{"id":{"repo_id":"northampton","oai_identifier":"oai:pure.atira.dk:studenttheses/7ef20ad7-eff1-4719-a1c4-f6f7cb45e088"},"canonical_url":"https://search.dev.ndltd.org/etd/northampton/oai:pure.atira.dk:studenttheses/7ef20ad7-eff1-4719-a1c4-f6f7cb45e088","repository":{"repo_id":"northampton","name":"University of Northampton","base_url":"https://pure.northampton.ac.uk/ws/oai"},"display":{"title":"Microload Management in Generation Constrained Power Systems","abstract":"The reasons for power systems' outages can be complicated and difficult to pinpoint, but an obvious shortfall in generation compared to electricity demand has been identified as the major cause of load shedding in generation constrained power systems. A sudden rise in demand for electricity on these networks at any time could result in a total collapse of the entire grid. Therefore, in this thesis, algorithms to efficiently allocate the available generation are investigated to prevent the associated hardships and lose experience by the final consumers and the electric utility suppliers, respectively.
Heuristic technique is utilised by developing various dynamic programming-based algorithms to achieve the constraints of uniquely controlling home appliances to reduce the overall demands for electricity by the consumers within the grid in context. These algorithms are focused on the consumers' comfort and the associated benefits to the electricity utility company in the long run. The evaluation of the proposed approach is achieved through microload management by employing three main techniques; General Shedding (GS), Priority Based Shedding (PBS) and Excess Reuse Shedding (ERS). These techniques were evaluated using both Grouped and “UnGrouped” microloads based on how efficient the microload managed the available generation to prevent total blackouts. A progressive reduction in excess microload shedding experienced by GS, PBS, and the ERS shows the proposed algorithms' effectiveness.
Further, predictive algorithms are investigated for microload forecasting towards microload management to prepare both consumers and the electric utility companies for any impending load shedding. Measuring the forecasting accuracy and the root mean square errors of the models evaluated proved the potential for microload demand prediction.","abstract_html":"The reasons for power systems' outages can be complicated and difficult to pinpoint, but an obvious shortfall in generation compared to electricity demand has been identified as the major cause of load shedding in generation constrained power systems. A sudden rise in demand for electricity on these networks at any time could result in a total collapse of the entire grid. Therefore, in this thesis, algorithms to efficiently allocate the available generation are investigated to prevent the associated hardships and lose experience by the final consumers and the electric utility suppliers, respectively. <br/><br/>Heuristic technique is utilised by developing various dynamic programming-based algorithms to achieve the constraints of uniquely controlling home appliances to reduce the overall demands for electricity by the consumers within the grid in context. These algorithms are focused on the consumers' comfort and the associated benefits to the electricity utility company in the long run. The evaluation of the proposed approach is achieved through microload management by employing three main techniques; General Shedding (GS), Priority Based Shedding (PBS) and Excess Reuse Shedding (ERS). These techniques were evaluated using both Grouped and “UnGrouped” microloads based on how efficient the microload managed the available generation to prevent total blackouts. A progressive reduction in excess microload shedding experienced by GS, PBS, and the ERS shows the proposed algorithms' effectiveness. <br/><br/>Further, predictive algorithms are investigated for microload forecasting towards microload management to prepare both consumers and the electric utility companies for any impending load shedding. Measuring the forecasting accuracy and the root mean square errors of the models evaluated proved the potential for microload demand prediction.","abstract_has_math":false,"creators":["Azasoo, Julius"],"institution":null,"degree_name":"Doctoral Thesis","degree_level":"Student thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Opoku Agyeman, Michael","Kanakis, Triantafyllos"],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-5","date_published":"2022-5","updated_at":"2026-07-24T03:26:52Z","subjects":["Optimization","Peak to average power ratio","Smart grids","Smart meters","Demand side management","Dynamic programming","Load shedding","Power generation economics","Power generation reliability","Pricing","Microload shedding"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["oai:pure.atira.dk:studenttheses/7ef20ad7-eff1-4719-a1c4-f6f7cb45e088"],"render_values":[{"text":"oai:pure.atira.dk:studenttheses/7ef20ad7-eff1-4719-a1c4-f6f7cb45e088","href":null,"code":true}]}]},"links":{"outbound_url":"https://pure.northampton.ac.uk/en/studentTheses/7ef20ad7-eff1-4719-a1c4-f6f7cb45e088","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Opoku Agyeman, Michael","Kanakis, Triantafyllos"]},{"key":"dc:creator","label":"Author","values":["Azasoo, Julius"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-5"]},{"key":"dc:date.issued","label":"Date","values":["2022-5"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Graduate School"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://pure.northampton.ac.uk/en/studentTheses/7ef20ad7-eff1-4719-a1c4-f6f7cb45e088"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Student thesis"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctoral Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Optimization","Peak to average power ratio","Smart grids","Smart meters","Demand side management","Dynamic programming","Load shedding","Power generation economics","Power generation reliability","Pricing","Microload shedding"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["oai:pure.atira.dk:studenttheses/7ef20ad7-eff1-4719-a1c4-f6f7cb45e088","https://pure.northampton.ac.uk/en/studentTheses/7ef20ad7-eff1-4719-a1c4-f6f7cb45e088"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://pure.northampton.ac.uk/files/55148922/Azasoo_Julius_2022_Microload_Management_in_Generation_Constrained_Power_Systems.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The reasons for power systems' outages can be complicated and difficult to pinpoint, but an obvious shortfall in generation compared to electricity demand has been identified as the major cause of load shedding in generation constrained power systems. A sudden rise in demand for electricity on these networks at any time could result in a total collapse of the entire grid. Therefore, in this thesis, algorithms to efficiently allocate the available generation are investigated to prevent the associated hardships and lose experience by the final consumers and the electric utility suppliers, respectively.
