Novel Approach for a Tidal Energy Resource Assessment within Long Island Sound Using a Spatial Multi-Criteria Decision Analysis Process
Bibliographic record
Abstract
The marine environment is a vast energy resource with the potential for supporting the world’s growing energy demand. Increasing the number of marine energy (ME) projects diversifies renewable energy portfolios, supporting decarbonization and energy security. Most of the energy potential along the U.S. coastline is in the <5 MW range, especially for tidal energy. On the opposite end of the spectrum, the UK Marine Energy Council has a target of 1 GW from tidal energy by 2035. However, the lack of robust and coherent site information, globally, leads to ignored resources, misdirected technology development, and perception of high project risk for stakeholders. To address this gap, a TEAMER (Testing & Expertise for Marine Energy) funded study was conducted to assess the pre-feasibility of tidal energy in Long Island Sound, an area bordered by the Connecticut and New York coastlines with representative characteristics to many U.S. tidal energy sites. The work used a novel spatial data analysis approach and development of a geodatabase to identify the area’s tidal energy resource potential. The geodatabase, at the foundation of the methodology, integrates numerical model based probability distributions of tidal currents as well as relevant geospatial data including, but not limited to, natural resources, bathymetry, oceanographic conditions, existing infrastructure, and socioeconomic data layers throughout the region of interest. Potential tidal energy siting limitations and opportunities were examined through a spatial multi-criteria decision analysis process. This involved the identification of key data layers and weighting factors to identify the areas most suited for tidal energy devices. The primary data layer was the available tidal resource, which directly influences the potential energy output. Power matrices for multiple generalized tidal turbines were included to calculate estimated power and mean annual energy production using international standards. Other data layers incorporated into the decision analysis library included binary exclusion zones such as marine protected areas and shipping lanes, ‘cost’ factors such as distance to transmission connection points or cable landing areas, and ‘benefit’ factors such as population density. Existing renewable energy infrastructure was also considered, as it impacts the ease of integration and distribution of the generated power. Areas of stakeholder concern, including environmental and socio-economic factors, were considered to ensure sustainable ME development. This novel methodology provided detailed insights into potentially suitable areas for tidal energy extraction. Despite the current limitations of tidal turbine technologies, our novel geodatabase and spatial analysis toolkit identified previously overlooked tidal energy sites along Connecticut's coastline. This approach assumes that future technologies will be optimized for lower energy environments, highlighting the potential for advancement in tidal energy. The findings demonstrate the efficacy of using automated geospatial processing and spatial analysis tools in ME resource assessments. The application of quantitative marine spatial planning techniques enables more effective planning of tidal energy infrastructure and improved technology development. Future plans include applying the method to other coastal regions worldwide and including considerations for other ME conversion technologies such as wave energy.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".