Cardona Rivera, Ricardo (2022) Modelling and Advanced Network Control of Future Smart Grids. [Tesi di dottorato]
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| Item Type: | Tesi di dottorato |
|---|---|
| Resource language: | English |
| Title: | Modelling and Advanced Network Control of Future Smart Grids |
| Creators: | Creators Email Cardona Rivera, Ricardo ricardo.cardonarivera@unina.it |
| Date: | 10 March 2022 |
| Number of Pages: | 122 |
| 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: | 34 |
| Coordinatore del Corso di dottorato: | nome email Riccio, Daniele daniele.riccio@unina.it |
| Tutor: | nome email di Bernardo, Mario UNSPECIFIED lo Iudice, Francesco UNSPECIFIED |
| Date: | 10 March 2022 |
| Number of Pages: | 122 |
| Keywords: | Power Systems, Control Theory, Complex Networks, Network Control, Intentional Controlled islanding, Network Partitioning, COVID 19, System Identification. |
| Settori scientifico-disciplinari del MIUR: | Area 09 - Ingegneria industriale e dell'informazione > ING-IND/32 - Convertitori, macchine e azionamenti elettrici Area 09 - Ingegneria industriale e dell'informazione > ING-IND/33 - Sistemi elettrici per l'energia Area 09 - Ingegneria industriale e dell'informazione > ING-INF/04 - Automatica Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 - Sistemi di elaborazione delle informazioni |
| Date Deposited: | 22 May 2022 21:24 |
| Last Modified: | 28 Feb 2024 10:59 |
| URI: | http://www.fedoa.unina.it/id/eprint/14428 |
Collection description
The power network is a key critical infrastructure for our everyday life. While there is a wide range of studies dealing with the modelling and control of power network components and their coordination for power generation, the links among different modelling approaches and control strategies are still not clear enough in the control theoretic literature. For this reason, in the first part of this Thesis we provide a review of the different dynamical models of the components of the power grid from a network perspective, the control specifications needed for their functioning and the control layers that fulfill them. As this detailed modelling of the power network can be cumbersome to handle for control design, we review the Swing Equation as a simplification of the frequency dynamics of the power network and provide a comprehensible framework to map each of the components of the power network into a set of parameters of the Swing Equation. This simpler model allows us to introduce additional control problems on the power network such as the secondary frequency control problem and the set-point scheduling problem and we frame these problems into a hierarchical description of the power network control. As the control architecture of the power network cannot always compensate the different disturbances it is subject to, we also discuss last resort strategies to contain failures. Specifically, we introduce the power network islanding problem and the Intentional Controlled Islanding (ICI) strategies found in literature. After this, we provide a novel self-organizing solution to the islanding problem based on the migration of nodes among islands defined by an initial partition of the network. This methods uses a power balance estimator based on virtual consensus dynamics and a distributed migration strategy that uses this estimate to decide the migration. Our method finds, under some assumptions on the network structure and in a finite number of migration steps, a partition of the power network such that the average absolute power imbalance remains within a certain bound from the total power imbalance of the power network and we give an analytical expression for this bound. Finally, we also present work carried out on a different topic which we focused on because of the pandemic, related with the network modelling of the spread of COVID-19 in Italy and the development of possible decentralized containment strategies.
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