Peer-to-Peer Energy Optimization in V2X Using Reinforcement Learning
Bibliographic record
Abstract
Recent advancements in renewable energy technologies, along with the energy exchange capabilities of Electric Vehicles (EVs), present new opportunities for enhancing renewable energy management and its integration into traditional power systems. Despite these advancements, challenges such as the pricing of charging and discharging clean energy, and the distribution of available energy supplies persist in the realm of energy trading. Our research addresses these issues by leveraging Vehicle-to-Everything (V2X) technologies, which enable EVs to distribute energy to a wide range of consumers. We introduce a dual-level optimization approach that synchronizes financial incentives with the variable electricity prices at EV charging in the parking. This approach is supported by a state-of-the-art reinforcement learning model that integrates primal-dual optimization with upper-confidence bound techniques. Our model is specifically designed to optimize both power management and the effective use of incentives. The overarching goal of this strategy is to augment the adaptability of Vehicle-to-Grid (V2G) and Vehicle-to-Vehicle systems to encourage user participation in energy exchange processes, thereby promoting a more efficient and integrated renewable energy ecosystem.
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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 | 0.000 |
| 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 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".