Smart Energy Systems for Increasing the Energy Independence ofSmall Islands: Integrating Renewable Energies, Storage Systems andSustainable Mobility
Bibliographic record
Abstract
Several islands around the world are facing common challenges in terms of high energy costs, local CO2 emissions, security of supply and system stability. In EU, many islands have become sites of energy innovation, where betting on Renewable Energy Sources (RES) is a winning choice to meet their energy needs. In this framework, renewable energies play a key role for supporting the transition of small islands to an autonomous, cleaner and low-carbon energy systemin line with the overall EU Energy Union package and EU2030 goals. The intervention will deal with the suitability of smart energy systems in the insular context. Particularly, the use of several RES generators in the grid will be discussed as well as the potential synergies that can be obtained by coupling different energy sectors such as the sectors of transport, heating and water production. In detail, the benefits in terms of enhanced grid flexibility and thus the potential increase of RES penetration without compromising the island electric grid will be presented. Particularly, the speech will consider solutions that exploit synergies between different energy consuming sectors trough power-to-X solutions. Where X stands for i) transport through the use of Electric Vehicles (EVs), ii) heating by means of Heat Pumps (HPs) and iii) hydrogen produced by electrolysis used in Fuel-cell Electric Vehicles (FCEVs) or in Hydrogen Compressed and Natural Gas (HCNG) blend fuelled buses in the public transportation sector. All of the above-mentioned solutions will be presented by means of several energy scenarios applied to the island of Favignana, Italy. Results will be presented and discussed focusing on the energetic point of view as well as the economic and the environmental aspects. The intervention will end proposing interesting hint for future research topics related to innovative market schemes able to trigger the above-mentioned solutions and also the cascade effect that RES investments have on island communities.
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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 | 0.000 |
| 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 | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".