Coordinated charging of electric vehicles connected to a net-metered PV parking lot
Bibliographic record
Abstract
Although Plug-in Electric Vehicles (PEVs) are associated with zero tailpipe emissions, the electrical grid emissions due to charging of the vehicles are motivating the use of distributed generation. This paper proposes an optimal strategy to coordinate the charging of a large fleet of electric vehicles when connected to a net-metered PV parking lot in Victoria, British Columbia. The problem is cast as a mixed-integer linear programming problem using the CPLEX optimization tool to determine the optimal charge scheduling of vehicles based on seasonal solar potential. The algorithm is used to assess the minimal feeder capacity and the number of charging stations for the installation, as well as minimize the daily operational cost (OC). The daily OCs for coordinated and uncoordinated charging with and without net metered rooftop PV installations for each season of the year are compared. It is determined that implementing coordinated charging can have up to a 20% OC decrease, whereas coupling solar generation with coordinated charging results in a 14-96% decrease depending on the season and peak power.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".