Design and Off-design modelling of advanced Power-to-Gas energy storage systems

Power-to-Gas storage systems have the potential to address grid-stability issues that arise when an increasing share of power is generated from sources that have a highly variable output. Although the proof-of-concept of these has been promising, the behaviour of the processes in off-design conditions is not easily predictable. The primary aim of this PhD project was to evaluate the performance of an original Power-to-Gas system, made up of innovative components. To achieve this, a numerical model has been developed to simulate the characteristics and the behaviour of the several components when the whole system is coupled with a renewable source. The developed model has been applied to a large variety of scenarios, evaluating the performance of the considered process and exploiting a limited amount of experimental data. The model has been then used to compare different Power-to-Gas concepts, in a real scenario of functioning. Several goals have been achieved. In the concept phase, the possibility to thermally integrate the high temperature components has been demonstrated. Then, the parameters that affect the energy performance of a Power-to-Gas system coupled with a renewable source have been identified, providing general recommendations on the design of hybrid systems; these parameters are: 1) the ratio between the storage system size and the renewable generator size; 2) the type of coupled renewable source; 3) the related production profile. Finally, from the results of the comparative analysis, it is highlighted that configurations with a highly oversized renewable source with respect to the storage system show the maximum achievable profit.

Towards cheaper, safer and low environmental impact materials for energy storage devices: natural resources-derived carbon based electrodes and aqueous electrolytes for supercapacitors

The growing market of electrical cars, portable electronics, photovoltaic systems..etc. requires the development of efficient, low-cost, and low environmental impact energy storage devices (ESDs) including batteries and supercapacitors.. Due to their extended charge-discharge cycle, high specific capacitance, and power capabilities supercapacitors are considered among the most attractive ESDs. Over the last decade, research and development in supercapacitor technology have accelerated: thousands of articles have been published in the literature describing the electrochemical properties of the electrode materials and electrolyte in addition to separators and current collectors. Carbon-based supercapacitor electrodes materials have gained increasing attention due to their high specific surface area, good electrical conductivity, and excellent stability in harsh environments, as well as other characteristics. Recently, there has been a surge of interest in activated carbon derived from low-cost abundant sources such as biomass for supercapacitor electrode materials. Also, particular attention was given to a major challenging issue concerning the substitution of organic solutions currently used as electrolytes due to their highest electrochemical stability window even though their high cost, toxicity, and flammability. In this regard, the main objective of this thesis is to investigate the performances of supercapacitors using low cost abundant safe, and low environmental impact materials for electrodes and electrolytes. Several prototypes were constructed and tested using natural resources through optimization of the preparation of appropriate carbon electrodes using agriculture by-products waste or coal (i.e. Argan shell or Anthracite from Jerrada). Such electrodes were tested using several electrolyte formulations (aqueous and water in salt electrolytes) beneficing their non-flammability, lower cost, and environmental impact; the characteristics that provide a promising opportunity to design safer, inexpensive, and environmentally friendly devices compared to organic electrolytes.

Il modello Positive Energy Districts. Approcci innovativi e strumenti operativi per la transizione verso città clima-neutrali

