A Dynamic Battery Charging Approach for Energy Trading in the Smart Grid
Bibliographic record
Abstract
Battery systems are going to play a major role in the emergence of the smart grid system. In this paper, we examined the effect of the battery system dynamics in the energy trading from the end-user perspective. In particular, a novel energy trading framework called Dynamic Battery Charging is developed to use the battery storage for energy trading strategically. Here, the market is considered to be partially decentralized with a two-way trade framework. The end-user is free to decide the amount of energy to bilaterally trade with the central grid. The proposal is tested and validated through a case study of three different load profiles (different in scale) in three energy markets. The simulation results clearly show the profitability of the proposed strategy in all test benchmarks. The proposed strategy resulted in 10-30% profit in Ontario, California and New-York market. Further, the prospect of reduced battery prices makes the proposal even more attractive.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".