Hydrogen Storage Optimal Scheduling for Fuel Supply and Capacity-Based Demand Response Program Under Dynamic Hydrogen Pricing
Why this work is in the frame
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Bibliographic record
Abstract
As the emerging technology offers more economic and efficient mechanisms for hydrogen production, fuel cell electric vehicles (FCEVs) are expected to be deployed more extensively in the near future. Proliferation of hydrogen fueling stations throughout the transportation network and justifying their economic viability are key factors to the success of the FCEVs. In today's deregulated market environment, many governments are encouraging private investors to invest in key infrastructures including the hydrogen fueling stations. To that end, this paper proposes a new model for optimal scheduling of privately owned hydrogen storage stations to both serve the transport sector and the electricity market operator. The model mainly aims to: 1) exploit the lower electricity market prices to reduce the power purchase cost and 2) contribute to the capacity- based demand response program to further enhance the economic feasibility of the investment. The profitability constraints and dynamic hydrogen pricing mechanisms are incorporated into the optimization process to guarantee the economic feasibility of the investment. Through such constraints, hydrogen sale prices would dynamically change to maintain system profitability at the lowest possible hydrogen price. Numerical studies reveal that the stacked profit from the two aforementioned sources of revenue under the proposed model would lead to a stronger rate of return.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it