Performance analysis of a real-time decentralized algorithm for coordinated PEV charging at home and workplace with PV solar panel integration
Bibliographic record
Abstract
Several studies dealt with grid-to-vehicle (G2V) and vehicle-to-grid (V2G) concepts emphasizing the importance of the integration of renewable energy sources (RESs). However, none has simultaneously evolved V2G, G2V and RES integration by simulating a real-time decentralized algorithm. Unlike previous work, our approach contributes to the coordination of plug-in electric vehicle (PEV) charging (G2V) and discharging (V2G) according to power generation while at the same time integrating photovoltaic (PV) solar panels at workplaces by proposing a real-time decentralized vehicle/grid algorithm, where PEVs can consume power from and supply stored power to the grid. The integration of PV solar panels to locally charge PEVs plays a major role in limiting the stress on grid demand. From the power perspective, our real-time decentralized algorithm shows superior performance in terms of peak shaving and minimizing system losses. The communication results indicate an efficient delay reduction and lower throughput compared to a benchmark centralized algorithm.
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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 | 0.004 |
| 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".