A Comprehensive Framework for Optimizing and Integrating Electric Bus Service with Smart Grids for Sustainable Public Transportation

Abstract

A Master of Science thesis in Electrical Engineering by Aisha Abdalla AlAli entitled, “A Comprehensive Framework for Optimizing and Integrating Electric Bus Service with Smart Grids for Sustainable Public Transportationl”, submitted in May 2025. 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).The electrification of public transportation plays a pivotal role in the global transition toward cleaner energy and carbon neutrality, aligning with the Net Zero 2050 strategy. However, large-scale deployment of electric bus (EB) fleets introduces complex challenges in infrastructure and resource planning. This research supports the roadmap for public transit electrification by presenting a comprehensive optimization framework for EB operations and associated infrastructure. It focuses on the strategic deployment of EB fleets, the implementation of advanced charging technologies, and the optimization of service assignments and charging schedules. Furthermore, the research integrates electric bus chargers into smart grid, addressing the optimal allocation of distributed generation (DG) units and assessing the need for line upgrades. This work combines genetic algorithms (GA) for resource optimization with mathematical optimization techniques for scheduling (e.g., GAMS). This hybrid framework leverages the strengths of GA in solving large, nonlinear problems and the reliability of mathematical optimization in enforcing system constraints, ensuring applicability to real-world, large-scale systems. The proposed approach has been studied with different types of EB charging technologies and is evaluated under various scenarios, considering variations in operational strategy and energy demand. An electric 38‐bus network and a representative multi-route bus service are used to simulate and validate the framework. Results showed that the Genetic Algorithm reliably produced cost-efficient solutions by effectively positioning distributed generation units, selectively upgrading the transmission lines, and optimizing the charger types for multi-routes. The results demonstrate the effectiveness of the framework in minimizing costs while successfully meeting the operational requirements of the bus service and the technical limitations of the electric grid, contributing to the development of efficient and resilient electric transit systems.College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE