Energy management for modular proton exchange membrane water electrolyzers under fluctuating solar inputs: a constrained nonlinear optimization approach
Bibliographic record
Abstract
This study introduces an advanced nonlinear optimization-based energy management system (EMS) specifically designed for modular Proton Exchange Membrane Water Electrolyzers (PEMWE) under fluctuating solar energy conditions. To optimize system efficiency, reduce degradation, and maintain balanced stack operation, the Rotary Power Allocation Strategy (RPAS) employs a dynamic power distribution method that allocates power among multiple stacks based on current demand. A validated electrochemical model calibrated through laboratory experiments and genetic algorithm optimization serves as the foundation for the analysis. Benchmarking in this study has demonstrated the superiority of the proposed strategy to established EMS methods, such as rule-based, fuzzy logic, composite optimization, and decentralized multi-agent scheduling. In terms of system efficiency, it has been demonstrated that there is an increase of 63 % in system efficiency, a greater hydrogen yield exceeding 4200 kg per year, and a considerable reduction in stack voltage degradation. The strategy effectively addresses key limitations associated with conventional EMS approaches, ensuring consistent and equitable stack usage, thus providing a robust, scalable solution for renewable energy-driven hydrogen production.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".