A Blockchain-based Spot Market Transaction Model for Energy Power Supply and Demand Network
Bibliographic record
Abstract
The energy power supply and demand network (EPSDN) is difficult to be scheduled in a coordinated manner, due to the fluctuations in intraday power price. To solve the problem, this paper puts forward a blockchain-based spot market transaction model for the EPSDN, with the aim to enhance the intelligence, real-time performance and security of spot power market transactions. Specifically, intraday time-of-use (TOU) pricing mechanisms were introduced to minimize the negative impacts of intraday power price variation on the spot market; the leading influencing factors of spot power market were identified effectively among various factors through factor analysis; multiple purchase plans were optimized by the multi-objective search algorithm based on the particle swarm optimization (PSO), enabling the seller to optimize the purchase plan when multiple suppliers are available under the relaxation of control over direct power trading. On this basis, the real-time property of the transaction information was guaranteed through EPSDN-based information exchange. The case analysis shows that our transaction model outperformed the traditional centralized transaction model in transaction efficiency and security. The research findings shed new light on the operation of spot power market under partial decentralization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".