Wind Power: Integrating Wind Turbine Generators (WTG’s) with Energy Storage
Bibliographic record
Abstract
Energy Storage is the missing link between wind driven power generation and delivering power in a sustainable manner that can be dispatched at times of high demand from the grid. Transmission systems that cover large territories such as in North America are particularly vulnerable, requires additional dedicated transmission and readily dispatcheable backup power systems. The installed capacity of Wind Turbine Generators (WTG’s) in the US and worldwide, while impressive, suffers from a low capacity factor of 30% or less due to the variability and intermittency of wind as the motive force. In 2007 the global installed capacity was 94 GW with a predicted capacity of 136 GW by 2010, 55% would be installed in Europe and 23 % (31 GW) in North America, these numbers could be exceeded, as the US already has over 29 GW installed capacity with 99 GW in planning in the next 10 years. The demand for electricity has considerable daily and seasonal variations and the maximum demand may only last for a few hours each year. As a result, some power plants are required to operate for short periods each year – an inefficient use of expensive plants. Without any additional storage above the present 2.5%, mainly PHS, of the installed base load in the USA, base loaded plants are being detrimentally cycled at higher frequency and the situation is further exacerbated by the latest growing demand for renewable energy such as wind energy. In the US, this capacity has now reached in excess of 29,000 MW [Fig 1] summarized by the American Wind Energy Association (AWEA) projects; in Canada the current 2800 MW projects under consideration or contract will grow to 7400 MW to meet energy objectives set for 2015. Installing larger wind farms, to cover the deficiency of a higher capacity factor, results in high costs per delivered kW/hr. This requires continued tax incentives to deliver “green” energy to the consumers. The full capability of the WTG is never realized, as at high wind speeds, some of the wind energy has to be “spilled” to maintain a smooth delivery profile. Technology improvements have not overcome the “wasted” capacity of these modern marvels except where Hydro or Pumped Hydro Storage (PHS) facilities are utilized. The Hydro power station can compensate for wind variability and intermittency while PHS provides energy storage and delivers power during high demand periods. Wind Energy Storage results in a much higher capacity factor, in effect reducing the cost of delivered kW/hrs., PHS amounts to less than 2.3 % of the current installed 1000 GW generating capacity and will decrease with the increasing addition of wind generation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".