Artificial Intelligence Based Optimal Operation of Green Hydrogen Refueling

This study presents an AI-based optimized energy management model for a hydrogen refueling station with a PV system and fuel cell (FC). Using the Time-series Dense Encoder (TiDE) model for day-ahead PV generation and electricity price forecasting, with real data from Spain, the station efficiently manages hydrogen demand and electricity sales. Despite lower-than-expected PV production (11,559.39 kWh vs. 12,991.71 kWh), real-time revenue rose to 1,144.84 Euros, demonstrating operational flexibility and profit.

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Publication Details

Published
2024-11-26
DOI
https://doi.org/10.1109/telfor63250.2024.10819143
Citations
3
Primary Topic
Spacecraft and Cryogenic Technologies
Type
article
Field-Weighted Citation Impact
3.90
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article

Artificial Intelligence Based Optimal Operation of Green Hydrogen Refueling

Alper Çiçek, Oğuz Kırat
3 citations
Spacecraft and Cryogenic Technologies
3.90
article

Artificial Intelligence Based Optimal Operation of Green Hydrogen Refueling

Alper Çiçek, Oğuz Kırat
article en
3 citations

Abstract

This study presents an AI-based optimized energy management model for a hydrogen refueling station with a PV system and fuel cell (FC). Using the Time-series Dense Encoder (TiDE) model for day-ahead PV generation and electricity price forecasting, with real data from Spain, the station efficiently manages hydrogen demand and electricity sales. Despite lower-than-expected PV production (11,559.39 kWh vs. 12,991.71 kWh), real-time revenue rose to 1,144.84 Euros, demonstrating operational flexibility and profit.

Trakya University (TR)
Openalex Percentile: Top 6%
Spacecraft and Cryogenic Technologies
3.90
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