A Multi-Participant Benefit Maximization Model and Strategy Optimization in Market Transactions Incorporating Game Theory
Bibliographic record
Abstract
This topic discusses the problem of maximizing the interests of multiple participants in the trading market based on game theory.Taking the electric power market as a study case, an interest maximization model of multi-party trading strategy in the electric power market is constructed, and the ADMM algorithm is used to solve the model.The rationality and effectiveness of the described model are verified through experimental analysis and arithmetic simulation.Compared with other algorithms, the ADMM algorithm in this paper has a faster convergence speed, and the benefits of the grid company, the benefits of the new entities and the benefits of the users under different numbers of users are all closest to the ideal Nash equilibrium state, which shows the superiority of the ADMM algorithm in this paper.The constructed model is used to solve the conflict of interests among the grid company, new entities and users, and the existence and uniqueness of the game equilibrium is proved through analysis and derivation, and has good convergence results.After the optimization of the strategy in this paper, the revenue of the added entity increases by 6.76%, the power purchase cost of the users decreases by 10.29%, and the consumption surplus increases by 4.50%.Through price-guided output, the load curve is realized to shift peaks and fill valleys, so that the grid company, the added entities and the users get higher benefits.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".