Experimental demonstration of dynamic demand response scheduling for PEM-electrolyzers

The use of renewable energy sources, such as wind power and photovoltaics is expected to produce fluctuating electricity prices. These fluctuations give PEM electrolyzers the opportunity to reduce costs, as they can adapt their production rates rapidly. Moreover, typically slow temperature dynamics of electrolyzers increase their flexibility for effective operational management strategies. With a defined temperature trajectory during scheduling optimization, overload operation of the electrolyzer for a given amount of time is possible. However, the temperature dynamics are typically nonlinear. In conjunction with discrete on/off decisions, temperature dynamics lead to mixed-integer nonlinear optimization problems for scheduling that are highly challenging to solve in real time. In this study, we experimentally validate the dynamic ramping scheduling optimization method that precisely linearizes nonlinear temperature dynamics using a flatness-based coordinate transformation. Utilizing the available information from the dynamic scheduling optimization a 100 kW PEM electrolyzer was operated by studying three stack temperature control methods, rejecting disturbances from load variations. Identifying a suitable control method was essential to guarantee the desired temperature tracking performance of the optimization. Our experiments show a 3.8 % cost reduction compared to the benchmark without overload operation. The designed PEM electrolyzer model also deviated only 0.6 % in costs from the experiment. Simulative scaling of PEM electrolysis to 2 MW demonstrates even higher cost reductions with the dynamic ramping method, as the larger electrolyzer has slower dynamics. • Enhanced economic viability of PEM electrolyzers under fluctuating electricity market conditions. • Experimental validation of real-time scheduling optimization for transient operation in PEM electrolyzers. • Improved cost efficiency and scalability of 2 MW industrial-scale PEM electrolyzers through simulation-based evaluation. • Discussion on potential degradation effects induced by overload and temperature conditions, and their implications on the dynamic ramping method.

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Publication Details

Journal
Applied Energy
Published
2025-05-16
DOI
https://doi.org/10.1016/j.apenergy.2025.126014
Citations
9
Primary Topic
Hybrid Renewable Energy Systems
Type
article
Field-Weighted Citation Impact
4.79

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article

Experimental demonstration of dynamic demand response scheduling for PEM-electrolyzers

Florian Joseph Baader, Ralf Peters, André Bardow, Martin Johannes Müller et al.
9 citations
Applied Energy
Hybrid Renewable Energy Systems
4.79
article

Experimental demonstration of dynamic demand response scheduling for PEM-electrolyzers

Florian Joseph Baader, Ralf Peters, André Bardow, Martin Johannes Müller, Roger Keller
article en
9 citations

Abstract

The use of renewable energy sources, such as wind power and photovoltaics is expected to produce fluctuating electricity prices. These fluctuations give PEM electrolyzers the opportunity to reduce costs, as they can adapt their production rates rapidly. Moreover, typically slow temperature dynamics of electrolyzers increase their flexibility for effective operational management strategies. With a defined temperature trajectory during scheduling optimization, overload operation of the electrolyzer for a given amount of time is possible. However, the temperature dynamics are typically nonlinear. In conjunction with discrete on/off decisions, temperature dynamics lead to mixed-integer nonlinear optimization problems for scheduling that are highly challenging to solve in real time. In this study, we experimentally validate the dynamic ramping scheduling optimization method that precisely linearizes nonlinear temperature dynamics using a flatness-based coordinate transformation. Utilizing the available information from the dynamic scheduling optimization a 100 kW PEM electrolyzer was operated by studying three stack temperature control methods, rejecting disturbances from load variations. Identifying a suitable control method was essential to guarantee the desired temperature tracking performance of the optimization. Our experiments show a 3.8 % cost reduction compared to the benchmark without overload operation. The designed PEM electrolyzer model also deviated only 0.6 % in costs from the experiment. Simulative scaling of PEM electrolysis to 2 MW demonstrates even higher cost reductions with the dynamic ramping method, as the larger electrolyzer has slower dynamics. • Enhanced economic viability of PEM electrolyzers under fluctuating electricity market conditions. • Experimental validation of real-time scheduling optimization for transient operation in PEM electrolyzers. • Improved cost efficiency and scalability of 2 MW industrial-scale PEM electrolyzers through simulation-based evaluation. • Discussion on potential degradation effects induced by overload and temperature conditions, and their implications on the dynamic ramping method.

Applied EnergyVol. 393
Forschungszentrum Jülich (DE), ETH Zurich (CH), Ruhr University Bochum (DE)
Bundesamt für Energie
Affordable and clean energy
Openalex Percentile: Top 5%
Hybrid Renewable Energy Systems
4.79
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