Enabling System Flexibility in Smart Grid Architecture
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
Electric power grids are witnessing fundamental transformations in their planning and operation paradigms, driven by the ambitious targets of sustainable and decarbonized smart grids. Grid modernization efforts have added several layers of complexity and uncertainty to the grid infrastructure and have affected the fundamental power systems planning and management models. This work adopts a systems engineering (SE) methodology to manage and optimize the grid architecture and maximize its structure flexibility to manage the increasing complexity and uncertainty of modern power grids. We develop a numerical framework that leverages the design structure matrix (DSM) tool to quantify the system-wide impacts of adopting promising emerging technologies based on their readiness levels and grid modernization efforts and evaluate their system integration risks. Using the delta DSM, we propose a technology infusion index metric to assess the risk-importance trade-off for smart grid upgrades on the overall grid structure. The results demonstrate that the impacts on the system differ significantly from the maturity assessments of individual technologies, which often overlook the necessity of function coordination. The developed SE approach offers valuable insights for power system planners and policymakers, equipping them with a strategic and quantifiable framework to prioritize grid investments and optimize overall grid benefits.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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