The aging Italian building stock has a high energy consumption, that heavily depends on natural gas, causing 18% of national greenhouse gas emissions. Following ambitious EU legislation, energy renovations are supported in Italy through public incentives. However, homeowners seeking to upgrade their home energy systems face uncertainty on which measures to implement, as the economic and environmental impacts of these changes can be unclear. This thesis presents the development of an energy system optimization model aimed at facilitating decision-making for energy renovations in residential buildings. The methodological approach centers on a Mixed-Integer Linear Programming model within a Supply and Use Input-Output framework, implemented using the pyESM Python package. The model is designed as a multi-carrier system to address the diverse energy demands within households. Demand profiles are constructed from coarse consumption data, while solar resource availability is quantified by aggregating time series through k-means clustering. Various technologies are simulated within the model to determine optimal configurations that balance investment costs with operational expenses over time. The cost-optimal domestic energy system is studied for 12 different house archetypes in Italy, varying in size and location. Alongside the baseline scenario, the robustness of key parameters is tested, particularly by assessing the effects of changes in electricity and natural gas prices and of reducing investment costs through government incentives. The results indicate that the high upfront costs of energy-efficient technologies pose a significant barrier to their adoption. However, this barrier can be overcome with public incentives that reduce the investment burden or if the intervention is considered from a multi-decade time perspective. Heating needs are the primary factor affecting decarbonization potential, especially for large homes in colder climates. Although the adoption of electricity-powered appliances is a key enabler for decarbonization, the overall impact depends on the carbon intensity of the power grid.
Il settore residenziale in Italia è responsabile del 18% delle emissioni di gas serra, a causa di un patrimonio edilizio vetusto che porta a elevati consumi energetici. Alla luce dell’ambiziosa legislazione dell’Unione Europea, le ristrutturazioni energetiche in Italia sono supportate tramite incentivi fiscali. Tuttavia, i proprietari di immobili che desiderano rinnovare i propri sistemi energetici domestici spesso esitano di fronte alla poca chiarezza sugli impatti economici e ambientali. Questa tesi propone un modello di ottimizzazione volto a facilitare il processo decisionale per ristrutturazioni energetiche di edifici residenziali. L’approccio metodologico si basa su un modello di Programmazione Lineare Intera Mista all'interno del framework Input-Output Supply and Use, implementato tramite il pacchetto Python pyESM. Il modello è progettato come un sistema multi-vettore per rispondere alle diverse esigenze energetiche delle abitazioni. I profili di domanda sono costruiti a partire da dati di consumo grezzi, mentre il profilo di produzione da fotovoltaico è ottenuto tramite l’aggregazione di serie temporali. Diverse tecnologie sono simulate per identificare configurazioni ottimali che bilancino i costi di investimento con le spese operative nel lungo termine. Viene studiata la configurazione ottimale in termini di costi di 12 differenti archetipi di abitazioni in Italia, diversi per dimensione e localizzazione. La robustezza dei parametri chiave è testata, valutando in particolare gli effetti delle variazioni nei prezzi di elettricità e gas naturale, e della riduzione dei costi d’investimento tramite incentivi statali. I risultati indicano che gli elevati costi iniziali delle tecnologie energicamente efficienti costituiscono un ostacolo significativo alla loro adozione. Tuttavia, tale ostacolo può essere superato tramite incentivi fiscali che riducano l'onere dell'investimento o adottando una prospettiva temporale di più decenni. Emerge che sebbene l'installazione di apparecchiature alimentate a elettricità sia un elemento chiave per la decarbonizzazione, l'impatto complessivo dipende dall'intensità di carbonio della rete elettrica nazionale.
Development of an energy system optimization model to support decision-making in energy retrofitting of residential buildings
Citterio, Camilla
2023/2024
Abstract
The aging Italian building stock has a high energy consumption, that heavily depends on natural gas, causing 18% of national greenhouse gas emissions. Following ambitious EU legislation, energy renovations are supported in Italy through public incentives. However, homeowners seeking to upgrade their home energy systems face uncertainty on which measures to implement, as the economic and environmental impacts of these changes can be unclear. This thesis presents the development of an energy system optimization model aimed at facilitating decision-making for energy renovations in residential buildings. The methodological approach centers on a Mixed-Integer Linear Programming model within a Supply and Use Input-Output framework, implemented using the pyESM Python package. The model is designed as a multi-carrier system to address the diverse energy demands within households. Demand profiles are constructed from coarse consumption data, while solar resource availability is quantified by aggregating time series through k-means clustering. Various technologies are simulated within the model to determine optimal configurations that balance investment costs with operational expenses over time. The cost-optimal domestic energy system is studied for 12 different house archetypes in Italy, varying in size and location. Alongside the baseline scenario, the robustness of key parameters is tested, particularly by assessing the effects of changes in electricity and natural gas prices and of reducing investment costs through government incentives. The results indicate that the high upfront costs of energy-efficient technologies pose a significant barrier to their adoption. However, this barrier can be overcome with public incentives that reduce the investment burden or if the intervention is considered from a multi-decade time perspective. Heating needs are the primary factor affecting decarbonization potential, especially for large homes in colder climates. Although the adoption of electricity-powered appliances is a key enabler for decarbonization, the overall impact depends on the carbon intensity of the power grid.| File | Dimensione | Formato | |
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https://hdl.handle.net/10589/230522