Incorporating intra-hour renewable variability into the design of stand-alone electrolytic hydrogen production systems

The integration of variable renewable energy sources (VRE) and electrolytic hydrogen production is a key factor in the cost-effective production of hydrogen. The challenge lies in designing systems where electrolyzers can flexibly adapt to the inherent temporal variability of VRE sources. This relationship is difficult to model, as it involves phenomena across a wide range of timescales, from sub-hourly fluctuations to long-term operational dynamics. In this work, we propose a mathematical model to represent the behavior of a renewable hydrogen production system over its lifetime, incorporating sub-hourly information on the balance between renewable generation and the flexibility of the electrolysis stage. Our approach is unique because it takes detailed sub-hourly behavior and efficiently embeds it into a standard hourly framework. It allows us to realistically model the complex interplay between VRE generation and electrolyzer flexibility over a system’s entire lifespan. The model is evaluated at four locations in Chile – two wind-based and two solar PV-based – and a variety of scenarios are constructed considering factors such as the temporal hydrogen dispatch profile, the flexibility of the alkaline electrolysis stage, and the electrolysis technology mix. Based on projected costs for the year 2030, the main results show that the levelized cost of hydrogen across the four sites ranges from $2.96 to $7.59/kg H 2 , although these values can be significantly affected by the temporal profiles of hydrogen dispatch. It was found that modeling intra-hourly characteristics can alter levelized cost predictions by up to 10 %. We also found that the optimal technology mix is influenced by the renewable resource. Specifically, wind-based sites need more flexible proton exchange membrane electrolysis capacity to handle their high sub-hour variability, whereas sites with less variable photovoltaic sources can rely more on alkaline technology. • We propose an optimization model for minimizing the cost of an off-grid renewable hydrogen production system. • Our model incorporates sub-hourly information into an hourly-resolution framework. • Sub-hourly data is used to balance renewable generation with flexible electrolysis. • Levelized cost of hydrogen ranges from $2.96 to $7.59/kg H 2 , in our case study. • Modeling intra-hourly characteristics impacts levelized cost estimates by up to 10 %.

Authors

Publication Details

Journal
Applied Energy
Published
2025-11-24
DOI
https://doi.org/10.1016/j.apenergy.2025.126982
Primary Topic
Hybrid Renewable Energy Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Incorporating intra-hour renewable variability into the design of stand-alone electrolytic hydrogen production systems

Enzo Sauma, Pedro Vargas-Ferrer, Francisca Jalil-Vega
Applied Energy
Hybrid Renewable Energy Systems
article

Incorporating intra-hour renewable variability into the design of stand-alone electrolytic hydrogen production systems

Enzo Sauma, Pedro Vargas-Ferrer, Francisca Jalil-Vega
article en

Abstract

The integration of variable renewable energy sources (VRE) and electrolytic hydrogen production is a key factor in the cost-effective production of hydrogen. The challenge lies in designing systems where electrolyzers can flexibly adapt to the inherent temporal variability of VRE sources. This relationship is difficult to model, as it involves phenomena across a wide range of timescales, from sub-hourly fluctuations to long-term operational dynamics. In this work, we propose a mathematical model to represent the behavior of a renewable hydrogen production system over its lifetime, incorporating sub-hourly information on the balance between renewable generation and the flexibility of the electrolysis stage. Our approach is unique because it takes detailed sub-hourly behavior and efficiently embeds it into a standard hourly framework. It allows us to realistically model the complex interplay between VRE generation and electrolyzer flexibility over a system’s entire lifespan. The model is evaluated at four locations in Chile – two wind-based and two solar PV-based – and a variety of scenarios are constructed considering factors such as the temporal hydrogen dispatch profile, the flexibility of the alkaline electrolysis stage, and the electrolysis technology mix. Based on projected costs for the year 2030, the main results show that the levelized cost of hydrogen across the four sites ranges from $2.96 to $7.59/kg H 2 , although these values can be significantly affected by the temporal profiles of hydrogen dispatch. It was found that modeling intra-hourly characteristics can alter levelized cost predictions by up to 10 %. We also found that the optimal technology mix is influenced by the renewable resource. Specifically, wind-based sites need more flexible proton exchange membrane electrolysis capacity to handle their high sub-hour variability, whereas sites with less variable photovoltaic sources can rely more on alkaline technology. • We propose an optimization model for minimizing the cost of an off-grid renewable hydrogen production system. • Our model incorporates sub-hourly information into an hourly-resolution framework. • Sub-hourly data is used to balance renewable generation with flexible electrolysis. • Levelized cost of hydrogen ranges from $2.96 to $7.59/kg H 2 , in our case study. • Modeling intra-hourly characteristics impacts levelized cost estimates by up to 10 %.

Applied EnergyVol. 404
Openalex Percentile: Top 62%
Hybrid Renewable Energy Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.