A comprehensive examination of the technical and economic dimensions for green hydrogen production and its application for mobility purpose is presented in this study. The analysis proposes a hydrogen production tool, algorithms are based in python, that optimize the costs of the overall plant, establishes the capacity mix and derive the expected associated revenues. The framework is applied to the Italian Hydrogen Valley project assessing vary transportation scenarios and hydrogen production plant setup. The study aims to contribute substantially by providing profound insights into sensitivities inherent to the green hydrogen onsite production. These encompass a spectrum of techno-economic sensitivities on the electrolyzer technologies, different renewable energy sources to electrolyze, batteries, island and grid-connect hydrogen production plants. Additionally, the paper provides a future perspective by forecasting the levelized costs of hydrogen in 2030 under conservative assumptions. This meticulous and comprehensive analysis is not only anticipated to significantly contribute to the advancement of hydrogen production plant for the mobility application, but also aspires to establish itself as a foundational resource for decision-maker within the realm of net zero energy scenario.
Una completa esplorazione delle dimensioni tecniche ed economiche per la produzione di idrogeno verde e la sua applicazioni in ambito mobilità è presentata in questo studio. L’analisi propone un tool di produzione dell’idrogeno, i cui algoritmi sono basati su python, che ottimizza il costo dell’impianto, stabilisce le capacità delle diverse componenti e determina i potenziali ricavi generati. Il progetto mira a fornire un contributo significativo offrendo approfondite riflessioni circa le modalità di produzione di idrogeno. Queste comprendono una serie di simulazioni tecno-economiche legate alle tecnologie degli elettrolizzatori, alle fonti di energia rinnovabile utilizzate per la produzione di idrogeno, le batterie, le configurazioni dell’impianto di idrogeno con collegamento o meno alla rete elettrica. Inoltre, l'articolo offre una prospettiva futura del costo di produzione dell’idrogeno al 2030 sulla base di assunzioni conservative. Questa analisi meticolosa e completa non solo è destinata a contribuire in modo significativo allo sviluppo di produzione di idrogeno per l'applicazione nella mobilità, ma aspira anche a diventare una risorsa fondamentale per i decisori verso uno scenario energetico ad emissioni nette nulle.
Optimization of hydrogen production system for mobility end use with a python-based tool
DYUSSENBAYEVA, AKMULDIR
2022/2023
Abstract
A comprehensive examination of the technical and economic dimensions for green hydrogen production and its application for mobility purpose is presented in this study. The analysis proposes a hydrogen production tool, algorithms are based in python, that optimize the costs of the overall plant, establishes the capacity mix and derive the expected associated revenues. The framework is applied to the Italian Hydrogen Valley project assessing vary transportation scenarios and hydrogen production plant setup. The study aims to contribute substantially by providing profound insights into sensitivities inherent to the green hydrogen onsite production. These encompass a spectrum of techno-economic sensitivities on the electrolyzer technologies, different renewable energy sources to electrolyze, batteries, island and grid-connect hydrogen production plants. Additionally, the paper provides a future perspective by forecasting the levelized costs of hydrogen in 2030 under conservative assumptions. This meticulous and comprehensive analysis is not only anticipated to significantly contribute to the advancement of hydrogen production plant for the mobility application, but also aspires to establish itself as a foundational resource for decision-maker within the realm of net zero energy scenario.File | Dimensione | Formato | |
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2023_10_Dyussenbayeva.pdf
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Descrizione: Akmuldir Dyussenbayeva Thesis submission
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https://hdl.handle.net/10589/210583