Optimization of Electric Vehicle Charge Scheduling in Multiple Parking Lots: A Method Based on Metaheuristics and High Performance Computing
Bibliographic record
Abstract
This paper presents a centralized model based on metaheuristics to solve the problem of optimal Electric Vehicle (EV) charge scheduling in multiple parking lots. A centralized optimization model using two-level particle swarm optimization was developed, to find the optimal allocation of power between parking lots by a central aggregator, and to find the optimal EV charging schedule for each parking lot within the system, in order to minimize the cost to charge all EVs. High Performance Computing (HPC) techniques are used to combat the high resource requirements and long simulation time associated with the problem size, to increase the scalability of the solution, and to complete the optimization in an acceptable real-time interval. The parallelized centralized optimization model was validated against its sequential version and a decentralized model on an HPC cluster, where it provided more optimal, lower cost solutions than the decentralized model, and provided average computation speedups of up to 139 times faster than the sequential model. The model was tested with scenarios from 3 to 27 parking lots, on the MATPOWER 18, 33, 69, and 141-bus distribution systems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".