Research on fuzzy logic-based virtual power plant market trading model under decentralized trading mechanism
Bibliographic record
Abstract
With the promulgation of relevant policies, virtual power plant market transactions are facing major adjustments, in order to promote the smooth entry of virtual power plants into market-oriented transactions and improve the economic benefits of virtual power plants, this paper proposes a virtual power plant market transaction model.The traditional virtual power plant resources are mathematically modeled, blockchain technology is introduced to build a decentralized trading framework, and fuzzy neural networks are combined to predict the power load of the virtual power plant.Then the decision-making model of virtual power plant participation in spot market trading is constructed by using two-stage stochastic planning theory with the goal of maximizing expected return.The results show that the prediction effect of the fuzzy logic-based virtual power plant market trading model is 2.925% higher than that of the traditional BP algorithm model, and its accuracy and stability are significantly improved.In addition, the distributed energy storage aggregated by the virtual power plant as well as the dynamic demand response rate is fast, the regulation is flexible, the short-time power throughput capability is strong, and it can accurately track the FM instructions.The cumulative FM capacity and FM mileage provided by the virtual power plant account for 84% and 99% of the total FM capacity demand in the system, respectively, making it highly competitive in the FM market.And under the premise of balancing riskiness and profitability, the bidding scheme of virtual power plant derived in this paper is more effective.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".