Dynamic Wide Area Situational Awareness: Propelling Future Decentralized, Decarbonized, Digitized, and Democratized Electricity Grids
Bibliographic record
Abstract
The millions of consumer-owned distributed energy resources (DERs) forecast for the grid by 2050 will trigger a major system change away from centralized monopolistic utilities to decentralized community projects exploiting innovative business models. Such a disruptive change, needed to switch to a carbon-neutral economy, requires rethinking both the economics and dynamics of power systems, knowing that the electric power sector is going digital. DER-driven uncertainties will impose costly operational margins and preventive measures based on solving very complex optimization problems. Keeping human operators in the loop to supervise actions can limit reaction time severely by making it difficult to respond in a timely manner when multiple control systems are required to stabilize the grid. The operator will hence need to be assisted by an artificial intelligence system trained at learning “good” decisions by imitating operators and assessing the associated operational risk. Hierarchical monitoring and control systems working in tandem with decentralized markets and resources will allow the true secure limits of decentralized, decarbonized, digitized, and democratized (4D) grids to be identified. This will be done with preventive/corrective actions executed in seconds while balancing grid cost versus safety, reliability, and stability.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".