{"id":{"repo_id":"calgary","oai_identifier":"oai:ucalgary.scholaris.ca:1880/123358"},"canonical_url":"https://search.dev.ndltd.org/etd/calgary/oai:ucalgary.scholaris.ca:1880/123358","repository":{"repo_id":"calgary","name":"University of Calgary","base_url":"https://ucalgary.scholaris.ca/server/oai/request"},"display":{"title":"High-Performance Computing and Parallel Techniques for Scalable Optimization of Power System Transition Planning","abstract":"This thesis introduces the Power System Transition Planning (PSTP) framework, formalizing a problem class in long-term transition planning problems that directly embraces the computational burden of realis-tic transition modeling. Unlike conventional Generation and Transmission Expansion (GTEP) or Macro-Energy-System approaches, which compromise on geospatial resolution, technology scope, or uncertainty representation to remain tractable, PSTP preserves these dimensions and tackles the resulting scale and complexity through decomposition and high-performance computing. Methodologically, the work (i) reassesses the role of high-performance computing (HPC) in power system optimization through a structured review and reporting guidance; (ii) formalizes PSTP and reformulates it into a dynamic-programming structure to enable Stochastic Dual Dynamic Programming (SDDP) and (iii) advances parallel SDDP with two contributions: a Nested Synchronous Parallel-by-Node (Nested-SPN) scheme tailored to Markov-chain uncertainty aggregation, and a Relaxed Fixed Integer Cut (RFIC) heuristic that strengthens cuts at modest overhead. Computational studies on an illustrative system (AESO-6) and a realistic, large-scale case (AESO-144) demonstrate that PSTP can be solved scalably, providing valuable insight, with interpretable policies. Rela-tive to conventional synchronous schemes, Nested-SPN reduces communication bottlenecks in large Markov decompositions and, together with RFIC, delivers materially faster convergence (up to 16× in reported trials) without sacrificing solution quality. The results shift the question from whether transition-scale stochastic planning is computationally feasible to how to design and schedule decomposition to sustain solvability as fidelity increases. The thesis closes by outlining directions for deeper integration of operations with planning and for hybrid decompositions that further expand the solvable boundary.","abstract_html":"This thesis introduces the Power System Transition Planning (PSTP) framework, formalizing a problem class in long-term transition planning problems that directly embraces the computational burden of realis-tic transition modeling. Unlike conventional Generation and Transmission Expansion (GTEP) or Macro-Energy-System approaches, which compromise on geospatial resolution, technology scope, or uncertainty representation to remain tractable, PSTP preserves these dimensions and tackles the resulting scale and complexity through decomposition and high-performance computing. Methodologically, the work (i) reassesses the role of high-performance computing (HPC) in power system optimization through a structured review and reporting guidance; (ii) formalizes PSTP and reformulates it into a dynamic-programming structure to enable Stochastic Dual Dynamic Programming (SDDP) and (iii) advances parallel SDDP with two contributions: a Nested Synchronous Parallel-by-Node (Nested-SPN) scheme tailored to Markov-chain uncertainty aggregation, and a Relaxed Fixed Integer Cut (RFIC) heuristic that strengthens cuts at modest overhead. Computational studies on an illustrative system (AESO-6) and a realistic, large-scale case (AESO-144) demonstrate that PSTP can be solved scalably, providing valuable insight, with interpretable policies. Rela-tive to conventional synchronous schemes, Nested-SPN reduces communication bottlenecks in large Markov decompositions and, together with RFIC, delivers materially faster convergence (up to 16× in reported trials) without sacrificing solution quality. The results shift the question from whether transition-scale stochastic planning is computationally feasible to how to design and schedule decomposition to sustain solvability as fidelity increases. The thesis closes by outlining directions for deeper integration of operations with planning and for hybrid decompositions that further expand the solvable boundary.","abstract_has_math":false,"creators":["Al-Shafei, Ahmed"],"institution":"Graduate Studies","degree_name":"Doctor of Philosophy (PhD)","degree_level":null,"degree_discipline":"Engineering – Electrical & Computer","degree_department":null,"school":null,"contributors":[],"advisors":["Zareipour, Hamidreza","Cao, Yankai"],"committee_chairs":[],"committee_members":["Mostafa Farrokhabadi,","Hedman, Mojdeh Khorsand","McCoy, Sean Thomas","Karimipour, Hadis"],"year":2025,"date_issued":"2025-12-09","date_published":"2025-12-09","updated_at":"2026-07-24T01:30:18Z","subjects":[],"languages":["en"],"rights":["University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://dx.doi.org/10.11575/PRISM/50810"],"render_values":[{"text":"https://dx.doi.org/10.11575/PRISM/50810","href":"https://dx.doi.org/10.11575/PRISM/50810","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/1880/123358","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Zareipour, Hamidreza","Cao, Yankai"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Mostafa Farrokhabadi,","Hedman, Mojdeh Khorsand","McCoy, Sean Thomas","Karimipour, Hadis"]},{"key":"dc:creator","label":"Author","values":["Al-Shafei, Ahmed"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2026-02"]},{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-12-11T18:39:35Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-12-09"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Calgary"]},{"key":"dc:type","label":"Dc Type","values":["doctoral thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering – Electrical & Computer"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Calgary"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["University of Calgary graduate students retain copyright ownership and moral rights for their thesis. 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Computational studies on an illustrative system (AESO-6) and a realistic, large-scale case (AESO-144) demonstrate that PSTP can be solved scalably, providing valuable insight, with interpretable policies. Rela-tive to conventional synchronous schemes, Nested-SPN reduces communication bottlenecks in large Markov decompositions and, together with RFIC, delivers materially faster convergence (up to 16× in reported trials) without sacrificing solution quality. The results shift the question from whether transition-scale stochastic planning is computationally feasible to how to design and schedule decomposition to sustain solvability as fidelity increases. 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