Phd in ENGINEERING AND APPLIED SCIENCE FOR ENERGY
AND INDUSTRY

Thesis title: Gestione ottimale dell’energia per i sistemi di mobilit`a urbana nel contesto delle smart grid

Power grids are facing a paradigmatic shift, moving away from the traditional centralized, unidirectional flow model. The massive integration of complex loads and distributed resources—such as electric vehicles, intermittent generation from renewable sources like photovoltaics and wind, and the electrification of heating systems—introduces unprecedented challenges. Indeed, the stochastic nature of these resources and uncoordinated demand can induce severe load peaks, voltage fluctuations, and a significant increase in operational costs, undermining the stability and efficiency of the entire infrastructure. This PhD thesis is situated within this transitional scenario, aiming to develop and validate a framework of advanced methodologies for the optimization, monitoring, and management of energy flows in modern smart grids. The proposed solutions are applied and tested across multiple, diverse case studies, demonstrating their robustness and scalability. The research path of this thesis addresses the smart management of complex energy systems, starting from the fundamentals of integrating renewable generation and electric mobility. The analysis begins with local optimization approaches aimed at synchronizing production peaks from photovoltaic systems with the charging demand of electric vehicles. The investigation then proceeds to aggregated charging infrastructures. In this context, a model is developed for optimizing their integration with photovoltaic generation, validated through a case study related to the design of a scalable, resilient, and efficient E-park. Before extending the research to larger systems, the path involved the development and experimental validation of a control device designed to implement domestic load management strategies, intended for users equipped with storage systems and inverters. This system enables smart management of energy flows, optimizing self-consumption from renewable sources and dynamically modulating the load towards the grid to reduce stress on the latter and minimize costs for the end-user. Subsequently, the scope of the investigation touches upon intelligent energy systems through the implementation of a Building Management System (BMS) for the monitoring and coordinated management of complex installations. The innovative core of the thesis lies in the development of a comprehensive framework for Digital Twin systems, applied to experimental contexts in collaboration with the ENEA research center. This phase explores the entire lifecycle of digital twins, from design to implementation, analyzing their architectural complexities, required computational resources, and strategic opportunities for a future energy system. Finally, the principles of optimization and electrical design are applied to the railway transport sector. This last part of the work focuses on the study of innovative technologies and the development of advanced Train Performance algorithms, aimed at optimizing the electrical design and operational efficiency of modern railway systems. In summary, this research path offers an integrated contribution to the realization of future smart cities, providing concrete models and tools for the synergistic management of energy infrastructures and sustainable mobility.