Analysis of the Dynamic System Behavior of AEL for Direct RE Utilization

The speed of alkaline electrolysis (AEL) is a key factor in the efficient utilization of renewable energy. In the “AEL-DynamicsOptimization” project, we investigated the dynamic system behavior of AEL plants when directly coupled with wind and PV power. The goal was to identify opportunities for technical optimization and reduce hydrogen production costs. Fraunhofer ISE provided practical insights for manufacturers and operators of H₂ plants.

Initial Situation

Alkaline electrolysers respond more slowly to fluctuations in the electricity supply than PEM systems, which can lead to hydrogen losses and higher costs. Until now, it was unclear which system parameters are critical for the economical use of directly fed-in PV and wind power. Furthermore, there is a lack of reliable analyses of startup times, ramp rates, and purging processes, which significantly influence the efficiency and costs of an AEL.

Objective

The aim of the project was to analyze the dynamic behavior of AEL systems under real-world renewable energy feed-in conditions. The study sought to determine the extent to which the AEL must track fluctuating power sources, identify the key cost drivers, and assess whether high response speeds – such as those required for PEM electrolysis – are actually necessary.

Approach

Fraunhofer ISE modeled the AEL plant, including all operating modes, in accordance with the manufacturer’s specifications. Optimized capacities for PV and wind farms were derived using location-specific generation time series. Sensitivity analyses examined ramp rates (up/down), N₂ purging processes, and startup times to quantify the effects on hydrogen production and costs.

Results

This illustration shows an example of the system design results for a specific location. The focus is on identifying the cost optimum for the design point as a function of the installed renewable energy capacity.

Key Aspects of the Analysis:

• Composition of LCOH: The hydrogen production costs are broken down in detail according to their main drivers:
  • Electrolysis system
  • Compression and cooling systems
  • Other system components
  • Proportion of renewable energy supplied
• Scenario analysis: To illustrate the sensitivity of costs, three different cost scenarios were examined. These demonstrate how external parameters influence the economic viability of the overall system.

• Optimization point: The diagram illustrates the correlation between the scaling of renewable energy and the resulting levelized cost of hydrogen, making it possible to precisely identify the point of highest cost efficiency (minimum LCOH) for system design.

Detailed results of the sensitivity analyses regarding startup times, ramp rates, and purging processes cannot be published for confidentiality reasons.

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