Building bridges: Unlocking cost savings through peer-to-peer trading between heat prosumers
Bibliographic record
Abstract
District heating networks (DHNs) offer efficient and sustainable heating for urban buildings, but their centralized form limits operational flexibility and cost-effectiveness for individual buildings, especially positive energy ones. Peer-to-peer (P2P) energy trading has emerged as a potential solution to enhance local and private energy exchange. However, research on the economic and environmental impacts of P2P heat trading remains scarce, and commercial applications are absent so far, compared to P2P trading of electricity. This study explores the feasibility and benefits of P2P heat trading under various trading mechanisms, with a focus on cost and emission savings, using a newly developed nonlinear optimization framework. Results indicate that P2P trading can significantly reduce costs and emissions. Particularly when storage capacities are limited, P2P trading achieves cost and emission reductions of up to 29.57% and 18.38%, respectively, for an economically focused optimization, or 3.03% and 28.13% for an environmentally focused optimization. Moreover, when heat exports to the DHN are not incentivized, the economic benefits of P2P trading become more pronounced, with cost savings increasing to 5.19% compared to individual operations instead of 2.09% when the exports are incentivized. It is highlighted that P2P trading is beneficial under various studied schemes, most notably in terms of environmental impact. However, it becomes most effective when buildings have limited thermal storage or when DHN interaction is constrained, where all trading schemes show substantial enhancements but differ greatly in terms of the magnitude of enhancement.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".