Research on cooperative scheduling and power restoration strategy of intelligent operation and maintenance equipment under flood disaster based on dynamic planning
Bibliographic record
Abstract
In order to cope with the damage of urban electricity and the dilemma of residents' electricity consumption caused by flooding disaster, we study the dynamic planning of intelligent operation and maintenance equipment scheduling and distribution network restoration under flooding disaster.Consideration is given to both pre-disaster deployment and post-disaster scheduling levels, while dynamic planning is carried out for collaborative repair and energy storage scheduling to construct a scheduling model with multi-source collaboration.Based on this, a multi-resource cooperative postdisaster recovery strategy for distribution networks is further proposed.The usability of this paper's multi-source cooperative strategy is studied in depth through case analysis.Among the six Cases of the simulation experiment, the total cost in Case 1, which is operated and restored according to the strategy proposed in this paper, is the lowest, which is only 257080.2RMB.The maximum, minimum, and average values of the solution time of the multi-source cooperative strategy are much faster than those of the comparison methods, and it has obvious advantages in fast decision making.The multisource synergy model in this paper is able to recover all the loads within 285 min, while the finite synergy model takes 330 min.The multi-source synergy model was able to recover 7,500 kW of load, while the limited synergy model was only able to recover 6,850 kW.The multi-source cooperative model has strong applicability.
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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 | 0.002 |
| 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".