793 research outputs found
Integrating Electric Buses and Diverse Charging Technologies for Sustainable Public Transportation
No full textA Master of Science thesis in Electrical Engineering by Sama Elkholy entitled, “Integrating Electric Buses and Diverse Charging Technologies for Sustainable Public Transportation”, submitted in May 2024. Thesis advisor is Dr. Mostafa Shaaban. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE
Optimal Dispatch of Mobile Energy Storage Unit to Support EV Charging Stations
No full textA Master of Science thesis in Electrical Engineering by Mohamed Mostafa Abdelazim Elmeligy entitled, “Optimal Dispatch of Mobile Energy Storage Unit to Support EV Charging Stations”, submitted in April 2021. Thesis advisor is Dr. Mostafa Shaaban. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).s transportation electrification increases globally, new technologies emerged in the past few years to meet the growth of the electricity demand. A mobile energy storage system (MESS) could provide several services to the distribution systems such as reactive power support, renewable energy integration, peak shaving, and load leveling. In addition, an MESS can be utilized to support electric vehicles (EVs) charging in different parking lots (PLs), which is the main focus of this thesis. The task of multiple stationary storage units can be achieved using a single MESS with a relatively lower cost. In this thesis, a new dynamic optimal dispatch strategy for MESS is proposed to support several charging stations sharing the same geographical area. The objective of the proposed approach is to optimally dispatch the MESS in conjunction with optimal EVs charging to minimize the total operation cost and address the extra demand of PLs. Different case studies are provided on the IEEE 38-bus system and a real radial feeder in Ontario, Canada to test the proposed approach. In the second phase of this research, a new approach is proposed for the optimal resource allocation for an MESS fleet owned by multiple PLs sharing the same geographical area and sharing its capital and operational cost. The aim is to optimally decide on the number of MESSs and their battery bank capacities that should be used in order to serve charging stations participated in the project. The optimization includes practical constraints for battery dynamics. Comparative case studies showed the effectiveness of the proposed algorithms.College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE
Optimizing Resource allocation for Post-Disaster Recovery in Resilient Distribution Networks
No full textA Master of Science thesis in Electrical Engineering by Saif Rashid AlMansoori entitled, “Optimizing Resource allocation for Post-Disaster Recovery in Resilient Distribution Networks”, submitted in April 2025. Thesis advisor is Dr. Ahmed Osman-Ahmed and thesis co-advisor is Dr. Mostafa Shaaban. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE
Analyzing and Mitigating Cyber Threats on Elecric Vehicles Chargers for Resilient Smart Grids
No full textA Master of Science thesis in Electrical Engineering by Ahmed Abdelfatah entitled, “Analyzing and Mitigating Cyber Threats on Elecric Vehicles Chargers for Resilient Smart Grids”, submitted in April 2024. Thesis advisor is Dr. Mostafa Shaaban and thesis co-advisor is Dr. Abdelfatah Mohamed. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE
Machine Learning Based Maintenance Strategies for Enhancing PV System Performance in UAE
No full textA Master of Science thesis in Electrical Engineering by Omar Abdelaziz entitled, “Machine Learning Based Maintenance Strategies for Enhancing PV System Performance in UAE”, submitted in May 2025. Thesis advisor is Dr. Mostafa Shaaban and thesis co-advisor is Dr. Ahmed Osman. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE
EV Charging Coordination in Blockchain-Based Energy Markets
No full textA Master of Science thesis in Electrical Engineering by Mahdi Ali Mohammed entitled, “EV Charging Coordination in Blockchain-Based Energy Markets”, submitted in April 2022. Thesis advisor is Dr. Mostafa Shaaban and thesis co-advisor is Dr. Mohammed Nassar. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE
Accommodating High Penetrations of Renewable Distributed Generation Mix in Smart Grids
No full textA Master of Science thesis in Electrical Engineering by Mohammad Tarek Khayata entitled, "Accommodating High Penetrations of Renewable Distributed Generation Mix in Smart Grids," submitted in April 2017. Thesis advisor is Dr. Mostafa Shaaban. Soft and hard copy available.This work proposes a new method for renewable distributed generation (DG) allocation in smart grid. The main objective is to minimize the overall investment which includes the capital cost of DG units, the operation and maintenance costs of DG units, and the cost of purchasing energy from the grid. The proposed approach takes into consideration the uncertainty and variability associated with generation, demand, and energy cost in addition to the communication infrastructure which is the main contribution of this work. The communication infrastructure under the smart grid paradigm will allow real-time control of the system assets. Therefore, considering this property during the planning phase enhances the system performance and optimizes the overall investment. The proposed approach relies on developing probabilistic models for each generation technology, energy prices, and demand. Then, these models are combined into one multi-state gen-load-price probabilistic model that describes all possible conditions of the system. The number of states in the final model is a tradeoff between the accuracy of results and computational time. Genetic algorithm (GA) optimization technique is utilized in this study to solve the DG planning problem. Simulation results on a typical distribution system are provided to prove the effectiveness of the proposed approach in increasing the renewable DG penetration in smart grids while maximizing the profit of the investment. Moreover, the results obtained through the use of the proposed smart operation are compared with the conventional planning methodologies to demonstrate the targeted added value. A significant cost saving of 28.3% and 254% higher percentage of DG penetration are achieved with the proposed DGs curtailment technique to mitigate technical system violations, which proves the significant advantage of adopting smart grid operation in planning problems.College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE
