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Cost Optimized Scheduling in Modular Electrolysis Plants

Vincent Henkel, Maximilian Kilthau, Felix Gehlhoff, Lukas Wagner, Alexander Fay

TL;DR

The paper tackles the challenge of coordinating many heterogeneous electrolysis modules under fluctuating renewable energy to minimize the marginal hydrogen cost. It introduces a decentralized ADMM-based scheduling model implemented within a multi-agent system, where each PEA-agent minimizes its local $mLCOH$ while collectively meeting demand through a coupling constraint and dual updates. A case study with three Enapter EL4 electrolyzers validates the approach, showing strong alignment with reference $mLCOH$ values, fast adaptation to faults and scale-up, and a low normalized error (approximately $3.3\%$) in production. The results demonstrate a scalable, robust framework for real-time operation and seamless integration with MTP-based plant automation and DSM for modular electrolysis plants.

Abstract

In response to the global shift towards renewable energy resources, the production of green hydrogen through electrolysis is emerging as a promising solution. Modular electrolysis plants, designed for flexibility and scalability, offer a dynamic response to the increasing demand for hydrogen while accommodating the fluctuations inherent in renewable energy sources. However, optimizing their operation is challenging, especially when a large number of electrolysis modules needs to be coordinated, each with potentially different characteristics. To address these challenges, this paper presents a decentralized scheduling model to optimize the operation of modular electrolysis plants using the Alternating Direction Method of Multipliers. The model aims to balance hydrogen production with fluctuating demand, to minimize the marginal Levelized Cost of Hydrogen (mLCOH), and to ensure adaptability to operational disturbances. A case study validates the accuracy of the model in calculating mLCOH values under nominal load conditions and demonstrates its responsiveness to dynamic changes, such as electrolyzer module malfunctions and scale-up scenarios.

Cost Optimized Scheduling in Modular Electrolysis Plants

TL;DR

The paper tackles the challenge of coordinating many heterogeneous electrolysis modules under fluctuating renewable energy to minimize the marginal hydrogen cost. It introduces a decentralized ADMM-based scheduling model implemented within a multi-agent system, where each PEA-agent minimizes its local while collectively meeting demand through a coupling constraint and dual updates. A case study with three Enapter EL4 electrolyzers validates the approach, showing strong alignment with reference values, fast adaptation to faults and scale-up, and a low normalized error (approximately ) in production. The results demonstrate a scalable, robust framework for real-time operation and seamless integration with MTP-based plant automation and DSM for modular electrolysis plants.

Abstract

In response to the global shift towards renewable energy resources, the production of green hydrogen through electrolysis is emerging as a promising solution. Modular electrolysis plants, designed for flexibility and scalability, offer a dynamic response to the increasing demand for hydrogen while accommodating the fluctuations inherent in renewable energy sources. However, optimizing their operation is challenging, especially when a large number of electrolysis modules needs to be coordinated, each with potentially different characteristics. To address these challenges, this paper presents a decentralized scheduling model to optimize the operation of modular electrolysis plants using the Alternating Direction Method of Multipliers. The model aims to balance hydrogen production with fluctuating demand, to minimize the marginal Levelized Cost of Hydrogen (mLCOH), and to ensure adaptability to operational disturbances. A case study validates the accuracy of the model in calculating mLCOH values under nominal load conditions and demonstrates its responsiveness to dynamic changes, such as electrolyzer module malfunctions and scale-up scenarios.
Paper Structure (9 sections, 14 equations, 6 figures, 4 tables)

This paper contains 9 sections, 14 equations, 6 figures, 4 tables.

Table of Contents

  1. Introduction and Motivation
  2. State of the Art
  3. Technical Background
  4. Related Work
  5. Decentralized ADMM Scheduling Model
  6. Case Study
  7. Structure and Methodology
  8. Results and Discussion
  9. Conclusion and Future Work

Figures (6)

  • Figure 1: POL Refinement Architecture (Adapted from SHM+23)
  • Figure 2: OpEx and CapEx contributions to mLCOH and the mLCOH gradient for a 10MW Proton Exchange Membrane (PEM) electrolyzer at varying operating points with electricity cost of 0.06€/kWh (Adapted from GVE+22)
  • Figure 3: Activity diagram of the decentralized scheduling
  • Figure 4: (a) Simulated and approximated production curve at different operating points (b) OpEx and CapEx contributions to mLCOH for an AEM EL4 electrolyzer at varying operating points with electricity cost of 0.05€/kWh
  • Figure 5: (a) Aggregated optimized schedule (Opt.) and simulation data (Sim.) over 12 periods (b) Demand deviation and lambda for PEA-agent 1 in period $t=10$
  • ...and 1 more figures