A Lean Simulator for Recoverable Robustness Assessment of Power Distribution Networks

The resilience of power distribution networks (PDNs) is critical for maintaining the stability and reliability of electrical power systems, especially during disruptive events. Traditional methods to enhance network resilience typically focus on robust infrastructure design and redundant capacity, but these approaches may not fully address the dynamic nature of large-scale disruptions. This paper proposes a novel, simulation-driven approach to improve operational responses by focusing on the concept of “recoverable robustness”, which is measured through the performance of a disrupted system after implementing simple repairs or recovery measures. In our paper, we argue that this concept, which originated in timetabling and logistics planning, applies well to the robustness of PDNs where optimal switch reconfiguration provides an easy to implement and fast recovery mechanism. Using the IEEE 69 network as a case study, we illustrate the process of simulating the recovery robustness of a standard radial PDN after a disruptive event. We integrate a lightweight Python simulator for PDNs with combinatorial optimization to restore connectivity based on varying priority levels. Our simulator not only assesses the recoverable robustness of a given PDN but also shows which power lines and hubs require special protection and maintenance due to their systemic importance.