Complementary EV Scheduling in Peer-to-Peer Energy Markets
Bibliographic record
Abstract
The adoption of electric vehicles (EVs) is accelerating and is expected to continue growing alongside the decentralization of energy markets. This paper presents an EV charging algorithm tailored for decentralized peer-to-peer (P2P) transactive energy systems and evaluates its cost minimization performance against traditional methods. To enhance computational efficiency, the nonlinear charging characteristics of EVs are linearized using segmentation techniques. The study incorporates multiple EV types and real-world parking lot data to reflect realistic scenarios. Vehicle-to-Grid (V2G) functionality is also explored, with the problem formulation simplified by eliminating binary variables, converting it from a mixed-integer to a continuous optimization problem. Different case studies are conducted to test the effectiveness of the proposed algorithm. The simulation results demonstrate a 16% reduction in total charging costs, which is 4.5% better than the nearest alternative. Additionally, the proposed approach consistently operates within a 10-minute runtime constraint for real-time applications, achieving runtimes under 10% of those observed in dynamic models, which often exceed the limit on typical weekdays. The V2G scenario yielded only a 1% cost benefit over the Grid-to-Vehicle (G2V) case, indicating that meaningful gains from V2G schemes depend heavily on user profiles and market pricing conditions.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".