Optimization of Cascade-Resilient Electrical Infrastructures and its Validation by 
Power Flow Modeling

Abstract

Large-scale outages on real-world critical infrastructures, although infrequent, are increasingly 
disastrous to our society. In this article, we are primarily concerned with power transmission 
networks and we consider the problem of allocation of generation to distributors by rewiring links 
under the objectives of maximizing network resilience to cascading failure and minimizing investment 
costs. The combinatorial multiobjective optimization is carried out by a nondominated sorting binary 
differential evolution (NSBDE) algorithm. For each generators-distributors connection pattern considered 
in the NSBDE search, a computationally cheap, topological model of failure cascading in a complex network 
(named the Motter-Lai [ML] model) is used to simulate and quantify network resilience to cascading failures 
initiated by targeted attacks. The results on the 400 kV French power transmission network case study show 
that the proposed method allows us to identify optimal patterns of generators-distributors connection that 
improve cascading resilience at an acceptable cost. To verify the realistic character of the results obtained 
by the NSBDE with the embedded ML topological model, a more realistic but also more computationally expensive 
model of cascading failures is adopted, based on optimal power flow (namely, the ORNL-Pserc-Alaska) model). 
The consistent results between the two models provide impetus for the use of topological, complex network 
theory models for analysis and optimization of large infrastructures against cascading failure with the 
advantages of simplicity, scalability, and low computational cost.