Optimal PHEV charging in coordination with distributed generation operation in distribution systems
Bibliographic record
Abstract
Increase in energy demand is one of the major challenges that utilities are faced with, which results in increase in environmental pollution and global warming. The transport sector has a significant share in the energy demand as well as environmental pollution. In Canada, almost 35% of the total energy demand is from the transport sector and it is the second largest source of greenhouse gas (GHG) emissions. The government of Ontario, Canada, has aimed to move toward a green energy economy, aimed towards increased penetration of renewable energy sources and Plug-in Hybrid Electric Vehicle (PHEV) technology. In this paper, an optimal power flow (OPF) based optimization framework considering two different objectives, minimizing feeder losses and PHEV charging cost, are presented to understand the impact of PHEV charging on distribution networks. Three different charging periods are considered and the impact of the Ontario Time-of-Use (TOU) tariff on PHEV charging schedules is examined. The impact of PHEV charging on distribution systems in the presence of renewable energy sources is also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".