A Market‐Oriented Trading Method for Integrated Community Energy System Based on Hierarchical Stackelberg Game Method
Bibliographic record
Abstract
In response to escalating environmental concerns and the shortage of traditional energy resources, it is essential to enhance energy management within multienergy markets to facilitate the integration of renewable energy sources with end‐users. To this end, our research focuses on the optimization of community‐level integrated energy systems (CIESs) equipped with combined cooling, heating, and power (CCHP), utilizing a multiagent system (MAS) and a hierarchical Stackelberg game approach. The research begins with the development of an MAS‐based optimization framework for game participants, followed by the establishment of a Stackelberg game model where the multienergy seller acts as the leader and the consumer as the follower. The model is thoroughly validated through the formulation and solution of a mixed‐integer linear program (MILP)–based multiobjective optimization, which not only demonstrates effective strategies for optimizing power dispatch but also enhances economic returns and ensures fast game convergence. The results significantly validate the approach of maximizing the utilization of clean energy in energy transactions, clarifying the dynamic relationship between price fluctuations and load in the pricing mechanism. These insights are vital for promoting environmental sustainability and conserving resources, proving essential for future energy management practices.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".