Integration of Wave and Tidal Power into the Haida Gwaii Electrical Grid
Bibliographic record
Abstract
Rising energy demand, fossil fuel costs, and greenhouse gas emissions have led to a growing interest in renewable energy integration. Remote communities, often accompanied by high energy costs and abundant renewable energy resources, are ideal cases for renewable energy integration. The Queen Charlotte Islands, also known as Haida Gwaii, are a remote archipelago off the northwest coast of British Columbia, Canada that relies heavily on diesel fuel for energy generation. An investigation is done into the potential for electricity generation using both tidal stream and wave energy in Haida Gwaii. A mixed integer optimization network model is developed in a Matlab and GAMS software environment, subject to set of system constraints including minimum operational levels and transmission capacities. The unit commitment and economic dispatch decisions are dynamically solved for four periods of 336 hours, representing the four annual seasons. Optimization results are used to develop an operational strategy simulation model, indicative of realistic operator behaviour. Results from both models find that the tidal stream energy resource in Haida Gwaii has a larger potential to reduce energy costs than wave energy; however, tidal steam energy is more difficult to integrate from a system operation point of view and, in the absence of storage, would only be practical at power penetration levels less than 20%.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".