A Multi-Parameter Approach to Optimal Power Dispatch in Grid-Connected Photovoltaic-Battery Systems
Bibliographic record
Abstract
In photovoltaic-battery energy storage systems (PV-BESSs), the optimal power dispatch between the power sources (PV, battery, and the grid) and the load demand is significant, from the viewpoint of system efficiency and household profit. In this paper, a method that considers the time-of-use pricing (TOU) of electricity, the PV feed-in-tariff, and battery lifetime is proposed for the optimal power dispatch of grid-connected PV-BESS systems during a 24-hour period. The proposed method is implemented for a residential PV-battery system using the genetic algorithm (GA) and real TOU electricity pricing data and PV feed-in-tariff data of London, ON, Canada. Simulation results show how the proposed method can result in the optimal power dispatch in the system and how user profit can be maximized.
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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 | 0.001 |
| 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".