization Under Uncertain Disruptions: A Distributionally Robust Approach

Designing a resilient power network is paramount for a stable power supply. The component hardening strategy has widespread applications in the resilience design of power networks to mitigate the impact of various natural disasters and malicious attacks. However, the uncertainties associated with natural disasters and deliberate attacks pose a significant challenge in the decision-making of the component hardening strategy. In this article, we put forth a distributionally robust optimization (DRO) model incorporating the component hardening strategy to minimize the impact of uncertain disruptions. The decision-dependent ambiguity set of the DRO model and the binary decision variables in the post-disruption stage greatly increase the complexity of solving the DRO model. A cutting plane algorithm is customized to solve the DRO model. We prove that the proposed cutting plane algorithm can exponentially converge for the master problem and linearly converge for the separate subproblem. The model is also extended to a general form to cover the existing post-disruption recovery models. The results from the case study of a 35 kV power network show that the proposed DRO model yields a high quality component hardening strategy to enhance the resilience of the power network, comparing with the existing stochastic programming models. The convergence speed of the algorithm is also examined, and it is demonstrated to be effective.