Price-Guided Cooperation of Combined Offshore Wind and Hydrogen Plant, Hydrogen Pipeline Network, Power Network, and Transportation Network
Bibliographic record
Abstract
Nowadays, the combined offshore wind farm and hydrogen production plant (COWHP) has been installed all around the world and will play an important role in reducing the carbon dioxide emissions. However, the cooperation of the COWHP, the hydrogen pipeline transportation, the power network transmission, and the hydrogen fuel cell electric vehicles (HFCEVs) scheduling is still an essential problem. This problem falls into the category of mixed-integer nonlinear optimization, which involves a significant number of decision variables and constraints. Existing methods have often struggled to effectively solve this problem due to its inherent complexity and non-linear nature. In this paper, a price-guided method is proposed to cooperate with this complex system. First, the COWHP model, the hydrogen pipeline network scheduling model, the power network flow model, the HFCEVs refuelling microgrid model, and the HFCEV traffic flow model in real-world transportation networks are presented. Second, the price-based cooperation model is proposed. Third, price strategies are deployed to cooperate with the complex system. The decision making trial and evaluation laboratory-the technique for order preference by similarity to ideal solution (Dematel-TOPSIS) method is deployed to evaluate the real-time performance of different strategies. The simulation results reveal that in the small vehicle flow transportation network cases, deep deterministic policy gradient (DDPG) has the best real-time evaluation performance; whereas for the large vehicle flow transportation network cases, long short term memory (LSTM) has the best real-time performance. From the posterior evaluation view, LSTM has the best performance for all cases to reduce total operation cost, and also reduce the total waiting time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".