Renewable energy communities under the 2019 European Clean Energy Package – Governance model for the energy clusters of the future?
Bibliographic record
Abstract
The recast of the European Union Renewable Energy Directive (RED II) entered into force in December 2018, followed by the Internal Electricity Market Directive (IEMD) and Regulation (IEMR) as part of the Clean Energy for all Europeans Package. The RED II, that the 28 Member States have until June 2021 to transpose into national law, defines “Renewable Energy Communities” (RECs), introduces a governance model for them and the possibility of energy sharing within the REC. It also provides an “enabling framework” to put RECs on equal footing with other market players and to promote and facilitate their development. This article defines "renewable energy clusters" that are comprised of complementarity of different energy sources, flexibility, interconnectivity of different actors and bi-directionality of energy flows. We argue that RECs and RE clusters are socio-technical mirrors of the same concept, necessary in a renewable energy transition. To test how these new rules will fare in practice, drawing on a secondary dataset of 67 best-practice cases of consumer (co-)ownership from 18 countries, each project is assessed using the criteria of cluster potential, and for the extent that they meet the RED II governance requirements of heterogeneity of members and of ownership structure. Nine cases were identified as having cluster potential all of which were in rural areas. Of these, five projects were found to be both RECs and RE clusters. The absence of the governance and heterogeneity criteria is observed in projects that fall short of the cluster elements of flexibility, bi-directionality and interconnectivity, while cluster elements occur where the governance and heterogeneity criteria are met. When transposing the new rules into national law we recommend careful attention to encourage complementarity of renewables, RECs in urban contexts and “regulatory sandboxes” for experimentation to find the range of optimal preferential conditions of the “enabling framework”.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".