{"id":{"repo_id":"regina","oai_identifier":"oai:uregina.scholaris.ca:10294/16168"},"canonical_url":"https://search.dev.ndltd.org/etd/regina/oai:uregina.scholaris.ca:10294/16168","repository":{"repo_id":"regina","name":"University of Regina","base_url":"https://uregina.scholaris.ca/server/oai/request"},"display":{"title":"Integration of electric vehicles into power systems","abstract":"The power system is continuously evolving and facing various challenges, such as increasing demand, and large-scale electric vehicle (EV) penetration. These challenges have a negative impact on the reliability and sustainability of the power system. For instance, EV charging represents an intensive electric load. Their penetration into the power system poses significant challenges to the operation and control of the power distribution system. Therefore, grid operators need to prepare for high-level EV penetration into the power system. On the other hand, EVs can be used as mobile energy storage systems for frequency regulation, which refers to vehicle-to-grid (V2G) applications. Since power generation must be controlled to continuously meet demand, and any imbalance between supply and demand will cause voltage and frequency deviation, short-term load forecasting becomes increasingly important. In this study, we developed a full wavelet neural network approach for short-term load forecasting, which is an ensemble method of full wavelet packet transform and neural networks. The proposed approach decreases MAPE by 20% compared to the traditional neural network methods. To tackle the frequency deviation issue, this study proposes centralized and distributed optimization models for V2G applications to provide frequency regulation to power systems. The centralized model has limitations which are addressed by developing a distributed model. The distributed model is solved iteratively with the alternating direction method of multipliers (ADMM). Simulation results show that the proposed models can aggregate EVs for frequency regulation; meanwhile, the EV owners can obtain monetary rewards. A data-driven and parameterized EV charging model is proposed to evaluate the impact of EV penetration on urban residential power distribution. Characteristics of EV charging are analyzed using actual profiles in Saskatchewan, Canada and a location-based algorithm identifies residential EV charging data. Model parameters are modeled by using statistical methods and aggregated using the Monte Carlo method. The results show that the proposed models are valid, accurate, and robust. The impact of EV penetration on power distribution systems is evaluated by integrating EV charging profiles and base demand into a load flow model based on transformer loading and voltage drop at customers' houses. Simulation results show that the 15-house distribution system can incorporate up to 22 EVs during on-peak demand days, while the 22-house system cannot handle more than 11 EVs. The observed trend can be attributed to the rise in on-peak demand as the number of houses in the distribution system increases, thereby necessitating a reduction in the critical number of EVs. The study proposes an optimal EV charging model that is considered as an elastic demand under the concept of demand response. The model schedules and controls EV charging to minimize peak demand and shift load off from the peak demand period. Results demonstrate the effectiveness of the model in reducing peak demand and deferring infrastructure investment.","abstract_html":"The power system is continuously evolving and facing various challenges, such as increasing demand, and large-scale electric vehicle (EV) penetration. These challenges have a negative impact on the reliability and sustainability of the power system. For instance, EV charging represents an intensive electric load. Their penetration into the power system poses significant challenges to the operation and control of the power distribution system. Therefore, grid operators need to prepare for high-level EV penetration into the power system. On the other hand, EVs can be used as mobile energy storage systems for frequency regulation, which refers to vehicle-to-grid (V2G) applications. Since power generation must be controlled to continuously meet demand, and any imbalance between supply and demand will cause voltage and frequency deviation, short-term load forecasting becomes increasingly important. In this study, we developed a full wavelet neural network approach for short-term load forecasting, which is an ensemble method of full wavelet packet transform and neural networks. The proposed approach decreases MAPE by 20% compared to the traditional neural network methods. To tackle the frequency deviation issue, this study proposes centralized and distributed optimization models for V2G applications to provide frequency regulation to power systems. The centralized model has limitations which are addressed by developing a distributed model. The distributed model is solved iteratively with the alternating direction method of multipliers (ADMM). Simulation results show that the proposed models can aggregate EVs for frequency regulation; meanwhile, the EV owners can obtain monetary rewards. A data-driven and parameterized EV charging model is proposed to evaluate the impact of EV penetration on urban residential power