{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/90670"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/90670","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Charging facility allocation in smart cities","abstract":"The raising concerns of energy consumption and air pollution advance the development of electric vehicle technologies and promote the increased deployment of Electric Vehicles (EVs) towards electric transportation. The increasing number of EVs on the road network leads to a growing challenge of electricity management for the power grid to promptly supply electricity to EVs. In order to address this challenge, we need to carefully plan the energy sources and energy delivery via charging facilities to EVs, taking into consideration interdependencies between roads/transportation and electric grid. In this thesis, we focus on studying the placement of energy sources and their charging facilities for EVs by developing: 1) an extended Flow Refueling Location model which finds optimal locations for charging stations as well as dynamic wireless charging pads, and 2) a 2-stage planning process for placement of charging station. The first stage of the planning process is to determine the optimal locations for placing the charging stations to serve the maximum amount of EVs on the road network. Given the selected optimal locations, the second stage determines the capacity of the charging service locations with the purpose of minimizing the total waiting time of EV drivers across the road network to charge their EVs. We show the effectiveness of these two planning models on a sample road network during our performance evaluation.","abstract_html":"The raising concerns of energy consumption and air pollution advance the development of electric vehicle technologies and promote the increased deployment of Electric Vehicles (EVs) towards electric transportation. The increasing number of EVs on the road network leads to a growing challenge of electricity management for the power grid to promptly supply electricity to EVs. In order to address this challenge, we need to carefully plan the energy sources and energy delivery via charging facilities to EVs, taking into consideration interdependencies between roads/transportation and electric grid. In this thesis, we focus on studying the placement of energy sources and their charging facilities for EVs by developing: 1) an extended Flow Refueling Location model which finds optimal locations for charging stations as well as dynamic wireless charging pads, and 2) a 2-stage planning process for placement of charging station. The first stage of the planning process is to determine the optimal locations for placing the charging stations to serve the maximum amount of EVs on the road network. Given the selected optimal locations, the second stage determines the capacity of the charging service locations with the purpose of minimizing the total waiting time of EV drivers across the road network to charge their EVs. We show the effectiveness of these two planning models on a sample road network during our performance evaluation.","abstract_has_math":false,"creators":["Chang, Siting"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-07-07T19:58:13Z","date_published":"2016-07-07T19:58:13Z","updated_at":"2026-07-22T22:26:34Z","subjects":["Charging facility, planning, optimization, EV, charging station, charging pad, traffic flow, energy source, placement, smart city"],"languages":["en"],"rights":["Copyright 2016 Siting Chang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/90670","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Chang, Siting"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-07-07T19:58:13Z","2016-04-27","2016-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Charging facility, planning, optimization, EV, charging station, charging pad, traffic flow, energy source, placement, smart city"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Siting Chang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/90670"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The raising concerns of energy consumption and air pollution advance the development of electric vehicle technologies and promote the increased deployment of Electric Vehicles (EVs) towards electric transportation. The increasing number of EVs on the road network leads to a growing challenge of electricity management for the power grid to promptly supply electricity to EVs. In order to address this challenge, we need to carefully plan the energy sources and energy delivery via charging facilities to EVs, taking into consideration interdependencies between roads/transportation and electric grid. In this thesis, we focus on studying the placement of energy sources and their charging facilities for EVs by developing: 1) an extended Flow Refueling Location model which finds optimal locations for charging stations as well as dynamic wireless charging pads, and 2) a 2-stage planning process for placement of charging station. The first stage of the planning process is to determine the optimal locations for placing the charging stations to serve the maximum amount of EVs on the road network. Given the selected optimal locations, the second stage determines the capacity of the charging service locations with the purpose of minimizing the total waiting time of EV drivers across the road network to charge their EVs. We show the effectiveness of these two planning models on a sample road network during our performance evaluation.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-07-07 without embargo terms","The student, Siting Chang, accepted the attached license on 2016-04-26 at 21:03.","The student, Siting Chang, submitted this Thesis for approval on 2016-04-26 at 21:04.","This Thesis was approved for publication on 2016-04-27 at 11:43.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9528 on 2016-07-07 at 13:33:35","Made available in DSpace on 2016-07-07T19:58:13Z (GMT). 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In order to address this challenge, we need to carefully plan the energy sources and energy delivery via charging facilities to EVs, taking into consideration interdependencies between roads/transportation and electric grid. In this thesis, we focus on studying the placement of energy sources and their charging facilities for EVs by developing: 1) an extended Flow Refueling Location model which finds optimal locations for charging stations as well as dynamic wireless charging pads, and 2) a 2-stage planning process for placement of charging station. The first stage of the planning process is to determine the optimal locations for placing the charging stations to serve the maximum amount of EVs on the road network. Given the selected optimal locations, the second stage determines the capacity of the charging service locations with the purpose of minimizing the total waiting time of EV drivers across the road network to charge their EVs. We show the effectiveness of these two planning models on a sample road network during our performance evaluation.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-07-07 without embargo terms","The student, Siting Chang, accepted the attached license on 2016-04-26 at 21:03.","The student, Siting Chang, submitted this Thesis for approval on 2016-04-26 at 21:04.","This Thesis was approved for publication on 2016-04-27 at 11:43.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9528 on 2016-07-07 at 13:33:35","Made available in DSpace on 2016-07-07T19:58:13Z (GMT). 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