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Record W1970370622 · doi:10.1109/tsg.2014.2317800

Total Cost of Ownership and Risk Analysis of Collaborative Implementation Models for Integrated Fiber-Wireless Smart Grid Communications Infrastructures

2014· article· en· W1970370622 on OpenAlexaff
Ramzi Charni, Martin Maier

Bibliographic record

VenueIEEE Transactions on Smart Grid · 2014
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsSmart gridComputer scienceTotal cost of ownershipTelecommunicationsExploitGridRenewable energyWirelessComputer networkComputer securityEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Most previous total cost of ownership (TCO) studies give insight into the overall costs of various communications network architectures under the assumption of the traditional vertical integration model. However, it is necessary to take a closer look at the possibilities and gains achieved through collaboration. In this work, we develop a flexible, generic yet comprehensive TCO framework for the rollout of integrated fiber-wireless (FiWi) smart grid communications infrastructures. Further, we propose a novel collaborative implementation model for a shared infrastructure for both broadband access and smart grid communications. In addition, we take into account that buildings currently shift from a product to a service (i.e, renewable power supply) and exploit the idea that housing companies may collaborate by offering surplus renewable energy to communications network providers across interconnected smart microgrids. We study the impact of our implementation model on TCO and compare it with the vertical integration model. We also conduct a sensitivity analysis for the key cost parameters and analyze the risks related to solar power intermittency and random fiber cuts in terms of power service penalty, fiber cut related costs, and TCO.

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 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 score0.736
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.015
GPT teacher head0.254
Teacher spread0.239 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations17
Published2014
Admission routes1
Has abstractyes

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