distribution. Characteristics of EV charging are analyzed using actual profiles in Saskatchewan, Canada and a location-based algorithm identifies residential EV charging data. Model parameters are modeled by using statistical methods and aggregated using the Monte Carlo method. The results show that the proposed models are valid, accurate, and robust. The impact of EV penetration on power distribution systems is evaluated by integrating EV charging profiles and base demand into a load flow model based on transformer loading and voltage drop at customers' houses. Simulation results show that the 15-house distribution system can incorporate up to 22 EVs during on-peak demand days, while the 22-house system cannot handle more than 11 EVs. The observed trend can be attributed to the rise in on-peak demand as the number of houses in the distribution system increases, thereby necessitating a reduction in the critical number of EVs. The study proposes an optimal EV charging model that is considered as an elastic demand under the concept of demand response. The model schedules and controls EV charging to minimize peak demand and shift load off from the peak demand period. Results demonstrate the effectiveness of the model in reducing peak demand and deferring infrastructure investment.","abstract_has_math":false,"creators":["Ahmed, Mohamed Ahmed Elhendawi"],"institution":"Faculty of Graduate Studies and Research, University of Regina","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral -- first","degree_discipline":"Engineering - Electronic Systems","degree_department":null,"school":null,"contributors":[],"advisors":["Wang, Zhanle (Gerald)"],"committee_chairs":[],"committee_members":["Bais, Abdul","Laforge, Paul","Deng, Dianliang"],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-24T04:03:34Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/4187"],"render_values":[{"text":"https://doi.org/10.82465/4187","href":"https://doi.org/10.82465/4187","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10294/16168","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Wang, Zhanle (Gerald)"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Bais, Abdul","Laforge, Paul","Deng, Dianliang"]},{"key":"dc:creator","label":"Author","values":["Ahmed, Mohamed Ahmed Elhendawi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-12-11T18:47:24Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-12-11T18:47:24Z"]},{"key":"dc:date.issued","label":"Date","values":["2023-05"]},{"key":"dc:publisher","label":"Institution","values":["Faculty of Graduate Studies and Research, University of Regina"]},{"key":"dc:type","label":"Dc Type","values":["master thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering - Electronic Systems"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral -- first"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Faculty of Graduate Studies and Research, University of Regina"]}]},{"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.doi","label":"DOI","values":["https://doi.org/10.82465/4187"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10294/16168"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Electronic Systems Engineering, University of Regina. xxv, 243 p."]},{"key":"dc:description.abstract","label":"Abstract","values":["The power system is continuously evolving and facing various challenges, such as increasing demand, and large-scale electric vehicle (EV) penetration. These challenges have a negative impact on the reliability and sustainability of the power system. For instance, EV charging represents an intensive electric load. Their penetration into the power system poses significant challenges to the operation and control of the power distribution system. Therefore, grid operators need to prepare for high-level EV penetration into the power system. On the other hand, EVs can be used as mobile energy storage systems for frequency regulation, which refers to vehicle-to-grid (V2G) applications. Since power generation must be controlled to continuously meet demand, and any imbalance between supply and demand will cause voltage and frequency deviation, short-term load forecasting becomes increasingly important. In this study, we developed a full wavelet neural network approach for short-term load forecasting, which is an ensemble method of full wavelet packet transform and neural networks. The proposed approach decreases MAPE by 20% compared to the traditional neural network methods. To tackle the frequency deviation issue, this study proposes centralized and distributed optimization models for V2G applications to provide frequency regulation to power systems. The centralized model has limitations which are addressed by developing a distributed model. The distributed model is solved iteratively with the alternating direction method of multipliers (ADMM). Simulation results show that the proposed models can aggregate EVs for frequency regulation; meanwhile, the EV owners can obtain monetary rewards. A data-driven and parameterized EV charging model is proposed to evaluate the impact of EV penetration on urban residential power distribution. Characteristics of EV charging are analyzed using actual profiles in Saskatchewan, Canada and a location-based algorithm identifies residential EV charging data. Model parameters are modeled by using statistical methods and aggregated