{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1522"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1522","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Discrete optimization of upgrade scheduling","abstract":"A primary objective of the mission to meet climate change goals of reducing greenhouse gas (GHG) emissions is to transition from fossil fuels to zero-emission energy. Fossil fuel production and transportation account for approximately half of the GHG emissions in Canada, making transitioning to zero-emission vehicles (ZEV) a climate action cornerstone. However, a 100% ZEV transportation system is beyond the capacity of Canadian electrical infrastructure in some areas, and the cost to upgrade these systems will be significant. The null hypothesis of this study is that there does not exist an approach to upgrades that optimally reduces the financial burden; conversely, the alternative hypothesis is that such an approach does exist. Mathematical rigour confirms the alternative hypothesis (with assumptions). Computational simulations reject the null hypothesis. The overall average of cost savings within satiable constraints (a subset of generated constraints in proportion to upgrade costs) is 34.5% (m) ± 18.0% (sv).","abstract_html":"A primary objective of the mission to meet climate change goals of reducing greenhouse gas (GHG) emissions is to transition from fossil fuels to zero-emission energy. Fossil fuel production and transportation account for approximately half of the GHG emissions in Canada, making transitioning to zero-emission vehicles (ZEV) a climate action cornerstone. However, a 100% ZEV transportation system is beyond the capacity of Canadian electrical infrastructure in some areas, and the cost to upgrade these systems will be significant. The null hypothesis of this study is that there does not exist an approach to upgrades that optimally reduces the financial burden; conversely, the alternative hypothesis is that such an approach does exist. Mathematical rigour confirms the alternative hypothesis (with assumptions). Computational simulations reject the null hypothesis. The overall average of cost savings within satiable constraints (a subset of generated constraints in proportion to upgrade costs) is 34.5% (m) ± 18.0% (sv).","abstract_has_math":false,"creators":["Smith, Derick"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Science (MSc)","degree_level":null,"degree_discipline":"Modelling and Computational Science","degree_department":null,"school":null,"contributors":[],"advisors":["van Veen, Lennaert","Hoornweg, Daniel"],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-08-01","date_published":"2022-08-01","updated_at":"2026-07-24T05:35:18Z","subjects":["Climate action","Green transition","Electrification","Optimization","Operational research"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1522","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["van Veen, Lennaert","Hoornweg, Daniel"]},{"key":"dc:creator","label":"Author","values":["Smith, Derick"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-09-06T19:37:26Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-09-06T19:37:26Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-08-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Modelling and Computational Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Climate action","Green transition","Electrification","Optimization","Operational research"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/1522"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["A primary objective of the mission to meet climate change goals of reducing greenhouse gas (GHG) emissions is to transition from fossil fuels to zero-emission energy. Fossil fuel production and transportation account for approximately half of the GHG emissions in Canada, making transitioning to zero-emission vehicles (ZEV) a climate action cornerstone. However, a 100% ZEV transportation system is beyond the capacity of Canadian electrical infrastructure in some areas, and the cost to upgrade these systems will be significant. The null hypothesis of this study is that there does not exist an approach to upgrades that optimally reduces the financial burden; conversely, the alternative hypothesis is that such an approach does exist. Mathematical rigour confirms the alternative hypothesis (with assumptions). Computational simulations reject the null hypothesis. The overall average of cost savings within satiable constraints (a subset of generated constraints in proportion to upgrade costs) is 34.5% (m) ± 18.0% (sv)."]},{"key":"dc:title","label":"Title","values":["Discrete optimization of upgrade scheduling"]}]}],"canonical_facts":{"dc:contributor.advisor":["van Veen, Lennaert","Hoornweg, Daniel"],"dc:creator":["Smith, Derick"],"dc:date.accessioned":["2022-09-06T19:37:26Z"],"dc:date.available":["2022-09-06T19:37:26Z"],"dc:date.issued":["2022-08-01"],"dc:description.abstract":["A primary objective of the mission to meet climate change goals of reducing greenhouse gas (GHG) emissions is to transition from fossil fuels to zero-emission energy. Fossil fuel production and transportation account for approximately half of the GHG emissions in Canada, making transitioning to zero-emission vehicles (ZEV) a climate action cornerstone. However, a 100% ZEV transportation system is beyond the capacity of Canadian electrical infrastructure in some areas, and the cost to upgrade these systems will be significant. The null hypothesis of this study is that there does not exist an approach to upgrades that optimally reduces the financial burden; conversely, the alternative hypothesis is that such an approach does exist. Mathematical rigour confirms the alternative hypothesis (with assumptions). Computational simulations reject the null hypothesis. The overall average of cost savings within satiable constraints (a subset of generated constraints in proportion to upgrade costs) is 34.5% (m) ± 18.0% (sv)."],"dc:identifier.uri":["https://hdl.handle.net/10155/1522"],"dc:language.iso":["en"],"dc:subject":["Climate action","Green transition","Electrification","Optimization","Operational research"],"dc:title":["Discrete optimization of upgrade scheduling"],"dc:type":["Thesis"],"thesis:degree_discipline":["Modelling and Computational Science"],"thesis:degree_name":["Master of Science (MSc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:18Z"}