Photovoltaic tracking technologies for sustainable electrification: A techno-economic analysis on Western Pelee Island, Canada
Bibliographic record
Abstract
This article investigates the economic and technical feasibility of employing various photovoltaic (PV) tracking systems to electrify Western Pelee Island. The systems under consideration include horizontal-axis monthly adjustment (HMA), horizontal-axis continuous adjustment (HCA), vertical-axis continuous adjustment (VCA), and dual-axis tracker (DAT). The analysis includes a techno-economic assessment of these trackers, considering solar, bio, and diesel operation and two dispatch strategies: cycle charging (CC) and load following (LF). The results indicate that the optimal solution is a CC-controlled system equipped with a VCA tracker. The LF-controlled system with this tracker has a higher net present cost (NPC), cost of energy (COE), and renewable fraction by ∼$0.02 M, ∼$0.002/kWh, and 7.6%, respectively. NPC of HMA and COE of HVA-based systems with CC strategies are the most sensitive cases to SOC min . In load variation, the largest and lowest decrease in COE, respectively, is observed in HVA and DA trackers controlled by CC dispatch strategy. In order for DA trackers to match the performance of VCA trackers, their costs must decrease by approximately 41% and 43% in CC and LF systems, respectively. The financial sensitivity of DA-based systems is higher due to albedo effects. This study provides valuable insights into optimizing PV tracking for the electrification of Western Pelee Island and enhances our understanding of the economic implications associated with dispatch strategies and tracking technologies in sustainable energy planning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".