Indicators and Representation Tools to Measure the Technical-Economic Feasibility of a Renewable Energy Community. The Case Study of Villar Pellice (Italy)
Bibliographic record
Abstract
Energy Communities (EC) are intended as legal entities that can ensure environmental, economic, and social benefits for energy exchanges between its members. The Italian legislation has recently introduced incentives to Renewable Energy Communities (REC). This work analyses the case study of the REC in Villar Pellice (Turin) and defines a methodology to assess its technical-economic feasibility. The hourly energy consumption and the local renewable energy production are assessed through a place-based methodology, considering different category of end users (municipalities, residential dwelling, companies), and obtaining data from available online database. The REC energy performance is assessed through the self-consumption and the self-sufficiency indexes. Besides, cost-optimal analysis evaluates its economic feasibility, considering investment costs and economic incentives. Several interventions are hypothesized to compare possible REC scenarios (e.g., photovoltaic panels and storage systems installation, energy efficiency measures for public lighting, and different configurations of end users). Results show that REC allows to aggregate stakeholders, ensuring economic advantages and environmental benefits. The methodology applied in this work can support the design phase of the RECs. Its flexibility makes it adaptable to different territorial and regulatory contexts, in evaluating the optimal REC configuration to maximize revenues from the incentive and reach the highest level of energy independence.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".