A quantitative assessment method for regulating capacity sufficiency under medium- and long-term supply and demand risk scenarios of provincial power grids with high proportion of new energy sources driven by multilevel temporal and spatial combinatorial optimization algorithms
Bibliographic record
Abstract
The regulatory capacity sufficiency of the grid is not only a technical indicator for dispatchers to measure the safe and stable operation of the grid, but also an important indicator for assessing the reliability of the grid, and an important basis for the planning and transformation of the grid.This paper combines the objective function and constraints of time and space scale optimal scheduling of provincial power grids with a high proportion of new energy, and establishes a model for optimal scheduling of power grids.Improved DE-ICA stochastic optimization search algorithm is used to seek the optimal value of the model, to obtain the optimal regulation method of the power grid driven by the multilevel spatio-temporal combinatorial optimization algorithm, and to put forward the quantitative assessment method of the adequacy of the power grid regulation capacity.Simulations and empirical case studies show that the regulation cost of the provincial grid is reduced after the application of the optimization algorithm, and the power balance effect and the regulation capacity adequacy are improved compared with the traditional scheme.The quantitative evaluation method of grid regulation capacity adequacy proposed in this study can comprehensively and accurately describe the transmission capacity of the grid under long-term supply and demand, which can provide more accurate reference information for power system security and planning.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".