Integration of Green Hydrogen-based Loads, Storage and Generation in Electricity Markets
Bibliographic record
Abstract
Energy transition, shifting from fossil-fuel based to clean resources, is a critical step toward achieving net-zero emission targets, and is being explored worldwide. Green hydrogen is a potential zero-carbon solution to meet decarbonization goals. In this context, this paper presents a novel framework and mathematical model to integrate hydrogen-based emission free resources (HEFRs) in a locational marginal price (LMP)-based day-ahead market (DAM). The model takes into account the detailed physical, energy arbitrage and operational characteristics of the HEFRs. The proposed model is tested on the IEEE 24-bus Reliability Test System (RTS) with PV and wind included, and is formulated as a mixed integer programming (MIP) problem. The results demonstrate the benefits of the proposed framework and the impact of HEFRs participation on market settlement, marginal prices, system emissions and system operation during normal, uncertainties and congestion scenarios.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".