Energy remuneration mechanisms for renewable energy communities: Insights from a mixed-use district
Bibliographic record
Abstract
Distributed energy generation is enabling new forms of collective self-consumption and local energy sharing. While the European Union has implemented regulatory frameworks supporting renewable energy communities, Canada remains in the early stages. As regulatory frameworks evolve to support energy communities, assessing the performance of different energy remuneration mechanisms (ERMs) becomes essential to guide their implementation. This study investigates the techno-economic impacts of different ERMs and revenue redistribution strategies within an urban building energy modelling framework. The case study is a mixed-use urban district in London, Ontario, transitioning toward a solar-based energy community. The analysis compares single-building Feed-in Tariff (FiT) with community-based ERMs, namely Ontario Community Net Metering (CNM), Italy Virtual Energy Sharing (VES), and Peer-to-Peer (P2P) trading with internal dynamic pricing. Results indicate that CNM achieves the highest cost savings, reducing annual energy expenditures by up to 35 % relative to the business-as-usual scenario, primarily by maximizing the local use of solar surplus over a 12-month horizon. In contrast, VES is constrained by hourly self-consumption requirements, leading to approximately 22 % unused PV generation. P2P scenario shows variable outcomes but generally yields marginal savings over FiT. Energy-sharing strategies of annual revenues significantly impact financial outcomes, support PV size or surplus ratios at the building level. The maximisation of self-consumption generates the highest savings. Aligning temporal flexibility from CNM with collective sharing benefits from VES suggests that hybrid models may offer more resilient and equitable frameworks for energy communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".