Energy Management of a Hybrid Tidal Turbine‐Hydrogen Micro‐Grid: Losses Minimization Strategy
Bibliographic record
Abstract
Abstract This paper presents the modeling and energy management system (EMS) of a hybrid marine‐hydrogen power generation system. The proposed system aims to convert the static nature of the tidal energy into an active system by using a hydrogen energy storage system. The system of the tidal energy converter (TEC) considers the fixed pitch direct drive technology while the hydrogen system consists of 1.0 MW (megawatt) proton exchange membrane electrolyzer. A MATLAB/Simulink based model has been developed for studying and evaluating the effectiveness of the proposed EMS. The developed model depends on scaling up a 50 W proton exchange membrane (PEM) electrolyzer model to 1 MW scale by adapting the model parameters for providing the same key performance indicators (KPIs). The EMS aims to convert all the TEC generated energy into hydrogen with considering the efficient and safe operation of the different system components. Thus, the loss minimization (efficiency maximization) of the tidal turbine generator is integrated as one of the EMS goals to evaluate its effect on hydrogen production. The generator of the TEC is controlled by two different strategies for estimating the surplus hydrogen that could be produced. The strategies are the maximum torque/ampere and the loss minimization.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".