Hierarchical Frequency-dependent Chance Constrained Unit Commitment for Bulk AC/DC Hybrid Power Systems with Wind Power Generation
Bibliographic record
Abstract
As the steady-state frequency of an actual power system decreases from its nominal value, the composite load of the system generally responds positively to lower power consumption, and vice versa. It is believed that this load frequency damping (LFD) effect will be artificially enhanced, i.e., sensitivities of loads with respect to operational frequency will increase, in future power systems. Thus, for wind-integrated power systems, this paper proposes a frequency-dependent chance constrained unit commitment (FDCCUC) model that employs the operational frequency as a dispatching variable so that the LFD effect-based load power can act as a supplemental reserve. Because the frequency deviation is safely restricted, this low-cost reserve can be sufficiently exerted to upgrade the wind power accommodation capability of a power system that is normally confined by an inadequate reserve to cope with uncertain wind power forecasting error. Moreover, when the FDCCUC model is applied to a bulk AC/DC hybrid power system consisting of several independently operated regional AC grids interconnected by DC tie-lines, a hierarchically implemented searching algorithm is proposed to protect private scheduling information of the regional AC grids. Simulations on a 2-area 6-bus system and a 3-area 354-bus system verify the effectiveness of the FDCCUC model and hierarchical searching algorithm.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 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".