Optimization of PV Array-to-Inverter Power Ratio in Grid-Connected Systems to Maximize System Profit
Bibliographic record
Abstract
Since PV arrays do not generate nominal power most of the time due to climate conditions, determining the optimal array-to-inverter power ratio (AIPR) is a significant factor in extracting the maximum energy with the highest efficiency to connect to the grid. Previous studies have tried to minimize investment costs, but this does not maximize profit as the time-of-use (TOU) electricity price is not considered. In the present paper, a method is proposed for the optimization of AIPR to maximize the profit during the entire lifetime of the PV system, considering TOU electricity pricing, climate conditions, and optimal array installation conditions. The proposed method results, implemented for a 10 kW PV system considers climate data, and electricity pricing for the city of London in Ontario, Canada, show that the optimal AIPR for this system in the city of London is 2, which results in $8446 more profit than an AIPR of 1 for the entire PV system lifetime. In addition, a comparison of the results of the proposed method with those obtained by minimizing the levelized cost of energy (LCOE) index shows that using the proposed method results in $4130 more profit.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.001 | 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".