Comparing Network-Centric and Power Flow Models for the Optimal Allocation of Link Capacities in a Cascade-Resilient Power Transmission Network

Abstract

Abstract-In this study, we tackle the problem of searching for the most favourable pattern of link capacities allocation that makes a power transmission network resilient to cascading failures with limited investment costs. This problem is formulated within a combinatorial multi-objective optimization framework and tackled by evolutionary algorithms. Two different models of increasing complexity are used to simulate cascading failures in a network and to quantify its resilience: a complex network model (namely, the Motter-Lai (ML) model) and a more detailed and computationally demanding power flow model (namely, the ORNL-Pserc-Alaska (OPA) model). Both models are tested and compared on a case study involving the 400kV French power transmission network. The results show that cascade-resilient networks tend to have a non-linear capacity-load relation: in particular, heavily loaded components have smaller unoccupied portions of capacity, whereas lightly loaded links present larger unoccupied portions of capacity (which is in contrast with the linear capacity-load relation hypothesized in previous works of literature). Most importantly, the optimal solutions obtained using the ML and OPA models exhibit consistent characteristics in terms of phrase transitions in the Pareto fronts and link capacity allocation patterns. These results provide incentive for the use of computationally-cheap network-centric models for the optimization of cascade-resilient power network systems, given the advantages of their simplicity and scalability.Index Terms-power transmission network, cascading failures, complex network theory model, power flow model, capacity optimization, evolutionary algorithm

Multiobjective Optimal Power Flow Using a Semidefinite Programming-Based Model

“…For all simulations, MATLAB-based toolbox "YALMIP" with the solver "SeDuMi" 1.3 as an SDP solver with tolerance parameter eps 10 −8 is used. In the pursuance of making a better comparison, the nondominated sorting genetic algorithm II (NSGA-II) [35], [36] has been used to solve the MO-OPF problems in the same run environment.…”

The future of risk assessment

“…Most recently, Matisziw et al [123] conclude on the appropriateness of graph theory techniques for the assessment of electric network vulnerability by comparison to physical power flow models. This is confirmed in [66], where the problem of searching for the most favorable pattern of link capacities allocation that makes a power transmission network resilient to cascading failures with limited investment costs is formulated within a combinatorial multi-objective optimization framework and tackled by evolutionary algorithms. Two different models of increasing complexity are used to simulate cascading failures and to quantify resilience: a complex network model, and a more detailed and computationally demanding power flow model.…”

Vulnerability analysis of critical infrastructure under disruptions: An application to China Railway High-speed

“…The impact caused by a disruption is mainly in the failure of services provided by the infrastructure, thereby causing inconvenience to people’s lives and financial losses. Different critical infrastructure systems have different physical flow characteristics of operation, such as electric flow in power systems, 37,38 train services in railway transportation systems, and information flow in telecommunication systems. With regard to CRH, this study considers the flow of trains.…”