Bibliographic record
Abstract
Based on the strategic goals of China's energy transition, this report conducts an analysis of the development trend of China's energy and power system in 2020-2060, and proposes the primary energy consumption structure of the new energy system considering 'green electricity plus green hydrogen replacement' .The revolution trend of core indicators such as energy structure, power structure, power generation, and total carbon dioxide emissions are emphatically analyzed.To address the safe and economic operation challenges faced by the new power system, combined with hydrogen energy related technologies, the concept and basic framework of integrated energy production unit (IEPU) in the source end and integrated energy production and consumption unit (IECU) in the receiving end are proposed.Preliminary simulation results show that IEPU is expected to be an effective means to assist the transition of coal-fired power units and address the imbalance between power supply and demand in medium and long term.While the IECU is expected to serve as the basic structural form of future power consumption networks.IEPU and IECU can jointly address the challenges of flexibility and resilience in new energy and power systems.Nevertheless, further economy research and related technological innovation are still needed.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.322 | 0.209 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".