Dynamic Distributed Energy Resources for Expansion of Ontario’s Greenhouse Sector
Bibliographic record
Abstract
The vegetable greenhouse sector is rapidly growing and adopting technology advances like supplemental lighting. Supplemental lighting has a dynamic impact on the demand and consumption of a greenhouse’s electricity load. There is uncertainty on the rate of adaptation of technologies and the impact this could have on the power consumption of the sector. Without electricity availability, the sectors innovation and expansion can come to a halt. This research focused on investigating greenhouse electrical load models and lighting trends to forecast demand on electricity grids and discover potential for Distributed Energy Resource (DER) applications. This thesis presents a series of studies developed and implemented with commercial greenhouse data and industry standards in Ontario. First, an electrical load model was developed using commercial greenhouse data to differentiate between unlit and lit greenhouse consumption. The use of ten commercial and literature-based combinations of lighting and fixtures resulted in certain combinations and fixtures providing significant electricity consumption savings but displayed a greater capital cost. This model also demonstrated a significant increase in electricity demand and consumption when applying lighting to an unlit pepper vegetable sector. An analysis was then conducted by forecasting the implications of 75% of the Ontario vegetable greenhouse sector adopting lighting. This model normalized the current sectors electrical grid and produced a grid multiplier for lighting scenarios varying in intensity, type, and harvesting area. The findings demonstrated that with careful lighting selection and limitations of lighting intensity, the electrical grid can implement guidelines to regulate and prevent extreme loads from the greenhouse sector. Lastly, an analysis was preformed to illustrate the demand and electrical consumption of five vegetable greenhouses, individually and as a five-grower network. DER designs including cogeneration and battery were developed for the greenhouses and five- grower network. The results show that by creating a network, there can be significant reduction in DER capacity and subsequently financial cost. Outcomes from this study confirm that creating greenhouse networks can allow for greenhouses to self-generate at a reasonable cost using DERs. Together these works combine to form a valuable analysis tool on the greenhouse sectors electrical load and provides potential solutions to moderate the power growth of the sector.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".