Multi-time-scale collaborative optimization strategy of source-grid-load-storage flexibility resources for new energy consumption
Bibliographic record
Abstract
With the increasing integration of new energy sources into the power system, their inherent volatility and intermittency have exacerbated the challenges of energy consumption. This study examines source-grid-load-storage systems that incorporate adjustable loads and decentralized energy storage, including distributed new energy, power grids, air conditioners, and electric vehicles. A multi-time-scale collaborative optimization strategy is proposed to enhance the capacity for new energy consumption. The article investigates the response characteristics and consumption potential of flexible resources across different time scales, namely, monthly, day-ahead, and intraday, and develops a multi-objective optimization model aimed at maximizing new energy consumption while minimizing system operating costs. Corresponding collaborative consumption strategies are formulated for each time scale. Specifically, a multi-time-scale source-grid-load-storage collaborative framework that accounts for the flexibility of demand-side management is initially established. Subsequently, a rolling adjustment method based on multi-objective optimization is proposed for monthly, day-ahead, and intraday operations. Finally, the detailed modeling and collaborative utilization of adjustable loads and decentralized energy storage are achieved. Simulation results demonstrate that the proposed strategy reduces the system’s wind and solar curtailment rate to below 3.5%, decreases operating costs by 12.7%, and significantly improves the system’s economic performance and new energy utilization efficiency.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".