Virtual energy flow-based carbon emission optimization and hybrid game model for multi-park integrated energy systems
Bibliographic record
Abstract
To advance sustainable energy management in Park Integrated Energy Systems (PIESs), this paper proposes a hybrid game model among multiple PIESs to reduce carbon emissions under carbon emission quota (CEQ) policies. We introduce a virtual energy flow-based carbon emission optimization (VEF-CEO) method, where the virtual energy flow refers to an energy allocation determined by trading contracts rather than physical transmission paths. This approach linearizes emission calculations and resolves locational carbon price disparities. Within PIES, a Stackelberg game model transfers CEQ assessment costs to the load side, clarifying carbon reduction responsibilities and enhancing collaborative effects. Among PIESs, a cooperative game model improves CEQ satisfaction and economic benefits through coordination. The Karush-Kuhn-Tucker (KKT) conditions transform the Stackelberg model into a single-level model, and the Augmented Lagrange based Alternating Direction Inexact Newton (ALADIN) method is employed for non-convex model distributed solving. Case study demonstrates that cooperative strategies increase revenues by 21.4% and 51.3% for two PIESs respectively, and achieve complete wind power accommodation. The Stackelberg game successfully steers user consumption via price signals, and the VEF-CEO method outperforms traditional methods in fairness and computational efficiency. These findings validate the effectiveness of hybrid game approach, VEF-CEO method, and ALADIN algorithm for PIES optimization.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".