Cost-optimized replacement strategies for water electrolysis systems affected by degradation

2025-08-22

AI Summary

This study addresses the increased levelized cost of hydrogen (LCOH) over the system lifetime caused by electrolyzer stack degradation in proton exchange membrane (PEM) water electrolysis. We propose a linear optimization-based replacement strategy grounded in a degradation-threshold criterion. A load-dependent degradation model is developed to quantify how uncertainty in degradation rate affects optimal replacement timing; results show that variations in degradation scaling can shift the optimal replacement time by up to nine years. The method jointly optimizes degradation dynamics, operational load profiles, degradation rates, and equipment costs to identify dynamic, LCOH-minimizing replacement windows. By integrating physics-informed degradation modeling with economic optimization, the framework enables precise, data-driven decisions on stack replacement. The approach extends electrolyzer stack service life and reduces green hydrogen production costs, thereby enhancing the economic viability and commercial scalability of electrolytic hydrogen projects.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Non-convex Optimization
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

A key factor in reducing the cost of green hydrogen production projects using water electrolysis systems is to minimize the degradation of the electrolyzer stacks, as this impacts the lifetime of the stacks and therefore the frequency of their replacement. To create a better understanding of the economics of stack degradation, we present a linear optimization approach minimizing the costs of a green hydrogen supply chain including an electrolyzer with degradation modeling. By calculating the levelized cost of hydrogen depending on a variable degradation threshold, the cost optimal time for stack replacement can be identified. We further study how this optimal time of replacement is affected by uncertainties such as the degradation scale, the load-dependency of both degradation and energy demand, and the costs of the electrolyzer. The variation of the identified major uncertainty degradation scale results in a difference of up to 9 years regarding the cost optimal time for stack replacement, respectively lifetime of the stacks. Therefore, a better understanding of the degradation impact is imperative for project cost reductions, which in turn would support a proceeding hydrogen market ramp-up.

Problem

• Optimizing electrolyzer replacement timing to minimize hydrogen production costs
• Modeling degradation impact on stack lifetime and replacement frequency
• Assessing uncertainty effects on cost-optimal stack replacement strategies

Innovation

• Linear optimization for cost minimization
• Degradation threshold-based replacement timing
• Uncertainty analysis on degradation scale