Stochastic Information Management in Smart Grid
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
Rising concerns about the efficiency, reliability, economics, and sustainability in electricity production and distribution have been driving an evolution of the traditional electric power grid toward smart grid. A key enabler of the smart grid is the two-way communications throughout the power system, based on which an advanced information system can make optimal decisions on power system operation. Due to the expected deep penetration of renewable energy sources, energy storage devices, demand side management (DSM) tools, and electric vehicles (EVs) in the future smart grid, there exist significant technical challenges on power system planning and operation. Specifically, efficient stochastic information management schemes should be developed to address the randomness in renewable power generation, buffering effect of energy storage devices, consumer behavior patterns in the context of DSM, and high mobility of EVs. In this paper, we provide a comprehensive literature survey on the stochastic information management schemes for the smart grid. We start this survey with an introduction to the smart grid system architecture and the technical challenges in information management. Various component-level modeling techniques are presented to characterize the sources of randomness in the smart grid. Built upon the component-level models, we further explore the system-level stochastic information management schemes for smart grid planning and operation. Future research directions and open research issues are identified.
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 | 0.003 | 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 | 0.001 | 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