Compressed Air Storage and Wind Energy for Time-of-day Electricity Markets
Bibliographic record
Abstract
As renewable energy generating capacity increases on electricity grids, technology is needed to balance the supply and demand of energy. In order to manage the demand side of electricity, time-of-day (TOD) tariffs are a simple economic mechanism that encourages consumers to smooth the diurnal demand profile by shifting consumption to off-peak times. The management of supply by renewable energy generators can be achieved using energy storage. This study investigates the use of compressed air energy storage (CAES) to de-couple a wind energy converter (WEC) from the electricity grid and manage its power output. Numerical and thermodynamic models simulate the operation of the system. One year of operation is simulated using 10 minute WEC time-step data for varying CAES capacities in order to optimize the economic performance of the total system. By selling electricity according to TOD tariff schedules, the income generated by a 0.8 MW WEC using a 4 MWh CAES system is increased by 30%. The CAES has a round-trip efficiency of 66% and annually experiences 450 deep cycles as it stores 25% of the energy generated by the WEC.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".