A holistic study on solar photovoltaic-based cleaner hydrogen production facilities: Economic and performance assessments
Bibliographic record
Abstract
• Four possible cases for solar PV based hydrogen production is studied. • Hydrogen storage limits electrolyser size, reducing output if oversized beyond 400 kW. • O&M costs dominate total expenses, reaching up to 76% over a 30-year lifespan. • Excluding hydrogen storage cuts LCOH nearly in half for 1 MWp PV configurations. • Scaling up PV capacity to 100 MWp drops LCOH below $2/kg for all configurations. This study presents a holistic technoeconomic analysis of solar photovoltaic-based green hydrogen production facilities, assessing hydrogen output potential and cost structures under various facility configurations. Four system cases are defined based on the inclusion of new photovoltaic (PV) panels and hydrogen storage (HS) subsystems, considering Southern Ontario solar data and a 30-year operational lifespan. Through a system level modeling, we incorporate the initial costs of sub-systems (PV panels, power conditioning devices, electrolyser, battery pack, and hydrogen storage), operating and maintenance expenses, and replacement costs to determine the levelized cost of hydrogen (LCOH). The results of this study indicate that including hydrogen storage significantly impacts optimal electrolyser sizing, creating a production bottleneck around 400 kW for a 1 MWp PV system (yielding approximately 590 tons H 2 over a period of 30 years), whereas systems without storage achieve higher yields (about 1080 tons of H 2 ) with larger electrolysers (approximately 620 kW). The lifetime cost analysis reveals that operating and maintenance cost constitutes the dominant expenditure (68–76 %). Including hydrogen storage increases the minimum LCOH and sharply penalizes electrolyser oversizing relative to storage capacity. For a 1 MWp base system, minimum LCOH ranged from approximately $3.50/kg (existing PV, no HS) to $6/kg (existing PV, with HS), $11–12/kg (new PV, no HS), and $22–25/kg (new PV, with HS). Leveraging existing PV infrastructure drastically reduces LCOH. Furthermore, significant economies of scale are observed with increasing PV facility capacity, potentially lowering LCOH below $2/kg at the 100 MWp scale. The study therefore underscores that there is a critical interplay between system configuration, component sizing, operating and maintenance management, and facility scale in determining the economic viability of solar hydrogen production.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".