Optimization of baseload electricity and hydrogen services by renewables for a nuclear-sized district in South Italy
Bibliographic record
Abstract
We present an optimization model of an energy district in South Italy that supplies baseload electricity and hydrogen services. The district is sized such that a nuclear reactor could provide these services. We define scenarios for 2050 to explore the system effects of discount rate sensitivity, vetoes on technologies, and cost uncertainties. We address the following issues relevant to decarbonization in South Italy: land-based wind and solar vs. exclusive solar rooftop, extra cost of a veto on nuclear, conservative assumptions on future storage technology and the role of pumped hydro storage , lack of low-cost geological storage of hydrogen and the industrial competitiveness of this carrier, and the methanation synergy with the agroforestry sector. Our results quantify the high system cost of vetoes on land-based wind and solar. Nuclear may enter the optimal mix only with a veto against onshore wind and a hypothesis of equal project risk, hence an equal discount rate, with renewables. Scenarios with land-based wind and solar obtain low-cost hydrogen and thus allow industrial uses for this carrier. The methanation synergy with the agroforestry sector does not offer a system cost advantage but improves the district’s configuration. The extra cost of full decarbonization relative to unregulated fossil gas is small with land-based wind and solar, and significant with vetoes to these technologies.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".