Deployment Effects of Marin Renewable Energy Technologies
Bibliographic record
Abstract
Given proper care in siting, design, deployment, operation and maintenance, marine and hydrokinetic technologies could become one of the more environmentally benign sources of electricity generation. In order to accelerate the adoption of these emerging hydrokinetic and marine energy technologies, navigational and environmental concerns must be identified and addressed. All developing hydrokinetic projects involve a wide variety of stakeholders. One of the key issues that site developers face as they engage with this range of stakeholders is that many of the possible conflicts (e.g., shipping and fishing) and environmental issues are not well-understood, due to a lack of technical certainty. In September 2008, re vision consulting, LLC was selected by the Department of Energy (DoE) to apply a scenario-based approach to the emerging wave and tidal technology sectors in order to evaluate the impact of these technologies on the marine environment and potentially conflicting uses. The project’s scope of work includes the establishment of baseline scenarios for wave and tidal power conversion at potential future deployment sites. The scenarios will capture variations in technical approaches and deployment scales to properly identify and characterize environmental impacts and navigational effects. The goal of the project is to provide all stakeholders with an improved understanding of the potential effects of these emerging technologies and focus all stakeholders onto the critical issues that need to be addressed. This groundwork will also help in streamlining siting and associated permitting processes, which are considered key hurdles for the industry’s development in the U.S. today. Re vision is coordinating its efforts with two other project teams funded by DoE which are focused on regulatory and navigational issues. The results of this study are structured into three reports: 1. Wave power scenario description 2. Tidal power scenario description 3. Framework for Identifying Key Environmental Concerns This is the second report in the sequence and describes the results of conceptual feasibility studies of tidal power plants deployed in Tacoma Narrows, Washington. The Narrows contain many of the same competing stakeholder interactions identified at other tidal power sites and serves as a representative case study. Tidal power remains at an early stage of development. As such, a wide range of different technologies are being pursued by different manufacturers. In order to properly characterize impacts, it is useful to characterize the range of technologies that could be deployed at the site of interest. An industry survey informs the process of selecting representative tidal power devices. The selection criteria is that such devices are at an advanced stage of development to reduce technical uncertainties and that enough data are available from the manufacturers to inform the conceptual design process of this study. Further, an attempt is made to cover the range of different technologies under development to capture variations in potential environmental effects. A number of other developers are also at an advanced stage of development including Verdant Power, which has demonstrated an array of turbines in the East River of New York, Clean Current, which has demonstrated a device off Race Rocks, BC, and OpenHydro, which has demonstrated a device at the European Marine Energy Test Center and is on the verge of deploying a larger device in the Bay of Fundy. MCT demonstrated their device both at Devon (UK) and Strangford Narrows (Northern Ireland). Furthermore OpenHydro, CleanCurrent, and MCT are the three devices being installed at the Minas Passage (Canada). Environmental effects will largely scale with the size of tidal power development. In many cases, the effects of a single device may not be measurable, while larger scale device arrays may have cumulative impacts that differ significantly from smaller scale deployments. In order to characterize these effects, scenarios are established at three deployment scales which nominally represent (1) a small pilot deployment, (2) an early, small commercial deployment, and (3) a large commercial scale plant. For the three technologies and scales at the selected site, this results in a total of nine deployment scenarios outlined in the report.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".