Heuristic technique is utilised by developing various dynamic programming-based algorithms to achieve the constraints of uniquely controlling home appliances to reduce the overall demands for electricity by the consumers within the grid in context. These algorithms are focused on the consumers' comfort and the associated benefits to the electricity utility company in the long run. The evaluation of the proposed approach is achieved through microload management by employing three main techniques; General Shedding (GS), Priority Based Shedding (PBS) and Excess Reuse Shedding (ERS). These techniques were evaluated using both Grouped and “UnGrouped” microloads based on how efficient the microload managed the available generation to prevent total blackouts. A progressive reduction in excess microload shedding experienced by GS, PBS, and the ERS shows the proposed algorithms' effectiveness.
Further, predictive algorithms are investigated for microload forecasting towards microload management to prepare both consumers and the electric utility companies for any impending load shedding. Measuring the forecasting accuracy and the root mean square errors of the models evaluated proved the potential for microload demand prediction."]},{"key":"dc:title","label":"Title","values":["Microload Management in Generation Constrained Power Systems"]}]}],"canonical_facts":{"dc:contributor.advisor":["Opoku Agyeman, Michael","Kanakis, Triantafyllos"],"dc:creator":["Azasoo, Julius"],"dc:date":["2022-5"],"dc:date.issued":["2022-5"],"dc:description.abstract":["The reasons for power systems' outages can be complicated and difficult to pinpoint, but an obvious shortfall in generation compared to electricity demand has been identified as the major cause of load shedding in generation constrained power systems. A sudden rise in demand for electricity on these networks at any time could result in a total collapse of the entire grid. Therefore, in this thesis, algorithms to efficiently allocate the available generation are investigated to prevent the associated hardships and lose experience by the final consumers and the electric utility suppliers, respectively.
Heuristic technique is utilised by developing various dynamic programming-based algorithms to achieve the constraints of uniquely controlling home appliances to reduce the overall demands for electricity by the consumers within the grid in context. These algorithms are focused on the consumers' comfort and the associated benefits to the electricity utility company in the long run. The evaluation of the proposed approach is achieved through microload management by employing three main techniques; General Shedding (GS), Priority Based Shedding (PBS) and Excess Reuse Shedding (ERS). These techniques were evaluated using both Grouped and “UnGrouped” microloads based on how efficient the microload managed the available generation to prevent total blackouts. A progressive reduction in excess microload shedding experienced by GS, PBS, and the ERS shows the proposed algorithms' effectiveness.
Further, predictive algorithms are investigated for microload forecasting towards microload management to prepare both consumers and the electric utility companies for any impending load shedding. Measuring the forecasting accuracy and the root mean square errors of the models evaluated proved the potential for microload demand prediction."],"dc:identifier":["oai:pure.atira.dk:studenttheses/7ef20ad7-eff1-4719-a1c4-f6f7cb45e088","https://pure.northampton.ac.uk/en/studentTheses/7ef20ad7-eff1-4719-a1c4-f6f7cb45e088"],"dc:identifier.uri":["https://pure.northampton.ac.uk/files/55148922/Azasoo_Julius_2022_Microload_Management_in_Generation_Constrained_Power_Systems.pdf"],"dc:language":["eng"],"dc:publisher.department":["Graduate School"],"dc:relation.isreferencedby":["https://pure.northampton.ac.uk/en/studentTheses/7ef20ad7-eff1-4719-a1c4-f6f7cb45e088"],"dc:subject":["Optimization","Peak to average power ratio","Smart grids","Smart meters","Demand side management","Dynamic programming","Load shedding","Power generation economics","Power generation reliability","Pricing","Microload shedding"],"dc:title":["Microload Management in Generation Constrained Power Systems"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["Student thesis"],"dc:type.qualificationname":["Doctoral Thesis"]},"updated_at":"2026-07-24T03:26:52Z"}