Accelerating the Commercialization of Marine Renewable Energy Through Parallel Deployments of Multiple Hydrokinetic Power Systems
Bibliographic record
Abstract
Abstract The objective of this paper is to outline how the technical validation and market uptake of marine hydrokinetic devices are being accelerated through the demonstration of the technology across multiple operating environments and use cases. This paper will describe how a single cross-flow hydrokinetic turbine design has been incorporated into a range of power systems covering multiple market applications across river and tidal operating environments. The technology has been advanced through a design and testing pathway that has spanned seventeen system deployments and has culminated in the parallel operation of six devices across four locations in the US and Canada in 2023. The paper will demonstrate how these projects have been delivered, combining internal engineering, development, and operational expertise with partnerships with communities, research organizations, regulators, and suppliers. The paper outlines the lessons learned from the installation and operation of ongoing hydrokinetic turbine deployments at four distinct river and tidal environments across North America. It will demonstrate how the performance and survivability of the core technology are being validated across a wide range of challenging conditions, and how operations and maintenance capabilities must be scaled up to manage parallel projects safely and effectively. Furthermore, the data and operating experience generated through these installations are helping to develop the business case for the commercial application of these power systems in providing baseload renewable power to remote and island communities as well as in industrial-scale use cases. Finally, the paper will share how these projects are helping to accelerate the growth of the installed hydrokinetic device capacity in 2024 and beyond by opening up new project opportunities of greater scale and commercial impact across global markets, including demonstrations on the Mississippi River, in South America, and Europe.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| 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".