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Record W2392287197

Optimization models for Beijing's generation expansion planning under uncertainties

2010· article· en· W2392287197 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePower System Protection and Control · 2010
Typearticle
Languageen
FieldEnergy
TopicPower Systems and Renewable Energy
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsElectricityBeijingElectricity generationReliability (semiconductor)Interval (graph theory)EngineeringReliability engineeringElectric power systemElectric powerCoalElectricity marketEnvironmental economicsOperations researchPower (physics)Computer scienceElectrical engineeringEconomicsChinaMathematicsWaste management
DOInot available

Abstract

fetched live from OpenAlex

Generation expansion planning,which plays a vital role in electric power system,has a great influence on the reliability,economics,electricity quality,network structure and development of electric power system in the future.In this study,an uncertainty optimization model for Beijing's generation expansion planning is developed.An interval parameter integrating mixes-integer programming techniques is introduced,which can effectively handle not only the uncertainties expressed as interval numbers,but also the installed power generation capacity-expansion issues.And the application shows that a diversified electricity structure,which is supplied through coal fired electricity and nature gas fired electricity mainly,new energy and reproducible energy etal as a supplement,will be established.It would provide steady and plenteous electricity for the economic and social development of Beijing,and also,for improving our people's lives.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.224
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it