Joint Load Scheduling and Voltage Regulation in the Distribution System With Renewable Generators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
By equipping with the advanced smart meters and two-way communications infrastructure, smart grids, as a key component of future smart cities, are able to improve the energy efficiency and reduce the energy cost through real-time monitoring and customer load scheduling. However, the high penetration of intermittent renewable energy such as solar power may cause frequent overvoltage and undervoltage problems at certain buses, making the load scheduling face new challenges on voltage regulation. In this paper, we investigate the impact of voltage constraints on load scheduling by power flow analysis in a power distribution system with renewable generators. A voltage regulator (VR) is introduced to regulate the voltage of buses in the distribution system and assist load scheduling. To jointly minimize the cost and stabilize the voltages of the distribution system, we propose a grid-customer coordinated load scheduling strategy, which simultaneously determines the tap changes of the VR and scheduling of customer electricity loads in each time slot. Finally, we evaluate the performance of the proposed strategy based on realistic power demand and renewable energy generation datasets. Extensive numerical results demonstrate that the proposed strategy can remarkably reduce the energy cost and stabilize the voltage fluctuation of distribution systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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