Di Pasquale, Antonio (2023) Smart urban rail: power system modelling and development of optimal centralised control strategies. [Tesi di dottorato]
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| Item Type: | Tesi di dottorato |
|---|---|
| Resource language: | English |
| Title: | Smart urban rail: power system modelling and development of optimal centralised control strategies |
| Creators: | Creators Email Di Pasquale, Antonio antonio.dipasquale@unina.it |
| Date: | 12 December 2023 |
| Number of Pages: | 152 |
| Institution: | Università degli Studi di Napoli Federico II |
| Department: | Ingegneria Elettrica e delle Tecnologie dell'Informazione |
| Dottorato: | Information technology and electrical engineering |
| Ciclo di dottorato: | 36 |
| Coordinatore del Corso di dottorato: | nome email Russo, Stefano stefano.russo@unina.it |
| Tutor: | nome email Pagano, Mario UNSPECIFIED |
| Date: | 12 December 2023 |
| Number of Pages: | 152 |
| Keywords: | smart railway system, network receptivity, steady-state analysis, centralised control, optimal power flow |
| Settori scientifico-disciplinari del MIUR: | Area 09 - Ingegneria industriale e dell'informazione > ING-IND/33 - Sistemi elettrici per l'energia |
| Date Deposited: | 12 Dec 2023 10:55 |
| Last Modified: | 04 May 2026 12:34 |
| URI: | http://www.fedoa.unina.it/id/eprint/15635 |
Collection description
This dissertation has two principal objectives. Firstly, it aims to develop an accurate model for the steady-state analysis of DC traction systems. Secondly, it introduces centralised control strategies in line with the modern smart grid concept to enhance conventional local control methods commonly employed in railway applications. In pursuit of the first objective, this research work conducts an in-depth analysis of DC traction systems, offering a comprehensive library of models for stationary analysis employing the well-established power flow approach. This analysis addresses and resolves numerical challenges related to the limited receptivity of the traction network and the loss of the slack bus when regenerative braking power exceeds railway load requirements. With regard to the second objective, it is worth noting that, despite extensive scientific literature on the smart grid concept for buildings, neighbourhoods, and cities, the concept of smart railway systems remains relatively unexplored. As a result, this dissertation lays the foundation for implementing this paradigm in the urban traction power system contexts. It introduces a modern control system where a central controller, informed by data exchange with all sub-systems (i.e., trains, energy storage systems, and substations), defines local controllers’ voltage and power set points according to suitable optimisation strategies. Depending on the system’s configuration, various control strategies, framed as optimal power flow problems, are proposed. Ultimately, the effectiveness of these strategies is assessed through a comprehensive numerical analysis, with real data from Naples Line 1 metro serving as a case study.
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