Digital Dynamic Agent (DDA) Driven Energy Contract Negotiation
Bibliographic record
Abstract
Abstract Digital Dynamic Agent (DDA) introduces the new paradigm of digital dynamic agent-driven energy contract negotiation, which is dynamic and real-time in adjustment. Long-term static contracts normally produce one-sided traditional market outcomes. On the contrary, DDA driving will be continuous in nature, at a level of rapid negotiations over the underlying transactions. This paper proposes a new generic DDA framework that negotiates energy contracts between grid agents and renewable generation sources, especially wind farms equipped with battery storage. The agents dynamically update their strategy concerning realtime market conditions, demand, and storage capacity. By simulation over one week with real data from the wind farms, it is evident that the DDA framework is better adapted in using battery storage for significantly lesser wastage of energy. Interestingly, the system secured energy at competitive prices in both live energy and capacity markets; it showed an improvement in cost savings compared to conventional static contracts. It also allows for effective energy procurement within diverse market states, which enables greater flexibility and responsiveness in energy management through the optimization of the negotiation process. The current paper highlights the capability of the framework to enhance negotiation outcomes while allowing the broader transition toward renewable energy markets.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".