Coordination of Power and Transportation Networks With Integrated Electricity-Hydrogen Stations: A Reinforcement Learning Approach Considering Delayed Reward
Bibliographic record
Abstract
The increasing adoption of electric vehicles (EVs) and fuel cell EVs (FCEVs) has intensified the coupling between urban transportation and energy systems. However, existing approaches lack effective optimization strategies for integrated transportation–energy systems, particularly in managing integrated electricity-hydrogen stations (IEHSs) to maximize revenue while serving various vehicle types, such as EVs and FCEVs. The present work proposes a dynamic optimization strategy for IEHSs that enhances the operation of IEHSs, power distribution networks, and transportation networks by guiding vehicle charging and fueling decisions. The sensitivity of vehicular traffic to system-level decisions is ensured by establishing a mixed dynamic user equilibrium model incorporating EVs, FCEVs, and conventional-fuel vehicles. An IEHS operation framework that integrates discount coupon allocation and hydrogen production/storage strategies is designed with traffic flow feedback and distribution locational marginal pricing. The inherent delayed reward problem in transportation, where the impacts of pricing decisions on station revenue and traffic congestion manifest over extended time horizons, is addressed by applying a multi-agent deep reinforcement learning approach, featuring a dual-critic architecture based on long short-term memory networks under the sequence markov decision process framework, to redistribute delayed rewards. The results of numerical computations involving real-world traffic and power networks demonstrate that the proposed approach improves IEHS profitability, balances charging demand, and alleviates traffic congestion compared with static and semi-dynamic traffic approaches.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".