Peer-to-Peer Renewable Energy Trading Using Agent-Based Modelling
Bibliographic record
Abstract
As interest in sustainable living grows, more households turn to rooftop solar panels to meet their energy needs. However, the economic return from selling surplus electricity back to the grid is often minimal, which discourages wider adoption of renewable energy technologies. This paper explores how local, peer-to-peer (P2P) energy trading can make residential solar power more financially rewarding by allowing neighbours to exchange surplus energy directly. The work presents a simulation-based framework that combines agent-based modelling and predictive analytics to test the viability of decentralized energy markets. A virtual community of diverse households is modelled over a year, using realistic consumption and solar generation patterns. A greedy matching strategy connects buyers and sellers in the local network, with any leftover surplus sold to the grid. Testing showed that this approach is both effective and adaptable across different regions. The simulation revealed that small households consistently generated surplus energy, making them key contributors to system-wide savings and grid independence. Even in Dublin, a low-sunlight city, the model delivered strong results, suggesting it would perform even better in regions with higher solar irradiance. The findings support the potential of smart, localized energy-sharing systems to improve both economic and environmental sustainability. The proposed model is adaptable across community scales and regulatory contexts, offering a flexible tool for designing and evaluating future energy-sharing policies.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".