Study on distributed source-load aggregation strategy and capacity optimization considering power supply security constraints in distribution networks
Bibliographic record
Abstract
With the rapid development of distribution networks in China and the increasing penetration of renewable and traditional energy sources, it is necessary to study the optimal allocation of capacity and optimal operation for the two stages of pre-planning and practical application of distribution networks.In this paper, the probability density function is used to model the uncertainty of "source" and "load" respectively, and the optimal allocation model of distributed power supply capacity of distribution network system is constructed by the equipment models of "wind generator", "photovoltaic generator", "diesel generator" and "battery".Comprehensive cost and power supply security are taken as the objective function and constraints, respectively, to improve the distributed power supply capacity optimization, and adaptive sparrow search algorithm is applied to solve the model.In the comparative analysis of source-load synergy, source-load synergy and energy storage system joint optimization configuration scheme, the joint planning of DPV and ESS enhances the installed capacity of DPV by about 13.45%, and the average power generation of the joint planning scheme is 88.35 kW/h.The joint planning obviously enhances the installed capacity of DPV under the condition of slightly increasing the DPV curtailment.Examples are examined to verify the practical application of the proposed adaptive sparrow search algorithm in configuring the power supply capacity of the hybrid generation system, and the cost of using the cyclic charging operation scheme is 81,067 yuan lower than that of using the load-tracking scheme, and the economic effect has been significantly improved.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".