A penetration of a large number of electric vehicles for charging their batteries in the grid can have a negative impact to the grid. To prevent a negative effect to the grid, the behavior of electric vehicles must be accurately modeled and their charging schedules must be coordinated. Therefore, it is necessary to determine where and how much charge is available in electric vehicles in the distribution system. In this thesis, a state transition algorithm is designed to determine a stochastic model of electric vehicles to simulate electric vehicle movement in an integrated traffic and power network. Dijkstra’s algorithm is used to determine the shortest distance between end-user residential and office areas. An uncoordinated and semi-coordinated charging technique are used to charge electric vehicles at different time intervals at different charging stations based on their driving patterns. Monte Carlo simulation is performed to analyze the effect of uncertainty in driving behavior. Results show that uncoordinated charging techniques generate new peaks in the load profile of each node in the distribution system and cause undervoltage problems in the power network. The semi-coordinated charging technique introduces a delay in the charging time to shift electric vehicle charging loads to off-peak times. Hence, with the semi-coordinated charging method, it is unnecessary to immediately upgrade the distribution network infrastructure to avoid network overloading.
","abstract_html":"<p>A penetration of a large number of electric vehicles for charging their batteries in the grid can have a negative impact to the grid. To prevent a negative effect to the grid, the behavior of electric vehicles must be accurately modeled and their charging schedules must be coordinated. Therefore, it is necessary to determine where and how much charge is available in electric vehicles in the distribution system. In this thesis, a state transition algorithm is designed to determine a stochastic model of electric vehicles to simulate electric vehicle movement in an integrated traffic and power network. Dijkstra’s algorithm is used to determine the shortest distance between end-user residential and office areas. An uncoordinated and semi-coordinated charging technique are used to charge electric vehicles at different time intervals at different charging stations based on their driving patterns. Monte Carlo simulation is performed to analyze the effect of uncertainty in driving behavior. Results show that uncoordinated charging techniques generate new peaks in the load profile of each node in the distribution system and cause undervoltage problems in the power network. The semi-coordinated charging technique introduces a delay in the charging time to shift electric vehicle charging loads to off-peak times. Hence, with the semi-coordinated charging method, it is unnecessary to immediately upgrade the distribution network infrastructure to avoid network overloading.</p>","abstract_has_math":false,"creators":["Shrestha, Sadhana"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Thesis - Open Access","degree_discipline":"Electrical Engineering and Computer Science","degree_department":null,"school":null,"contributors":["Timothy Hansen"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-01-01T08:00:00Z","date_published":"2016-01-01T08:00:00Z","updated_at":"2026-07-24T04:28:30Z","subjects":["Dijkstra's algorithm","electric vehicles","integrated traffic and power system","Monte Carlo simulation","semi-coordinated charging","state transition algorithm","Electrical and Computer Engineering"],"languages":["en"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://openprairie.sdstate.edu/etd/1047","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Timothy Hansen"]},{"key":"dc:creator","label":"Author","values":["Shrestha, Sadhana"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2016-08-22T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering and Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Dijkstra's algorithm","electric vehicles","integrated traffic and power system","Monte Carlo simulation","semi-coordinated charging","state transition algorithm","Electrical and Computer Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://openprairie.sdstate.edu/etd/1047"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["A penetration of a large number of electric vehicles for charging their batteries in the grid can have a negative impact to the grid. To prevent a negative effect to the grid, the behavior of electric vehicles must be accurately modeled and their charging schedules must be coordinated. Therefore, it is necessary to determine where and how much charge is available in electric vehicles in the distribution system. In this thesis, a state transition algorithm is designed to determine a stochastic model of electric vehicles to simulate electric vehicle movement in an integrated traffic and power network. Dijkstra’s algorithm is used to determine the shortest distance between end-user residential and office areas. An uncoordinated and semi-coordinated charging technique are used to charge electric vehicles at different time intervals at different charging stations based on their driving patterns. Monte Carlo simulation is performed to analyze the effect of uncertainty in driving behavior. Results show that uncoordinated charging techniques generate new peaks in the load profile of each node in the distribution system and cause undervoltage problems in the power network. The semi-coordinated charging technique introduces a delay in the charging time to shift electric vehicle charging loads to off-peak times. Hence, with the semi-coordinated charging method, it is unnecessary to immediately upgrade the distribution network infrastructure to avoid network overloading.
"]},{"key":"dc:title","label":"Title","values":["Spatial-Temporal Stochasticity of Electric Vehicles in Integrated Traffic and Power System"]}]}],"canonical_facts":{"dc:contributor":["Timothy Hansen"],"dc:creator":["Shrestha, Sadhana"],"dc:date.available":["2016-08-22T07:00:00Z"],"dc:description.abstract":["A penetration of a large number of electric vehicles for charging their batteries in the grid can have a negative impact to the grid. To prevent a negative effect to the grid, the behavior of electric vehicles must be accurately modeled and their charging schedules must be coordinated. Therefore, it is necessary to determine where and how much charge is available in electric vehicles in the distribution system. In this thesis, a state transition algorithm is designed to determine a stochastic model of electric vehicles to simulate electric vehicle movement in an integrated traffic and power network. Dijkstra’s algorithm is used to determine the shortest distance between end-user residential and office areas. An uncoordinated and semi-coordinated charging technique are used to charge electric vehicles at different time intervals at different charging stations based on their driving patterns. Monte Carlo simulation is performed to analyze the effect of uncertainty in driving behavior. Results show that uncoordinated charging techniques generate new peaks in the load profile of each node in the distribution system and cause undervoltage problems in the power network. The semi-coordinated charging technique introduces a delay in the charging time to shift electric vehicle charging loads to off-peak times. Hence, with the semi-coordinated charging method, it is unnecessary to immediately upgrade the distribution network infrastructure to avoid network overloading.
"],"dc:identifier":["https://openprairie.sdstate.edu/etd/1047"],"dc:language":["en"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Dijkstra's algorithm","electric vehicles","integrated traffic and power system","Monte Carlo simulation","semi-coordinated charging","state transition algorithm","Electrical and Computer Engineering"],"dc:title":["Spatial-Temporal Stochasticity of Electric Vehicles in Integrated Traffic and Power System"],"thesis:degree_discipline":["Electrical Engineering and Computer Science"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T04:28:30Z"}