Community-Oriented Energy Trading Strategy in Multiagent Cloud Energy Storage Framework
Bibliographic record
Abstract
Cloud energy storage (CES) is a cost-effective solution for residential energy sharing, transforming consumers into self-sufficient ones. This paper uses a multiround seller–buyer matching strategy to introduce an optimized energy management model for end-to-end (E2E) energy trading. The seller–buyer offers the bid multiple times in a time slot. The model considers factors, such as agent load profile, distributed energy resources, user grid cost, energy trading cost investment for individual batteries, and CES. The efficacy of the proposed model is substantiated through simulation. The main highlights are introducing a single-round seller–buyer matching strategy and a multiround seller–buyer matching strategy to determine the market clearing price for E2E energy trading between agents. Simulations show that CES user agents reduce costs, reduce grid energy demand, and increase profit for users, with overall community costs reduced by 36.05% and profit increased by 17.10% with a single-round seller–buyer matching strategy. The proposed trading strategy has also been validated using market data from India and British Columbia, Canada.
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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 | 0.000 | 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) | 0.004 | 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".