In this study, a Mixed-Integer Linear Programming (MILP)-based energy cost optimiza- tion framework is proposed for smart railway hub that integrate multiple energy tech- nologies, including rooftop photovoltaic (PV) systems, energy storage systems (ESS), and electric vehicle (EV) charging infrastructure. The proposed model focuses on minimizing the total daily energy cost of a railway substation while ensuring operational reliability and system flexibility. The energy hub includes AC charging stations for private EVs and DC fast charging points dedicated to electric buses. Additionally, the model considers the regenerative braking energy (RBE) of trains as a recoverable energy source within the system. Different scenarios are analyzed to compare the economic performance and energy management efficiency under various scenarios, including grid-connected and semiislanded modes. The MILP formulation accounts for time-of-use electricity tariffs, ESS charge/discharge cycles, and solar generation profiles, ensuring optimal energy flows between system components. San Donato station is cho- sen as a case study. Results highlight the benefits of coordinated energy management strategies in reducing dependency on grid energy and lowering operational costs. Moreover, the impact of RBE utilization and charging EVs from this is thoroughly eval- uated. The proposed approach demonstrates significant potential for improving the sus- tainability and cost-effectiveness of next generation railway energy hubs.
Inquestostudio, vienepropostounframeworkdiottimizzazionedeicostienergeticibasato sulla Programmazione Lineare Intera Mista (MILP) per un hub ferroviario intelligente che integra diverse tecnologie energetiche, tra cui sistemi fotovoltaici (FV) su tetto, sistemi di accumulo di energia (ESS) e infrastrutture di ricarica per veicoli elettrici (EV). Il modello proposto si concentra sulla minimizzazione del costo energetico giornaliero totale di una sottostazione ferroviaria, garantendo al contempo l’affidabilità operativa e la flessibilità del sistema. L’hub energetico include stazioni di ricarica in corrente alternata (CA) per veicoli elettrici privati e punti di ricarica rapida in corrente continua (CC) dedicati agli autobus elettrici. Inoltre, il modello considera l’energia di frenata rigenerativa (RBE) dei treni come fonte di energia recuperabile all’interno del sistema. Vengono analizzati diversi scenari per confrontareleprestazionieconomicheel’efficienzadellagestioneenergeticainvariscenari, tra cui modalità connesse alla rete e semi-isolate. La formulazione MILP tiene conto delle tariffeelettrichebasatesull’orariodiutilizzo, deiciclidicarica/scaricadell’ESSedeiprofili digenerazionesolare,garantendoflussidienergiaottimalitraicomponentidelsistema. La stazionediSanDonatoèstatasceltacomecasodistudio. Irisultatievidenzianoivantaggi di strategie di gestione energetica coordinate nel ridurre la dipendenza dall’energia di rete e i costi operativi. Inoltre, l’impatto dell’utilizzo di RBE e della ricarica dei veicoli elettrici viene valutato in modo approfondito. L’approccio proposto dimostra un potenziale significativo per miglio- rare la sostenibilità e l’economicità dei centri energetici ferroviari di prossima generazione.
Comparative MILP-based energy cost optimization for smart railway hubs integrating EV charging, PV, and energy storage
YULIANTO, RIAN
2024/2025
Abstract
In this study, a Mixed-Integer Linear Programming (MILP)-based energy cost optimiza- tion framework is proposed for smart railway hub that integrate multiple energy tech- nologies, including rooftop photovoltaic (PV) systems, energy storage systems (ESS), and electric vehicle (EV) charging infrastructure. The proposed model focuses on minimizing the total daily energy cost of a railway substation while ensuring operational reliability and system flexibility. The energy hub includes AC charging stations for private EVs and DC fast charging points dedicated to electric buses. Additionally, the model considers the regenerative braking energy (RBE) of trains as a recoverable energy source within the system. Different scenarios are analyzed to compare the economic performance and energy management efficiency under various scenarios, including grid-connected and semiislanded modes. The MILP formulation accounts for time-of-use electricity tariffs, ESS charge/discharge cycles, and solar generation profiles, ensuring optimal energy flows between system components. San Donato station is cho- sen as a case study. Results highlight the benefits of coordinated energy management strategies in reducing dependency on grid energy and lowering operational costs. Moreover, the impact of RBE utilization and charging EVs from this is thoroughly eval- uated. The proposed approach demonstrates significant potential for improving the sus- tainability and cost-effectiveness of next generation railway energy hubs.File | Dimensione | Formato | |
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https://hdl.handle.net/10589/239478