Resilient micro energy grids with gas-power and renewable technologies
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 world is moving towards smart energy grid with green and clean infrastructure which will enable efficient bidirectional energy supply with reduced carbon footprint. Due to increasing energy demands and the pressing issues of efficient energy use, there is a real need to increase the penetration of gas technologies in the power grid. The government of Canada and stakeholders are looking for ways to increase the reliability and sustainability of the power grid; and gas-power technologies may provide a solution. This paper explores the integration of gas and renewable energy generation technologies within various electricity generation scenarios with the goal of developing designs for a resilient micro energy grid (MEG). The distinct scenarios are then evaluated using an advanced algorithm to provide optimum scenario depending on various key performance indicators (KPIs). KPIs to be examined include: economic, power quality, reliability, and environmental friendliness. This work is done using three different systems; geographic information system (GIS) for recording transmission/distribution lines and generation data, a database to store the information, and a MATLAB-based algorithm for evaluating scenarios. These systems are synthesized and represented into a graphical user interface (GUI), where the user defines the zone, area and cell for desired output and system parameters to generate distinct scenarios to identify the optimum generation.
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 | 0.000 | 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) | 0.000 | 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