Investigation of the Capacity Factor of Weather-Routed Energy Ships Deployed in the Near-Shore
Bibliographic record
Abstract
Abstract The energy ship concept has been proposed as an alternative wind power conversion system to harvest offshore wind energy. Energy ships are ships propelled by the wind and which generate electricity by means of water turbines attached underneath their hull, The generated electricity is stored on-board (batteries, hydrogen, etc.) It has been shown that energy ships deployed far-offshore in the North Atlantic Ocean may achieve capacity factors over 80% using weather-routing. The present paper complements this research by investigating the capacity factors of energy ships harvesting wind power in the near-shore. Two case studies are considered: the French islands of Saint-Pierre et-Miquelon, near Canada, and Ile de Sein, near metropolitan France. The methodology is as follows. First, the design of the energy ship considered in this study is presented. It was developed using an in-house Velocity, and Power Performance Program (VPPP) developed at LHEEA. The velocity and power production polar plots of the ship were used as input to a modified version of the weather-routing software QtVlm. This software was then used for capacity factor optimization using 10m altitude wind data analysis which was extracted from the ERA-Interim dataset provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three years (2015, 2016, and 2017) data are considered. The results show that average capacity factors of approximately 40% and 40% can be achieved at Ile de Sein and Saint-Pierre-et-Miquelon with considered energy ship design.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".