INTEGRATING WAVE AND TIDAL CURRENT POWER: Case Studies Through Modelling and Simulation
Bibliographic record
Abstract
Ocean renewable energy is an emerging resource option. In the long term, ocean renewableenergy has the potential to provide a significant share of global energy needs. Currently,some of the conversion technologies for harnessing variable wave and tidal current energyresources are reaching commercial stage. Several pilot projects, having sizes upto 2 MW,are operating in various parts of the world. Also multi-MW wave and tidal current energyfarms are being developed. Identification of the near- and longer-term technical potentialof wave and tidal current resources that could be integrated to existing and futureelectricity infrastructure in a region is an important step towards developing integratedlong-term energy planning for the region and relevant policy instruments to realize thepotential.During the past three years, a collaborative project related to integration of wave and tidalcurrent energy into electrical systems (known as Annex III) was carried out under theumbrella of the International Energy Agency’s Implementing Agreement on Ocean EnergySystems (OES-IA) (www.iea-oceans.org). Following the completion of the Work Packages1 and 2 under the Annex III of IEA’s Ocean Energy Implementing Agreement, a numberof landmark activities took place within the emerging global ocean energy sector. Thisreport summarizes the work performed through the Work Package 3 activities. The reportprovides insight into the grid integration of wave and tidal current resources, particularlythrough case studies spanning a wide range of scenarios. Prior to discussing these case studies, a number of generic power system related aspects (such as system control, stability, power quality, grid codes, etc.) are highlighted in thisreport. In addition, brief discussions are presented on wave and tidal resourcevariability/predictability, offshore farm layout, system control and characteristics, plantlocation (in contrast to the location of load centers, network topology, etc.).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".