Edge/cloud virtualization techniques and resources allocation algorithms for IoT-based smart energy applications.

Raffa, Viviana (2021) Edge/cloud virtualization techniques and resources allocation algorithms for IoT-based smart energy applications. [Laurea magistrale], Università di Bologna, Corso di Studio in Informatica [LM-DM270]
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Nowadays, the installation of residential battery energy storage (BES) has increased as a consequence of the decrease in the cost of batteries. The coupling of small-scale energy generation (residential PV) and residential BES promotes the integration of microgrids (MG), i.e., clusters of local energy sources, energy storages, and customers which are represented as a single controllable entity. The operations between multiple grid-connected MGs and the distribution network can be coordinated by controlling the power exchange; however, in order to achieve this level of coordination, a control and communication MG interface should be developed as an add-on DMS (Distribution Management System) functionality to integrate the MG energy scheduling with the network optimal power flow. This thesis proposes an edge-cloud architecture that is able to integrate the microgrid energy scheduling method with the grid constrained power flow, as well as providing tools for controlling and monitoring edge devices. As a specific case study, we consider the problem of determining the energy scheduling (amount extracted/stored from/in batteries) for each prosumer in a microgrid with a certain global objective (e.g. to make a few energy exchanges as possible with the main grid). The results show that, in order to have better optimization of the BES scheduling, it is necessary to evaluate the composition of a microgrid in such a way as to have balanced deficits and surpluses, which can be performed with Machine Learning (ML) techniques based on past production and consumption data for each prosumer.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Raffa, Viviana
Relatore della tesi
Correlatore della tesi
Corso di studio
Curriculum C: Sistemi e reti
Ordinamento Cds
Parole chiave
Battery Energy Storage,Edge-cloud computing,Energy management system,Energy scheduling,Energy sources,Energy trading,Internet of Things,Message broker,Photovoltaic,Renewable Energy Source,Smart energy,Smart grid,Time series
Data di discussione della Tesi
18 Marzo 2021

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