Deep Reinforcement Learning-Based Charging Price Determination Considering the Coordinated Operation of Hydrogen Fuel Cell Electric Vehicle, Power Network and Transportation Network
Bibliographic record
Abstract
Currently, hydrogen fuel cell electric vehicles (HFCEVs) are becoming more financially accessible as an alternative to petroleum-powered vehicles, while also decreasing carbon dioxide emissions. However, the coordinated scheduling of HFCEVs refueling with the operations of hydrogen refueling stations, electrical power network (PN), and transportation networks (TN) represents an integral challenge. This complex problem encompasses a combinatorial mixed-integer nonlinear optimization problem with a sizeable number of decision variables. Existing methods struggle to adequately solve this problem. In this article, deep reinforcement learning (DRL) is deployed to determine the refuelling price to guide the HFCEV refuelling in the transportation network. First, HFCEV traffic flow model based on the refuelling price in real-world TN is presented. Then, the HFCEVs hydrogen demands in the microgrid is presented. After that, an IEEE 30 nodes utility grid exporting electricity to microgrids is presented. At last, DRL (DDPG, TD3, SAC, PPO) is deployed to determine the price based on the traffic condition of the TN and the voltage condition of the PN. The simulation results demonstrate that through the DRL price agent, the total travel time of the TN and the total operation costs of the PN are all reduced, and multiagent DDPG and TD3 algorithm have the best performance.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".