This thesis examines the decarbonization of the Italian industrial sector by 2050 through a multi-node energy system model with high temporal and spatial resolution. A novel database is introduced, mapping site-level energy consumption and emissions for major industrial facilities across Italy, categorized by temperature ranges (<100°C, 100–500°C, 500–1000°C, >1000°C) and disaggregated by fuel type. This detailed dataset provides granular insight into process-specific energy uses, including feedstock-driven emissions within hard-to-abate subsectors. By capturing the nuances of industrial energy demand, the database enables the model to represent technology substitution more accurately. Using the Open Energy Modelling Framework (Oemof), the research performs both dispatch and capacity expansion optimizations to identify cost-effective pathways for reducing CO₂ emissions. Following the validation of a 2022 baseline scenario, the impact of the energy transition on Italy’s industrial sector is investigated through ten future scenarios. Each scenario imposes a specific CO₂ emission limit for the year 2050—aligned with Italy’s climate objectives—and uses an open capacity expansion approach. Key model variables, including renewable generation, industrial heat technologies, storage systems, and powerlines, are endogenously optimized to meet demand at the lowest total cost while adhering to stringent carbon constraints. The system is configured across seven electricity market zones, allowing for regional differentiation in resource availability, infrastructure constraints, and industrial energy profiles.
La presente tesi analizza la decarbonizzazione del settore industriale italiano entro il 2050 mediante un modello di sistema energetico multi-nodo, caratterizzato da un’elevata risoluzione temporale e spaziale. Viene introdotto un nuovo database che mappa i consumi energetici e le emissioni a livello di singolo impianto per i principali stabilimenti industriali in Italia, suddivisi in base a quattro fasce di temperatura (<100°C, 100–500°C, 500–1000°C, >1000°C) e differenziati per tipologia di combustibile. Grazie a questo dataset dettagliato, che include anche le emissioni legate alle reazioni di processo nei sottosettori “hard-to-abate”, è possibile catturare con precisione le specificità della domanda energetica industriale e simulare in modo più accurato la transizione energetica. Utilizzando l’Open Energy Modelling Framework (Oemof), la ricerca performa ottimizzazioni sia di dispacciamento sia di espansione di capacità per individuare soluzioni economicamente ottimali nella riduzione delle emissioni di CO₂. Dopo aver validato uno scenario di base riferito al 2022, si analizza l’impatto della transizione energetica sul settore industriale italiano in dieci scenari futuri. Ciascuno di essi prevede un limite alle emissioni di CO₂ da raggiungere nel 2050, in linea con gli obiettivi climatici nazionali. Le principali variabili di modello, come la produzione rinnovabile, le tecnologie per la generazione di calore industriale, i sistemi di accumulo e le reti di trasmissione, vengono ottimizzate in modo endogeno per soddisfare la domanda energetica al minor costo totale, rispettando al contempo vincoli stringenti sulle emissioni di carbonio. Il sistema è articolato in sette zone del mercato elettrico, consentendo di cogliere le differenze regionali in termini di risorse disponibili, vincoli strutturali e profili di domanda industriale.
Decarbonizing the italian industrial sector: a high temporal and saptial resolution optimization model with granular industrial demand disaggregation for carbon-neutral pathways
Bernelli-Zazzera, Enrico
2023/2024
Abstract
This thesis examines the decarbonization of the Italian industrial sector by 2050 through a multi-node energy system model with high temporal and spatial resolution. A novel database is introduced, mapping site-level energy consumption and emissions for major industrial facilities across Italy, categorized by temperature ranges (<100°C, 100–500°C, 500–1000°C, >1000°C) and disaggregated by fuel type. This detailed dataset provides granular insight into process-specific energy uses, including feedstock-driven emissions within hard-to-abate subsectors. By capturing the nuances of industrial energy demand, the database enables the model to represent technology substitution more accurately. Using the Open Energy Modelling Framework (Oemof), the research performs both dispatch and capacity expansion optimizations to identify cost-effective pathways for reducing CO₂ emissions. Following the validation of a 2022 baseline scenario, the impact of the energy transition on Italy’s industrial sector is investigated through ten future scenarios. Each scenario imposes a specific CO₂ emission limit for the year 2050—aligned with Italy’s climate objectives—and uses an open capacity expansion approach. Key model variables, including renewable generation, industrial heat technologies, storage systems, and powerlines, are endogenously optimized to meet demand at the lowest total cost while adhering to stringent carbon constraints. The system is configured across seven electricity market zones, allowing for regional differentiation in resource availability, infrastructure constraints, and industrial energy profiles.File | Dimensione | Formato | |
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Executive_Summary_Bernelli.pdf
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Descrizione: Executive Summary della tesi "Decarbonizing the Italian Industrial Sector: A High Temporal and Spatial Resolution Optimization Model with Granular Industrial Demand Disaggregation for Carbon-Neutral Pathways"
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Thesis_Bernelli_Zazzera.pdf
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Descrizione: Thesis: "Decarbonizing the Italian Industrial Sector: A High Temporal and Spatial Resolution Optimization Model with Granular Industrial Demand Disaggregation for Carbon-Neutral Pathways"
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https://hdl.handle.net/10589/236237