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Record W4407948556 · doi:10.1109/mpe.2024.3427727

Macrogrids and Supergrids: Wide Area Transmission to Improve Electrification and Variable Renewable Energy Use

2025· article· en· W4407948556 on OpenAlexaff
D.A. Woodford

Bibliographic record

VenueIEEE Power and Energy Magazine · 2025
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsElectrovaya (Canada)
Fundersnot available
KeywordsRenewable energyElectrificationVariable (mathematics)Variable renewable energyComputer scienceEnvironmental economicsEnvironmental scienceElectrical engineeringEngineeringEconomicsElectricityEnergy storagePhysicsMathematicsPower (physics)

Abstract

fetched live from OpenAlex

Studies have shown that wide area high-voltage dc (HVdc) electric power grids will be needed for the future. In North America, these are known as “macrogrids” and in Europe, “supergrids,” which are sometimes referred to as “hypergrids.” There are obstacles in the way to achieving these wide area grids, however. What are these obstacles, and how can they be overcome? Such obstacles include passing through multiple jurisdictions and obtaining permitting. The NIMBY challenge is one such issue. Of course, a wide area transmission grid will pass through different countries and states, adding to the permitting challenge. The processes that are being applied include the use of rights-of-way of existing railroads or highways. Underwater locations may include those beneath lakes and rivers as well as those undersea. In the United States, the right-of-way of a rail line is being used to traverse several states for 2,100-MW, ±525-kV HVdc cables. Roadside rights-of-way are also under active consideration for HVdc cables for the macrogrid.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.003
GPT teacher head0.172
Teacher spread0.169 · 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 designNot applicable
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

Citations1
Published2025
Admission routes1
Has abstractyes

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