Evaluating Technology Infusion Impacts on Electric Grid Modernization
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
The ambitious targets of sustainable electrification and decarbonized energy systems drive the radical shifts in modern power systems’ planning and operation paradigms. These transformations require the integration of an increasingly large and diverse portfolio of heterogeneous technologies and rethink its system architecture to enable continuous, reliable, and cost-effective power supply. This work aims to embed system flexibility and adaptability in power systems design and planning by mapping innovative technologies and flexibility providers into grid designs and determining their value and associated risks. Specifically, we develop a quantitative framework based on the design structure matrix (DSM) to identify flexible design opportunities and evaluate new electric grid concepts. The DSM analysis enables a system-wide assessment of emerging technologies’ impacts and potential value from a grid architecture perspective. The proposed approach identifies the technologies poised to provide the highest value and quantifies their value-risk trade-offs for system integration using a novel technology infusion index. The research outcome allows power system planners to proactively tackle the grid’s uncertainties by enhancing the complex system flexibility and aligning future investments and modernization strategies.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.017 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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