Show simple item record

dc.contributor.advisorDamnjanovic, Ivanen_US
dc.creatorKim, Seoken_US
dc.date.accessioned2012-02-14T22:20:22Zen_US
dc.date.accessioned2012-02-16T16:16:24Z
dc.date.available2014-01-15T07:05:28Z
dc.date.created2011-12en_US
dc.date.issued2012-02-14en_US
dc.date.submittedDecember 2011en_US
dc.identifier.urihttp://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10533en_US
dc.description.abstractThis dissertation formulates and develops models and solution approaches for plug-in electric vehicle (PEV) charging station installation. The models are formulated in the form of bilevel programming and stochastic programming problems, while a meta-heuristic method, genetic algorithm, and Monte Carlo bounding techniques are used to solve the problems. Demand for PEVs is increasing with the growing concerns about environment pollution, energy resources, and the economy. However, battery capacity in PEVs is still limited and represents one of the key barriers to a more widespread adoption of PEVs. It is expected that drivers who have long-distance commutes hesitate to replace their internal combustion engine vehicles with PEVs due to range anxiety. To address this concern, PEV infrastructure can be developed to provide re-fully status when they are needed. This dissertation is primarily focused on the development of mathematical models that can be used to support decisions regarding a charging station location and installation problem. The major parts of developing the models included identification of the problem, development of mathematical models in the form of bilevel and stochastic programming problems, and development of a solution approach using a meta-heuristic method. PEV parking building problem was formulated as a bilevel programming problem in order to consider interaction between transportation flow and a manager decisions, while the charging station installation problem was formulated as a stochastic programming problem in order to consider uncertainty in parameters. In order to find the best-quality solution, a genetic algorithm method was used because the formulation problems are NP-hard. In addition, the Monte Carlo bounding method was used to solve the stochastic program with continuous distributions. Managerial implications and recommendations for PEV parking building developers and managers were suggested in terms of sensitivity analysis. First, in the planning stage, the developer of the PEV parking building should consider long-term changes in future traffic flow and locate a PEV parking building closer to the node with the highest destination trip rate. Second, to attract more parking users, the operator needs to consider the walkability of walking links.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoen_USen_US
dc.subjectplug-in electric vehicleen_US
dc.subjectparking buildingen_US
dc.subjectdecision-support modelen_US
dc.subjecttransportation networken_US
dc.subjectelectric power systemen_US
dc.subjectcharging stationen_US
dc.titleModels and Solution Approaches for Development and Installation of PEV Infrastructureen_US
dc.typeThesisen
thesis.degree.departmentCivil Engineeringen_US
thesis.degree.disciplineCivil Engineeringen_US
thesis.degree.grantorTexas A&M Universityen_US
thesis.degree.nameDoctor of Philosophyen_US
thesis.degree.levelDoctoralen_US
dc.contributor.committeeMemberAnderson, Stuart D.en_US
dc.contributor.committeeMemberQuadrifoglio, Lucaen_US
dc.contributor.committeeMemberKezunovic, Mladenen_US
dc.type.genrethesisen_US
dc.type.materialtexten_US
local.embargo.terms2014-01-15en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record