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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".