Estimating Dust Accumulation on Photovoltaic Modules in the UAE
No full textA Master of Science thesis in Electrical Engineering by Amal AbdulAziz AlArif entitled, “Estimating Dust Accumulation on Photovoltaic Modules in the UAE”, submitted in May 2019. Thesis advisor is Dr. Mostafa Shaaban. Soft and hard copy available.Among the challenges facing solar photovoltaic (PV) systems in the United Arab Emirates (UAE), dust is considered the most severe problem that faces the growth of solar power plants. Dust accumulation on solar PV panels results in a degradation in the output power. The UAE has a low intensity of rainfalls and wind velocity; thus, solar PV panels must be cleaned manually or using automated cleaning methods which are costly. Estimating dust accumulation on solar PV panels will increase the output power of solar PV power plants and reduce maintenance costs by initiating cleaning actions only when required. In this thesis, the effect of natural dust accumulation on solar PV panels is investigated using field measurements and regression modeling. Experimental data were collected under various weather conditions and controlled levels of dust. Solar PV output power, ambient temperature, solar irradiance, and dust were monitored in a period of two months to collect sufficient data for constructing a dust estimation model. Regression models were trained and tested to develop an accurate model for estimating the dust accumulated on solar PV panels in the UAE. The developed fine tree regression model provided accurate dust accumulation prediction with Root Mean Square Error (RMSE) of\ 0.0255 g/m2. The model was tested on different case studies with a random amount of dust applied the solar PV panels to confirm the accuracy of the developed model.College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE
Planning and Operation of Smart EV Parking Lots
No full textA Master of Science thesis in Electrical Engineering by Osama Magdy Abdelwahab entitled, “Planning and Operation of Smart EV Parking Lots”, submitted in May 2019. Thesis advisor is Dr. Mostafa Farouk Shaaban. Soft and hard copy available.Aiming to reduce Green House Gas (GHG) emissions and to increase resources security, renewable resources and electric vehicles (EVs) are gaining a lot of global interest. Promoting the use of EVs for consumers requires proper charging infrastructure adding a considerable amount of load on the grid. Aiming to accommodate this extra load economically, proper planning and operation studies have to be implemented avoiding grid overloading severe consequences. The smart coordination of EV charging via the demand side management and local generation from Photovoltaic panels can efficiently reshape the EV charging load to ensure seamless integration with the grid. Therefore, this research focuses on the development of new methodologies to facilitate accommodating high penetration of EVs. The research work will be achieved through two main stages, namely the planning stage and the operation stage. One of the main pillars of smart grids is the implementation of twoway communication with customers. Therefore, in both research stages, it is assumed that smart signals can be exchanged between the EV and the dispatch center. Planning stage starts with the development of new models to describe the effects of EVs charging as an electric load. The output of the proposed planning scheme will include several implementation decisions such as configuration of the charging stations, types, and number of EV chargers, the local generation by phtovoltic units and the expected profit, among other information. The operation stage proposes an innovative approach for day ahead load scheduling for smart charging/discharging management for EV charging stations. The main target of this approach is to maximize both customer satisfaction and stakeholder's profitCollege of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE
Electric Grid Resilience Enhancement During Natural Disasters: An Optimization-Based UAV Inspection and Dynamic Crew Dispatch Model
No full textA Master of Science thesis in Electrical Engineering by Yousef Serag entitled, “Electric Grid Resilience Enhancement During Natural Disasters: An Optimization-Based UAV Inspection and Dynamic Crew Dispatch Model”, submitted in April 2025. Thesis advisor is Dr. Mostafa Shaaban and thesis co-advisor is Dr. Mahmoud Ibrahim. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Natural disasters pose significant challenges to power grid resilience, often resulting in prolonged outages and substantial economic losses due to inefficient restoration processes. Traditional methods primarily focus on optimizing repair crew (RC) sequences while neglecting the critical inspection phase, leading to delayed fault detection and increased costs of interruption . This thesis introduces a holistic, UAV-assisted framework that integrates unmanned aerial vehicle (UAV) inspections, dynamic RC dispatch, and strategic charger placement to address these shortcomings. The approach leverages probabilistic failure analysis to prioritize high-risk lines, optimizes UAV inspection sequences with battery-aware path planning, and dynamically coordinates repair efforts to minimize COI.
The framework’s efficacy is evaluated using three distinct methods: Optimization based Approach, (GA), and Deep Learning (DL). OPTIMIZATION BASED APPROACH provides high accuracy in simplified scenarios but lacks scalability for real-time applications. GA offers a balanced trade-off between accuracy and computational efficiency, while DL delivers rapid, scalable solutions with acceptable accuracy, making it ideal for urgent disaster response. Tested on a 33-bus system, the framework achieves a 56.34% reduction in COI compared to conventional strategies, demonstrating its superiority in reducing downtime and enhancing resilience. The novelty of this work lies in its comprehensive integration of inspection and repair processes, utilizing advanced technologies for real-time adaptability. By addressing the overlooked inspection phase and optimizing resource allocation, this thesis presents a scalable, data-driven solution that significantly advances post-disaster grid restoration, offering a practical approach to mitigate the socio-economic impacts of power outages in large-scale disaster scenarios.College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE
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