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Record W4206292675 · doi:10.1051/e3sconf/202132901026

Research on Energy Storage Cost Model in Distributed Energy System Environment

2021· article· en· W4206292675 on OpenAlexaff
Shuo Yin, Zhe Chai, Xing Chen, Meng Yang, Yao Lu, Kyle Georgiou

Bibliographic record

VenueE3S Web of Conferences · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsUniversity of Waterloo
FundersState Grid Corporation of China
KeywordsEnergy storageMarketizationEnergy (signal processing)Distributed generationIntermittent energy sourceEnvironmental economicsComputer scienceReliability engineeringPower (physics)Renewable energyEngineeringElectrical engineeringEconomicsChina

Abstract

fetched live from OpenAlex

With the "dual carbon" goal proposed and the direction of building a new power system dominated by new energy sources clarified, the energy storage industry has entered the fast lane of accelerated development. However, due to the imperfect marketization mechanism and the lack of effective power market ancillary service policies, it is difficult to 00EFfectively recover the cost of energy storage construction, which restricts the development of the energy storage industry to a certain extent. Therefore, it is urgent to clarify the development trend and cost recovery mechanism of typical types of energy storage under the distributed energy system, and clarify the impact of the operating cost of the electrochemical energy storage system. This paper systematically analyses the domestic and foreign energy storage investment channelling mechanisms, clarifies the impact of energy storage cost system operation on costs, and helps the development of the energy storage industry.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.052
GPT teacher head0.294
Teacher spread0.242 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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