Optimal Photovoltaic Energy Allocation in Residential Renewable Energy Communities
Bibliographic record
Abstract
Effective deployment of Renewable Energy Communities (RECs) is essential for the success of Europe’s energy transition. However, there are no clear criteria for sharing energy generation among community members, which conditions the rhythm of RECs penetration. The lack of transversal legislation gave rise to different approaches in EU countries for solving this issue, with the calculation of ’a priori’ and ’a posteriori’ energy sharing coefficients being the most extended ones. Nevertheless, it is still necessary to develop better optimization algorithms to make the most of RECs, to minimize the surplus of energy, and improve payback among end-users and their impact on the energy system. This paper gives an overview of the state-of-the-art and proposes improvements to the linear programming (LP) methodologies devoted to obtaining an optimal calculation of renewable sharing coefficients; able to provide satisfactory results, no matter the size of the REC, while consuming affordable computation resources. The performance of the proposed method has been validated using real generation and consumption data collected from a Spanish DSO, considering both ’a priori’ and ’a posteriori’ remuneration schemes. The results prove the good behaviour of the proposed solution for different REC sizes and operating conditions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".