Ad oggi le città europee si configurano come i principali centri di cultura, innovazione e sviluppo economico. Tuttavia, ospitando circa il 75% della popolazione e consumando quasi l’80% dell’energia prodotta, a causa delle significative emissioni di gas serra esse contribuiscono in modo rilevante ai cambiamenti climatici e, allo stesso tempo, ne subiscono gli effetti più intensi. La Comunità Europea ha preso atto della necessità di intraprendere un’azione sinergica che adotti strategie di mitigazione climatica e preveda misure di adattamento per far fronte agli impatti climatici ormai inevitabili. L'orientamento dei Programmi europei di Ricerca e Innovazione sul tema delle città smart e clima-neutrali sposta l'attenzione dalla dimensione urbana verso la scala di distretto. In questa prospettiva, i Positive Energy Districts (PEDs) si configurano come distretti di nuova edificazione, ma anche come soluzioni ambiziose per la riqualificazione di quartieri esistenti che gestiscono in modo attivo il fabbisogno energetico con un bilancio nullo di emissioni e un surplus di energia prodotta da rinnovabili. La ricerca di dottorato focalizza l’indagine sul modello PEDs esplorandone il potenziale di applicabilità nel contesto urbano consolidato. Nello specifico, la tesi lavora allo sviluppo di due contributi di ricerca originali: il PED-Portfolio e il PED-Toolkit. Tali contributi propongono un approccio sistemico, attraverso il quale intraprendere un percorso di conoscenza e sperimentazione del modello PEDs in una prospettiva di riduzione del fabbisogno energetico, ma anche in un’ottica di migliore accessibilità, vivibilità e resilienza di questi distretti. Al fine di verificare l’applicabilità dei risultati della ricerca, gli strumenti sviluppati vengono testati su un’area pilota e gli esiti di tale sperimentazione sono poi messi a confronto con il quadro dello stato dell’arte e con le principali linee di ricerca internazionali sul tema PEDs, affinando gli esiti del progetto di dottorato in un processo di ricerca-sperimentazione-ricerca.

Machine learning as a service for high energy physics (MLaaS4HEP): a service for ML-based data analyses

With the CERN LHC program underway, there has been an acceleration of data growth in the High Energy Physics (HEP) field and the usage of Machine Learning (ML) in HEP will be critical during the HL-LHC program when the data that will be produced will reach the exascale. ML techniques have been successfully used in many areas of HEP nevertheless, the development of a ML project and its implementation for production use is a highly time-consuming task and requires specific skills. Complicating this scenario is the fact that HEP data is stored in ROOT data format, which is mostly unknown outside of the HEP community. The work presented in this thesis is focused on the development of a ML as a Service (MLaaS) solution for HEP, aiming to provide a cloud service that allows HEP users to run ML pipelines via HTTP calls. These pipelines are executed by using the MLaaS4HEP framework, which allows reading data, processing data, and training ML models directly using ROOT files of arbitrary size from local or distributed data sources. Such a solution provides HEP users non-expert in ML with a tool that allows them to apply ML techniques in their analyses in a streamlined manner. Over the years the MLaaS4HEP framework has been developed, validated, and tested and new features have been added. A first MLaaS solution has been developed by automatizing the deployment of a platform equipped with the MLaaS4HEP framework. Then, a service with APIs has been developed, so that a user after being authenticated and authorized can submit MLaaS4HEP workflows producing trained ML models ready for the inference phase. A working prototype of this service is currently running on a virtual machine of INFN-Cloud and is compliant to be added to the INFN Cloud portfolio of services.

Advanced statistical models for the segregation, identification and measurement of coexisting sound sources

Long-term monitoring of acoustical environments is gaining popularity thanks to the relevant amount of scientific and engineering insights that it provides. The increasing interest is due to the constant growth of storage capacity and computational power to process large amounts of data. In this perspective, machine learning (ML) provides a broad family of data-driven statistical techniques to deal with large databases. Nowadays, the conventional praxis of sound level meter measurements limits the global description of a sound scene to an energetic point of view. The equivalent continuous level Leq represents the main metric to define an acoustic environment, indeed. Finer analyses involve the use of statistical levels. However, acoustic percentiles are based on temporal assumptions, which are not always reliable. A statistical approach, based on the study of the occurrences of sound pressure levels, would bring a different perspective to the analysis of long-term monitoring. Depicting a sound scene through the most probable sound pressure level, rather than portions of energy, brought more specific information about the activity carried out during the measurements. The statistical mode of the occurrences can capture typical behaviors of specific kinds of sound sources. The present work aims to propose an ML-based method to identify, separate and measure coexisting sound sources in real-world scenarios. It is based on long-term monitoring and is addressed to acousticians focused on the analysis of environmental noise in manifold contexts. The presented method is based on clustering analysis. Two algorithms, Gaussian Mixture Model and K-means clustering, represent the main core of a process to investigate different active spaces monitored through sound level meters. The procedure has been applied in two different contexts: university lecture halls and offices. The proposed method shows robust and reliable results in describing the acoustic scenario and it could represent an important analytical tool for acousticians.