4 research outputs found
Recommended from our members
Genetic Algorithms for Agent-Based Infrastructure Interdependency Modeling and Analysis
Today’s society relies greatly upon an array of complex national and international infrastructure networks such as transportation, electric power, telecommunication, and financial networks. This paper describes initial research combining agent-based infrastructure modeling software and genetic algorithms (GAs) to help optimize infrastructure protection and restoration decisions. This research proposes to apply GAs to the problem of infrastructure modeling and analysis in order to determine the optimum assets to restore or protect from attack or other disaster. This research is just commencing and therefore the focus of this paper is the integration of a GA optimization method with a simulation through the simulation’s agents
Recommended from our members
Toward Developing Genetic Algorithms to Aid in Critical Infrastructure Modeling
Today’s society relies upon an array of complex national and international infrastructure networks such as transportation, telecommunication, financial and energy. Understanding these interdependencies is necessary in order to protect our critical infrastructure. The Critical Infrastructure Modeling System, CIMS©, examines the interrelationships between infrastructure networks. CIMS© development is sponsored by the National Security Division at the Idaho National Laboratory (INL) in its ongoing mission for providing critical infrastructure protection and preparedness. A genetic algorithm (GA) is an optimization technique based on Darwin’s theory of evolution. A GA can be coupled with CIMS© to search for optimum ways to protect infrastructure assets. This includes identifying optimum assets to enforce or protect, testing the addition of or change to infrastructure before implementation, or finding the optimum response to an emergency for response planning. This paper describes the addition of a GA to infrastructure modeling for infrastructure planning. It first introduces the CIMS© infrastructure modeling software used as the modeling engine to support the GA. Next, the GA techniques and parameters are defined. Then a test scenario illustrates the integration with CIMS© and the preliminary results
Impacts of Climate Change on the Evolution of the Electrical Grid
Maintaining interdependent infrastructures exposed to a changing climate requires understanding 1) the local impact on power assets; 2) how the infrastructure will evolve as the demand for infrastructure changes location and volume and; 3) what vulnerabilities are introduced by these changing infrastructure topologies. This dissertation attempts to develop a methodology that will a) downscale the climate direct effect on the infrastructure; b) allow population to redistribute in response to increasing extreme events that will increase under climate impacts; and c) project new distributions of electricity demand in the mid-21st century.
The research was structured in three parts. The first used downscaling techniques to scale regional gridded atmospheric processes to measurements of local extreme events. These techniques illustrate the ability to move reasonably from regional to local effects. The second chapter explored how people migrated in response to the extreme events for which climate change will increase the frequency and intensity. The third chapter translated downscaled climate impacts and granular population movements into a national map of electricity demand.
The results of this research illustrates the feasibility of the three part approach to address possible future infrastructure vulnerabilities under varying policy options and technology assumptions. This methodology can be an important tool for increasing the robustness of the nation’s infrastructure