Load aggregation from generation-follows-load to load-follows-generation
Bibliographic record
Abstract
The growing interests in optimizing the generation, distribution, and delivery of electric power have motivated the implementation of several smart grid functions in many power systems around the globe. Among such functions are the peak-load management, demand response, direct load control, and integration of distributed power generation. Nowadays, smart grid functions are being implemented for industrial, residential, and/or commercial loads. One of the key requirements for implementing smart grid functions is the accurate and reliable load aggregation. The bottom-up, coordinated, and bus-split aggregation methods have been found applicable for different load types that are included in smart grid functions. Such a requirement has made aggregation methods main components in power system operation, as well as smart grid functions. This paper overviews the methods and approaches for performing the load aggregation, and provides a discussion for the critical role of load aggregation in power systems operating and smart grid functions. In addition, this paper discusses the applicability of the load aggregation methods in smart grid functions for residential loads.
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".