Growing International Operations: Multiple Deployments of Multiple Hydrokinetic Power Systems in 2023
Bibliographic record
Abstract
Abstract The objective of this paper is to outline how technical validation of marine hydrokinetic devices is achieved through multiple in-water demonstration projects. The importance of these projects in accelerating the commercialization of the technology by increasing market awareness and adoption is also presented. This paper will outline how a suite of power systems using the same core technology, based around a patented cross-flow hydrokinetic turbine, has been developed to generate power in both river and tidal environments. A pathway advancing the design and testing five generations of turbine technology through fourteen system deployments has been followed, successfully facilitating progress to multiple parallel deployments in an 18 month period across 2022-23. The paper will demonstrate how these projects are being delivered, combining internal engineering, development and operational expertise with partnerships with communities, research organisations, regulators and suppliers. Structured innovation processes for optimizing and advancing novel systems and components are also discussed. The paper outlines the installation and operation of a new hydrokinetic turbine device in Manitoba, Canada, next-generation river turbine units in Millinocket, Maine, and a single turbine tidal energy test system in Eastport, Maine. These are joining an ongoing deployment at Igiugig, Alaska which is demonstrating the performance and survivability of these systems in challenging conditions and proving the model of providing baseload renewable power to remote communities using hydrokinetic turbines. The lessons learned from these projects are described in detail. The success of these demonstration projects will be shown to unlock the growth of the installed hydrokinetic device capacity by up to nine devices in eight locations on multiple continents and countries over the next 12 to 18 months. The paper will outline how management of parallel device deployments has enabled technology developers to significantly enhance their supply chain capacity, increase the volume of devices that can be manufactured and installed, and reduce unit costs as the project pipeline grows. The paper will detail industry-leading approaches to supply chain strategy; project opportunity validation and advancement; regulatory and operations timeline implementation; and community engagement. This will provide insight into approaches that can be replicated across the wider marine energy sector. Deployed devices also enable pilot demonstrations of new market applications for the power produced. Use cases such as point of generation EV charging, decarbonization of onshore industrial facilities, and electrofuels production are outlined.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".