Design and Field Implementation of Blockchain Based Renewable Energy\n Trading in Residential Communities
Bibliographic record
Abstract
This paper proposes a peer to peer (P2P), blockchain based energy trading\nmarket platform for residential communities with the objective of reducing\noverall community peak demand and household electricity bills. Smart homes\nwithin the community place energy bids for its available distributed energy\nresources (DERs) for each discrete trading period during a day, and a double\nauction mechanism is used to clear the market and compute the market clearing\nprice (MCP). The marketplace is implemented on a permissioned blockchain\ninfrastructure, where bids are stored to the immutable ledger and smart\ncontracts are used to implement the MCP calculation and award service contracts\nto all winning bids. Utilizing the blockchain obviates the need for a trusted,\ncentralized auctioneer, and eliminates vulnerability to a single point of\nfailure. Simulation results show that the platform enables a community peak\ndemand reduction of 46%, as well as a weekly savings of 6%. The platform is\nalso tested at a real-world Canadian microgrid using the Hyperledger Fabric\nblockchain framework, to show the end to end connectivity of smart home DERs to\nthe platform.\n
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".