{"id":{"repo_id":"reykjavik","oai_identifier":"oai:skemman.is:1946/40087"},"canonical_url":"https://search.dev.ndltd.org/etd/reykjavik/oai:skemman.is:1946/40087","repository":{"repo_id":"reykjavik","name":"Reykjavík University","base_url":"https://skemman.is/oai/request"},"display":{"title":"Reconfiguration of distribution systems to reduce power loss using ant Colony Optimization and Simulated Annealing","abstract":"This study researches the optimal reconfiguration to minimize power losses of the test grid, the CIGRE network, and a grid owned by the Reykjavik area utility company Veitur, called A3-A5. It is implemented in Python, where the grid's data is obtained and processed with Pandapower, a software-based on python that allows characterization of distribution grids and running power flows. The optimization is executed with two techniques: Simulated annealing (SA) and ant colony optimization (ACO). SA is a well know and widely used optimization technique for reconfiguration problems. It is an implementation based on keeping radiality and minimizing power losses. Also, it evaluates every option with the cooling schedule and finally accepts the best reconfiguration. ACO works with a focus on processing the grid as a graph. The implementation is based on graph theory that allows establishing a rule framework that assures the constraints and fast execution and searches for the best reconfiguration option. Reliability, power quality, and end-user are direct beneficiaries of the reconfiguration of the distribution grids. In addition, reconfiguration is the first step for future implementations to improve the grid.","abstract_html":"This study researches the optimal reconfiguration to minimize power losses of the test grid, the CIGRE network, and a grid owned by the Reykjavik area utility company Veitur, called A3-A5. It is implemented in Python, where the grid's data is obtained and processed with Pandapower, a software-based on python that allows characterization of distribution grids and running power flows. The optimization is executed with two techniques: Simulated annealing (SA) and ant colony optimization (ACO). SA is a well know and widely used optimization technique for reconfiguration problems. It is an implementation based on keeping radiality and minimizing power losses. Also, it evaluates every option with the cooling schedule and finally accepts the best reconfiguration. ACO works with a focus on processing the grid as a graph. The implementation is based on graph theory that allows establishing a rule framework that assures the constraints and fast execution and searches for the best reconfiguration option. Reliability, power quality, and end-user are direct beneficiaries of the reconfiguration of the distribution grids. In addition, reconfiguration is the first step for future implementations to improve the grid.","abstract_has_math":false,"creators":["Daniel Barrios Castilla 1992-"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Háskólinn í Reykjavík"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-10-28T11:36:16Z","date_published":"2021-10-28T11:36:16Z","updated_at":"2026-07-27T20:36:46Z","subjects":["Raforkuverkfræð","Meistaraprófsritgerðir","Orkunýting","Dreifikerfi","Electric power distribution","Electric power engineering","Energy consumption"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1946/40087","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Háskólinn í Reykjavík"]},{"key":"dc:creator","label":"Author","values":["Daniel Barrios Castilla 1992-"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2021-10-28T11:36:15Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-10-28T11:36:15Z"]},{"key":"dc:date.issued","label":"Date","values":["2021-10-28T11:36:16Z"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Raforkuverkfræð","Meistaraprófsritgerðir","Orkunýting","Dreifikerfi","Electric power distribution","Electric power engineering","Energy consumption"]}]},{"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":["http://hdl.handle.net/1946/40087"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This study researches the optimal reconfiguration to minimize power losses of the test grid, the CIGRE network, and a grid owned by the Reykjavik area utility company Veitur, called A3-A5. It is implemented in Python, where the grid's data is obtained and processed with Pandapower, a software-based on python that allows characterization of distribution grids and running power flows. The optimization is executed with two techniques: Simulated annealing (SA) and ant colony optimization (ACO). SA is a well know and widely used optimization technique for reconfiguration problems. It is an implementation based on keeping radiality and minimizing power losses. Also, it evaluates every option with the cooling schedule and finally accepts the best reconfiguration. ACO works with a focus on processing the grid as a graph. The implementation is based on graph theory that allows establishing a rule framework that assures the constraints and fast execution and searches for the best reconfiguration option. Reliability, power quality, and end-user are direct beneficiaries of the reconfiguration of the distribution grids. In addition, reconfiguration is the first step for future implementations to improve the grid."]},{"key":"dc:title","label":"Title","values":["Reconfiguration of distribution systems to reduce power loss using ant Colony Optimization and Simulated Annealing"]}]}],"canonical_facts":{"dc:contributor":["Háskólinn í Reykjavík"],"dc:creator":["Daniel Barrios Castilla 1992-"],"dc:date.accessioned":["2021-10-28T11:36:15Z"],"dc:date.available":["2021-10-28T11:36:15Z"],"dc:date.issued":["2021-10-28T11:36:16Z"],"dc:description.abstract":["This study researches the optimal reconfiguration to minimize power losses of the test grid, the CIGRE network, and a grid owned by the Reykjavik area utility company Veitur, called A3-A5. It is implemented in Python, where the grid's data is obtained and processed with Pandapower, a software-based on python that allows characterization of distribution grids and running power flows. The optimization is executed with two techniques: Simulated annealing (SA) and ant colony optimization (ACO). SA is a well know and widely used optimization technique for reconfiguration problems. It is an implementation based on keeping radiality and minimizing power losses. Also, it evaluates every option with the cooling schedule and finally accepts the best reconfiguration. ACO works with a focus on processing the grid as a graph. The implementation is based on graph theory that allows establishing a rule framework that assures the constraints and fast execution and searches for the best reconfiguration option. Reliability, power quality, and end-user are direct beneficiaries of the reconfiguration of the distribution grids. In addition, reconfiguration is the first step for future implementations to improve the grid."],"dc:identifier.uri":["http://hdl.handle.net/1946/40087"],"dc:language.iso":["en"],"dc:subject":["Raforkuverkfræð","Meistaraprófsritgerðir","Orkunýting","Dreifikerfi","Electric power distribution","Electric power engineering","Energy consumption"],"dc:title":["Reconfiguration of distribution systems to reduce power loss using ant Colony Optimization and Simulated Annealing"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T20:36:46Z"}