Real-Time Integration of Intermittent Generation With Voltage Rise Considerations
Bibliographic record
Abstract
In the modern electric power grid, a commonly observable recent phenomenon is the increasing penetration of renewable generation sources especially at the distribution network (DN) level. The traditional DN is not designed for bidirectional power flow induced by these volatile sources and, therefore voltage rise is a major concern. In order to enable mass renewable integration into any type of existing radial DN without requiring expensive line/bus upgrades and avoiding adverse effects of voltage rise, these generation sources (with possible nonconvex discrete output levels) must be dispatched in real-time while taking into account nonconvex voltage constraints. Ubiquitous connectivity between power components is available in today's grid due to the cyber-physical nature of these devices. We leverage this to propose a distributed algorithm based on principles of population games for efficient dispatch that minimizes dependence of the DN on the main grid for sustainable system operation. Theoretical and simulation studies show that the proposed algorithm allows for the seamless coexistence of a large number of renewables that are highly responsive to fluctuations in demand and supply with strong convergence properties while successfully mitigating voltage rise issues.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".