Comparison of Centralized and Distributed Photovoltaic System Power Intermittency Based on Measured Data at the Municipal Scale
Bibliographic record
Abstract
The decrease in solar photovoltaic (PV) system costs and regulatory barriers coupled with increased financial incentives and climate awareness has led to rapid deployment in both commercial and residential sectors. As solar is an intermittent resource, PV systems can negatively impact electricity grid stability due to increased power ramp-rates and temporal misalignment between energy production and load. Distributed systems, especially rooftop mounted residential, present a range of geometric alignments and spatial separation that when aggregated introduce temporal diversity. If these features reduce barriers to integration with the electricity grid, then they should be weighed against increased costs per rated power compared to larger commercial systems. To aid in such assessments, we compare power production data from 44 diverse residential PV systems spread across a large municipality (1200 km 2 ), and one large rooftop commercial installation (660 kW) within the same area. These production data were used to calculate 5 and 15-minute ramp rates and were contrasted against provincial load. The aggregated residential PV systems ramp rates never exceeded 10% per 5 minutes, while a large central installation experienced this nearly 2000 times in a one-year period. Additionally, the centralized system experienced ramp rates exceeding 50% per 5 minutes 17 times and had a peak ramp rate of 65% per 5 minutes once in the year. These results are consistent with a previous study conducted in the region using pyranometers, supporting the use of measured irradiance data for planning purposes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".