Modelling tools for the assessment of Renewable Energy Communities
Bibliographic record
Abstract
The energy transition is driving the adoption of local renewable energy production. Decentralised renewable plants enable citizens to play an active role in generating and managing energy supplies. In Europe, recent policies are promoting Renewable Energy Communities (RECs), which consist of aggregations of end-users aiming to produce and share renewable energy, generating and managing cost-effective energy supply chains autonomously. A comprehensive analysis of REC potential requires tools that integrate socio-economic, environmental, and spatial evaluations for renewable energy assessment. The objective of this study is to present the current status and capabilities of tools for REC modelling. This paper reviews twelve energy modelling tools which have the potential for the evaluation of RECs. The review structure follows the steps of a REC assessment process, which is structured in background, inputs, simulation or optimisation and outputs. Technical, economic, and environmental aspects of REC projects should be included without leaving behind the spatialisation and geographical planning of the new energy systems. Findings reveal that the co-existence of multiple criteria is not satisfied in any of the current tools, as most of them mainly analyse a few areas of interest and partially consider other aspects. The comparison reveals that the energy and financial outputs are mainly deepened. Meanwhile, environmental and spatial criteria have a marginal role among both inputs and outputs. Finally, software marginally spatializes the workflow steps except for CEA and URBANopt, which are revealed to be the most complete options for REC design.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".