Smart Contract-Based System for Automating Energy Transactions in Peer-to-Peer Energy Marketplace
Bibliographic record
Abstract
The adoption of distributed energy resources (e.g., solar panels) is reshaping the energy landscape by enabling participants to trade energy efficiently. Electric vehicles (EVs) further improve this ecosystem by acting as mobile energy storage units that are capable of supplying power back to the grid. However, existing energy transaction systems heavily rely on centralized intermediaries which may lead to insecure trading transactions and limited transparency. To address these challenges, this study proposes a blockchain-based Peer-to-Peer (P2P) energy trading system that uses smart contracts for secure and automated transactions between participants. In this system, the blockchain ensures tamper-proof records, while smart contracts automate key processes such as verifying energy availability, executing payments, and enforcing agreements by eliminating trusted intermediaries. Furthermore, we introduce a penalty mechanism to prevent fraudulent claims and ensure sellers meet their commitments. We evaluated the scalability, security, and economic feasibility of the proposed system for decentralized energy trading using the Ethereum local blockchain network.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".