A game-theoretic model for renewable and conventional energy generators under tradable green certificate mechanism
Bibliographic record
Abstract
This paper explores the strategic behavior of power generators under green certificate trading policies, considering both renewable and conventional energy generators. Using game theory, we construct a Nash equilibrium model that incorporates the unit price of green certificates, the required quantity of certificates, and the cap on the quantity. By applying the Karush-Kuhn-Tucker conditions, we reform this Nash equilibrium problem as a mixed complementarity system, which can be solved by MATLAB software. Furthermore, we conduct sensitivity analysis and numerical tests on a number of important parameters. The results reveal that, under certain conditions, the unit price of green certificates does not affect the number obtained by renewable energy generators or purchased by conventional energy generators. However, as the required number of certificates for conventional energy generators increases, both the quantity of certificates that renewable generators obtained and conventional generators purchased increase proportionally. Additionally, the outcomes of limiting the quantity of green certificates awarded to renewable energy generators align with government regulations on the purchase requirements for conventional energy generators. This research provides new insights for power generators in ensuring financial viability and optimizing operations under green certificate trading policies. By enhancing carbon emission reduction capacity, these findings may contribute to the effective management of the electrical supply chain.
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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 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".