Realizing a smart microgrid — Pioneer Canadian experience
Bibliographic record
Abstract
This paper outlines the main accomplishments towards realizing a Smart Microgrid Testbed at the British Columbia Institute of Technology (BCIT), Burnaby, BC, Canada. The paper describes a methodology to optimally select, site, size, control, and integrate distributed generation and storage units with the existing Campus power system. The proposed approach enables optimal operation of the Campus power system in both grid-tied and autonomous modes of operation. Two control strategies are selected, implemented, and tested to permit the safe and efficient integration and operation of the on-site DER units into the Campus power system during the grid-tied and islanded modes of operation. Moreover, a smart and accurate fault detection, isolation, and service restoration (FDISR) algorithm is proposed and implemented, based on a bidirectional communication infrastructure, to support the Campus power system operation in the presence and absence of the utility bus. Finally, the results of one the conducted case studies are reported and analyzed to evaluate the impacts of the proposed power management and control strategies on the performance of the BCIT Microgrid as well as the interaction between different on-site DER units and the utility.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".