Toward a Blockchain-Based, Reputation-Aware Secure Transactive Energy Market
Bibliographic record
Abstract
The rapid expansion of transactive energy has transformed traditional electricity consumers into producers, engaging in local energy trading. In the context of distributed energy transactions, blockchain technology has been increasingly applied to facilitate transaction transparency and reliability. However, due to the challenges in collecting accurate energy transmission data from power lines, most existing studies on the blockchain-based transactive energy market are still vulnerable to security attacks, such as malicious users misreporting energy prices, refusing to pay or refusing to transmit energy. Therefore, based on the co-simulation platform PEMT-CoSim and a blockchain, we establish a blockchain-based, reputation-aware secure transactive energy market (STEM) by introducing a reputation scheme to evaluate the trustworthiness of all prosumers and designing reputation-aware, multi-round double auction and energy transmission algorithms to detect and penalize malicious attacks. Furthermore, we run comprehensive experiments for different use cases. The results show that even with malicious participants, the proposed system can guarantee the interests of the honest participants and improve the robustness and effectiveness of the energy market.
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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 | 0.004 |
| 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 | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".