The Economics of Tidal Stream Power
Bibliographic record
Abstract
Renewable solar, tidal and wind energy have the potential of reducing dependency on fossil fuels and their environmentally negative impacts. Because of their variability, wind and solar energy in particular impose added costs on electrical grids as system operators attempt to balance operation of existing thermal power plants. In this regard, tidal stream power has an advantage over solar and wind energy as tides are predictable and comparatively regular; yet, tides remain intermittent and thereby still may create inefficiencies to the grid. In this paper, we develop a dynamic optimization framework for analyzing the allocation of power output across generating sources when tidal and wind power are added to the system. In particular, we minimize the cost of satisfying the 2006 British Columbia electricity demand. We use tidal current and wind data from sites around Vancouver Island to estimate the effects of an increase in renewable energy penetration into grids consisting of three typical generating mixes – the British Columbia generation mix that has a significant hydropower component, the Alberta generating mix with a coal-fired power dominance, and the Ontario generation mix which includes significant nuclear and coal-fired generation. Simulation results over an entire year (hourly time step) indicate that the cost of electricity will increase from its current levels by between 73% and 150% at renewable penetration rates of 30% depending on the assumed generating mix. The cost of reducing CO2 emissions ranges from $97.47 to $1674.79 per tonne of CO2, making this an expensive way of mitigating emissions. The reasons for these high costs are increased inefficiencies from standby spinning reserves and operation of plants at less than optimal levels (so that more fuel is burned per unit of electricity). Further, it is impossible to determine the displacement of emissions by renewable energy without considering the complete operating system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".