Phd in ENGINEERING AND APPLIED SCIENCE FOR ENERGY
AND INDUSTRY

Thesis title: A digital twin and a distributed monitoring system to support the green transition in electricity distribution networks

This thesis addresses the unprecedented transformation occurring in electric power distribution networks, driven by the increasing integration of renewable energy sources, the emergence of prosumers, and the growing need for network flexibility. Traditional passive distribution networks are evolving into active, dynamic entities that require sophisticated monitoring and control capabilities. This evolution is accelerated by digital technologies such as Digital Twins, the Internet of Things (IoT), and smart monitoring systems, complemented by computational tools including Python programming and OpenDSS simulation environments. The research bridges the gap between theoretical concepts and the practical implementation of digital solutions for modern distribution networks, focusing on Digital Twin technology and flexibility management. A comprehensive case study at ASM Terni S.p.A. provides real-world validation of these solutions through implementation and empirical testing. The research makes several key contributions to the field: 1) development of a robust framework for implementing Digital Twin technology in power distribution networks; 2) practical observation, comparison, and validation of smart monitoring device measurements (note: smart monitoring devices were not developed in the doctoral work; the thesis reports an analysis of measurement accuracy and benefits for Distribution System Operators); 3) development and evaluation of a Decision Support System for Flexibility Exploitation (DSSFE) that addresses congestion management while accounting for user behavior uncertainty; and 4) integration of IoT technologies to enable advanced monitoring and control capabilities. The methodology combines theoretical analysis with practical implementation and validation, leveraging a comprehensive literature review and real-time monitoring data. Results demonstrate the practical viability of the proposed solutions while highlighting areas requiring further development. The research opens promising avenues for future exploration, including the integration of artificial intelligence with Digital Twin frameworks and refined management strategies for flexibility provision based on user behavior patterns.