Wind-coupled hydrogen integration for commercial greenhouse food and power production: A case study
Bibliographic record
Abstract
• Green hydrogen integration via wind power for commercial greenhouse. • Transitioning cogeneration from natural gas to hydrogen for electricity generation. • Nine scenarios analyzed for hydrogen production, transportation, blending and usage. • Evaluated levelized cost of hydrogen, IRR, payback, and discounted payback. • 10% hydrogen blend reduces levelized cost; 100% blend improves IRR and payback. This study investigates the feasibility of using green hydrogen technology produced via Proton Exchange Membrane (PEM) electrolysis powered by a 200 MW wind farm for a commercial Greenhouse in Ontario, Canada. Nine different scenarios are analyzed, exploring various approaches to hydrogen (H 2 ) production, transportation, and utilization for electricity generation. The aim is to transition from using natural gas to using varying combinations of H 2 and natural gas that include 10 %, 20 %, and 100 % of H 2 with 90 %, 80 %, and 0 % of natural gas, to generate 13.3 MW from Combined Heat and Power (CHP) engines. The techno-economic parameters considered for the study are the levelized cost of hydrogen (LCOH), payback period (PBT), internal rate of return (IRR), and discounted payback period (DPB). The study found that a 10 % H 2 -Natural Gas blend using existing wired or transmission line (W-10H 2 ) with 5 days of storage capacity and 2,190 h of CHP operation per year had the lowest cost with a LCOH of USD 3.69/kg. However, 100 % of H 2 using existing wired or transmission line (W-100H 2 ) with the same storage and operation hours revealed better PBT, IRR, and DPB with values of 6.205 years, 15.16 % and 7.993 years respectively. It was found impractical to build a new pipeline or transport H 2 via tube trailer from wind farm site to greenhouse. A sensitivity analysis was also conducted to understand what factors affect the LCOH value the most.
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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 | 0.000 |
| 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".