Research on Coordinated Charging Strategy Considering Load and Cost of Electric Vehicles in a Residential Quarter
Bibliographic record
Abstract
At present， all countries are vigorously developing new energy electric vehicles to ease the oil crisis and environmental protection pressure. However， a large number of electric vehicles are connected to the grid， thus the grid load is increased the load peak-to-valley difference is aggravated， and the electricity cost of residents is increased and the economy and stability of the power grid are reduced. In view of the aforementioned problems， this paper establishes and analyzes the random charging load model of electric vehicles in a residential quarter， and proposes a charging strategy that adjusts the charging time by using the time-of-use price of electricity， which uses the load variance of the residential quarter and the charging cost of electric vehicles as evaluation indexes to optimize the charging time of electric vehicles. The example shows that the aforesaid charging strategy can effectively reduce the peak-valley difference of load caused by grid integration of EVs， achieve the effect of peak-shifting and valley filling， and greatly reduce the charging cost of EVs.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".