Optimization of Residential Hydrogen Facilities with Waste Heat Recovery: Economic Feasibility across Various European Cities
Bibliographic record
Abstract
The European Union has established ambitious targets for lowering carbon dioxide emissions in the residential sector, aiming for all new buildings to be “zero-emission” by 2030. Integrating solar generators with hydrogen storage systems is emerging as a viable solution for achieving these goals in homes. This paper introduces a linear programming optimization algorithm aimed at improving the installation capacity of residential solar–hydrogen systems, which also utilize waste heat recovery from electrolyzers and fuel cells to increase the overall efficiency of the system. Analyzing six European cities with diverse climate conditions, our techno-economic assessments show that optimized configurations of these systems can lead to significant net present cost savings for electricity and heat over a 20-year period, with potential savings up to EUR 63,000, which amounts to a 26% cost reduction, especially in Southern Europe due to its abundant solar resources. Furthermore, these systems enhance sustainability and viability in the residential sector by significantly reducing carbon emissions. Our study does not account for the potential economic benefits from EU subsidies. Instead, we propose a novel incentive policy that allows owners of solar–hydrogen systems to inject up to 20% of their total solar power output directly into the grid, bypassing hydrogen storage. This strategy provides two key advantages: first, it enables owners to profit by selling the excess photovoltaic power during peak midday hours, rather than curtailing production; second, it facilitates a reduction in the size—and therefore cost—of the electrolyzer.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".