A review of global tidal stream energy resources
Bibliographic record
Abstract
This review identifies 426 sites with potentially suitable characteristics for tidal stream energy development, across nineteen countries in Europe, the Americas, Asia and Australasia. The most common site assessment quantifies the theoretical resource, which is the maximum amount of total energy that can be extracted by filling a site with turbines. This includes not only the energy extracted directly for electricity production, but also the energy losses resulting from support structure drag, drive train losses and wake mixing. The aggregated theoretical resource estimate is 1,000 TWh/year, from 262 sites (62% of those identified), across six countries. A more informative, albeit less common, indicator of electricity generation potential, is provided by practical resource studies, that estimate annual electricity production once economic, environmental, regulatory and social constraints are taken into account. Practical resource assessments are limited mainly to UK sites, with an estimated resource of 34 TWh/year, equivalent to 10% of the theoretical resource estimate. Of the seven countries with sufficient resource information, sites in the UK, Indonesia and New Zealand show the greatest potential to make national-scale electricity supply contributions. Resource assessment in France, Canada, USA and China indicates regional-scale impact potential, with possible further resource that may be developed, depending on future resource assessment of an additional 80 sites. For countries limited to resource assessment of ambient flow characteristics only, sites in Norway, Faroe Islands, Japan, South Korea and the Philippines show the greatest energy potential. This review brings to light the inconsistent and wide ranging site selection criteria and practical constraints that are adopted in the literature. This is reflective of uncertainty in (i) what constitutes a suitable site, (ii) how site suitability evolves over time (e.g., with changing competing energy costs in the energy sector), and (iii) the level of energy that can practically be extracted. When these uncertainties are combined, along with uncertainty in resource data, and the magnitude of energy losses, reported P10 and P90 resource estimates can lie 43% below and 30% above the P50 value respectively. It is recommended that future resource assessment characterises how the resource magnitude is impacted by site selection criteria/practical constraint ranges, that acknowledge and reflect their time-dependency and uncertainty.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".