A Game Theory Strategy-Based Bidding Evaluation for Power Generation Market
Bibliographic record
Abstract
In a deregulated power market, the economical-risk due to real-time pricing is critical as it is not permitted to alter the decisions taken once. The existing generation market needs improvement in terms of enhancing its effectiveness and reliability. This article presents a game-theoretic approach-based bidding strategy decision-making through a case study. In this case, three thermal generating units feed three different constant loads for base load demand, one at a time. The economic load dispatch has been obtained using MATLAB software applying the particle swarm optimization (PSO) method. Three different bidding strategies for individual generators have been chosen to create 27 combinations to create data where the zero-sum game theory is applied. The marginal costs are calculated for each of the 27 combinations to formulate a game theory matrix. The game theory dominance method is then applied to obtain the market clearing price (MCP). The proposed methodology can help the GENCOs in making a profit or reducing the risk of making a loss by making a judicious selection among the possible available strategies.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".