Fairness-Oriented Volt–Watt Control Methods of PV Units for Over-Voltage Suppression in PV-Enriched Smart Cities
Why this work is in the frame
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Bibliographic record
Abstract
The higher integration of photovoltaic (PV) units is an inevitable component of smart city development. Thanks to smart meter devices that can record the exchange of power between the grid and customers, it is expected that homeowners and businesses will tend to install PV arrays on their rooftops and parking lots to benefit from selling power back to the grid. However, the overvoltage issue resulting from high PV penetration is a major challenge that necessitates the active power curtailment of PV units to ensure power grid stability. Fairness-oriented methods aim to minimize the active power of PV units as much as possible, adopting a fairer approach, and then address the PV owner’s satisfaction with fair profit and loss. Maintaining voltage within a limited standard range under very low load conditions while prioritizing PV inverters’ participation in reactive power contribution and attempting to ensure fairer curtailment of active power presents challenges to existing control design approaches. This paper presents twelve new volt–watt curve design methods to achieve these goals and address the challenges. The methods yield polynomial curves, piecewise linear curves, and single linear curves. A unique voltage sensitivity value for each PV inverter is used to determine the control region area and the slope of the curve at the starting point in certain instances. The effectiveness of the proposed methods is discussed by evaluating their capabilities on the 37-bus IEEE system.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it