using the Monte Carlo method. The results show that the proposed models are valid, accurate, and robust. The impact of EV penetration on power distribution systems is evaluated by integrating EV charging profiles and base demand into a load flow model based on transformer loading and voltage drop at customers' houses. Simulation results show that the 15-house distribution system can incorporate up to 22 EVs during on-peak demand days, while the 22-house system cannot handle more than 11 EVs. The observed trend can be attributed to the rise in on-peak demand as the number of houses in the distribution system increases, thereby necessitating a reduction in the critical number of EVs. The study proposes an optimal EV charging model that is considered as an elastic demand under the concept of demand response. The model schedules and controls EV charging to minimize peak demand and shift load off from the peak demand period. Results demonstrate the effectiveness of the model in reducing peak demand and deferring infrastructure investment."]},{"key":"dc:title","label":"Title","values":["Integration of electric vehicles into power systems"]}]}],"canonical_facts":{"dc:contributor.advisor":["Wang, Zhanle (Gerald)"],"dc:contributor.committeemember":["Bais, Abdul","Laforge, Paul","Deng, Dianliang"],"dc:creator":["Ahmed, Mohamed Ahmed Elhendawi"],"dc:date.accessioned":["2023-12-11T18:47:24Z"],"dc:date.available":["2023-12-11T18:47:24Z"],"dc:date.issued":["2023-05"],"dc:description":["A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Electronic Systems Engineering, University of Regina. xxv, 243 p."],"dc:description.abstract":["The power system is continuously evolving and facing various challenges, such as increasing demand, and large-scale electric vehicle (EV) penetration. These challenges have a negative impact on the reliability and sustainability of the power system. For instance, EV charging represents an intensive electric load. Their penetration into the power system poses significant challenges to the operation and control of the power distribution system. Therefore, grid operators need to prepare for high-level EV penetration into the power system. On the other hand, EVs can be used as mobile energy storage systems for frequency regulation, which refers to vehicle-to-grid (V2G) applications. Since power generation must be controlled to continuously meet demand, and any imbalance between supply and demand will cause voltage and frequency deviation, short-term load forecasting becomes increasingly important. In this study, we developed a full wavelet neural network approach for short-term load forecasting, which is an ensemble method of full wavelet packet transform and neural networks. The proposed approach decreases MAPE by 20% compared to the traditional neural network methods. To tackle the frequency deviation issue, this study proposes centralized and distributed optimization models for V2G applications to provide frequency regulation to power systems. The centralized model has limitations which are addressed by developing a distributed model. The distributed model is solved iteratively with the alternating direction method of multipliers (ADMM). Simulation results show that the proposed models can aggregate EVs for frequency regulation; meanwhile, the EV owners can obtain monetary rewards. A data-driven and parameterized EV charging model is proposed to evaluate the impact of EV penetration on urban residential power distribution. Characteristics of EV charging are analyzed using actual profiles in Saskatchewan, Canada and a location-based algorithm identifies residential EV charging data. Model parameters are modeled by using statistical methods and aggregated using the Monte Carlo method. The results show that the proposed models are valid, accurate, and robust. The impact of EV penetration on power distribution systems is evaluated by integrating EV charging profiles and base demand into a load flow model based on transformer loading and voltage drop at customers' houses. Simulation results show that the 15-house distribution system can incorporate up to 22 EVs during on-peak demand days, while the 22-house system cannot handle more than 11 EVs. The observed trend can be attributed to the rise in on-peak demand as the number of houses in the distribution system increases, thereby necessitating a reduction in the critical number of EVs. The study proposes an optimal EV charging model that is considered as an elastic demand under the concept of demand response. The model schedules and controls EV charging to minimize peak demand and shift load off from the peak demand period. Results demonstrate the effectiveness of the model in reducing peak demand and deferring infrastructure investment."],"dc:identifier.doi":["https://doi.org/10.82465/4187"],"dc:identifier.uri":["https://hdl.handle.net/10294/16168"],"dc:language.iso":["en"],"dc:publisher":["Faculty of Graduate Studies and Research, University of Regina"],"dc:title":["Integration of electric vehicles into power systems"],"dc:type":["master thesis"],"thesis:degree_discipline":["Engineering - Electronic Systems"],"thesis:degree_level":["Doctoral -- first"],"thesis:degree_name":["Doctor of Philosophy (PhD)"],"thesis:institution_name":["Faculty of Graduate Studies and Research, University of Regina"]},"updated_at":"2026-07-24T04:03:34Z"}