Computational analysis of network survivability with application to power systems

AbstractThe operability of society’s critical infrastructures depends on the availability of electric power. Adverse events (natural disasters, intelligent adversary, etc.) occur rarely, but power system failure under such conditions has typically devastating effects on the economy and lives. A key factor in the system’s ability to withstand massive sudden damage caused by adverse events is its topology: the number of system elements that generate and demand power and the connections between them. The topology factor can be quantified by analyzing the impact of all possible combinations of unrecoverable faults (fault scenarios) on the availability and connectivity of system elements. As the number of possible fault scenarios grows as 2M with increasing number M of system elements, such an analysis becomes a computational challenge for large-scale systems. The paper discusses possibilities of reducing the computational complexity of the problem

Designing Power System Topologies of Enhanced Survivability

Survivability, or the ability to deliver service in spite of multiple simultaneous faults caused by natural or hostile disruptions, is a desirable feature of any complex system. For some systems such as the integrated power system in an all-electric ship, the ability to withstand massive sudden damage is of vital importance. Although many factors contribute to power system survivability, a key factor is its topology -the number of generators and the connections between generators, between loads, and generators with loads. Previously, we developed a basic mathematical framework and computational tools for analyzing the topological survivability of power systems with multiple generators and a single load. This paper considers a case of multiple generators and multiple loads with application to the topology of a notional medium voltage DC shipboard power system. Possible improvements are suggested. Nomenclature m = number of faulty elements M = total number of system elements N = total number of fault scenarios N(m) = number of fault scenarios with a given m k! = factorial S, R, F = numbers of "no-response", reconfiguration, and complete failure scenarios P = probability of the fault scenarios of a given type (S, R, or F

Floridian high-voltage power-grid network partitioning and cluster optimization using simulated annealing

Many partitioning methods may be used to partition a network into smaller clusters while minimizing the number of cuts needed. However, other considerations must also be taken into account when a network represents a real system such as a power grid. In this paper we use a simulated annealing Monte Carlo (MC) method to optimize initial clusters on the Florida high-voltage power-grid network that were formed by associating each load with its "closest" generator. The clusters are optimized to maximize internal connectivity within the individual clusters and minimize the power deficiency or surplus that clusters may otherwise have.Comment: 9 pages, 3 figures, University of Georgia 24th Annual CSP Worksho

Spectral matrix methods for partitioning power grids: Applications to the Italian and Floridian high-voltage networks

Intentional islanding is used to limit cascading power failures by isolating highly connected "islands" with local generating capacity. To efficiently isolate an island, one should break as few power lines as possible. This is a graph partitioning problem, and here we give preliminary results on islanding of the Italian and Floridian high-voltage grids by spectral matrix methods.Comment: 4 pages, 2 figures