Day-ahead dispatch of PEV loads in a residential distribution system
Bibliographic record
Abstract
With the expectation of increasing market share of Plug-in Electric Vehicles (PEVs), utilities expect to see a significant increase in energy demand and system peak as a result of PEVs recharging their batteries from the grid, if the charging is not controlled at the distribution system level. By making the grid “smarter”, utilities would be able to maximize utilization of existing assets and defer capital investments, while maintaining system security and reliability. The current research proposes a modeling framework for day-ahead dispatch and dynamic control of PEV loads as well as scheduling of taps and capacitors. The first step of the proposed work, which is presented in this paper, involves the development of a static Genetic Algorithm (GA)-based optimization model that determines the day-ahead schedule for PEV loads, taps and capacitors, with the base load and relevant PEV information provided as inputs to the model. In this case, the objective is to minimize the system peak, while satisfying the physical and operational limits of the distribution system, as a higher system peak translates into higher operational costs for the utility. The proposed approach is tested in an actual distribution feeder, demonstrating its feasibility for realistic applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".