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Record W4220972753 · doi:10.36227/techrxiv.19319705

Blockchain for Energy Systems: Bridging Top-down and Bottom-up Management of the Electricity Grid

2022· preprint· en· W4220972753 on OpenAlexaff
Sakshi Mishra, Roohallah Khatami, Yu Christine Chen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRestructuringGridElectricityComputer scienceBridging (networking)Smart gridTop-down and bottom-up designDistributed computingBusinessEngineeringMathematicsElectrical engineeringComputer networkGeometry

Abstract

fetched live from OpenAlex

Point-of-view article. Abstract: For more than a century, the grid has operated in a centralized top-down fashion. However, as the penetration of the distributed energy resources (DERs) grows, the grid edge is increasingly infused with intelligent computing and communication capabilities. Thus, the bottom-up approach to grid operations inclined toward decentralization of energy systems will likely gain momentum alongside the existing centralized paradigm. In this point of view article, we aim to highlight the need for and outline a credible path toward restructuring the current operational architecture of the electricity grid in view of the ongoing decentralization trends with an emphasis on peer-to-peer energy trading. We further suggest that blockchain is an effective technology for facilitating the synergistic operations of top-down and bottom-up approaches to grid management.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0050.008
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0130.003

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 head0.010
GPT teacher head0.230
Teacher spread0.220 · 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 designTheoretical or conceptual
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
Published2022
Admission routes1
Has abstractyes

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