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Power Restoration in Integrated Power and Gas Distribution Grids

2020· article· en· W3023616159 on OpenAlexaff
Abdullah Al-Obaidi, M. Zaki El-Sharafy, Hany E. Z. Farag

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsYork University
Fundersnot available
KeywordsPower to gasGridComputer sciencePower (physics)ArbitrageAutomotive engineeringBusinessEngineeringPhysics

Abstract

fetched live from OpenAlex

Integration of power and gas grids has recently attracted significant attention from researchers around the globe. Utilization of power-to-gas (PtG) and gas-fired generation (GfG) facilities can allow bi-directional energy flow between the two grids. In such a case, PtG and GfG facilities can be jointly operated for multiple purposes including power restoration within an integrated power and gas grid. To that end, this paper proposes a new optimization-based model for power restoration in an integrated power and gas distribution grid. Besides, the model aims to schedule PtG and GfG units for exploiting gas and electricity price arbitrage opportunities in the market. Using historical values of optimization variables, the participation of PtG and GfG facilities in power restoration is measured. A 33-bus power distribution grid is integrated with a 7-node gas grid and employed for numerical studies. The results demonstrate that utilization of PtG and GfG units for power restoration in addition to the main functionality of arbitrage brings a larger revenue for the PtG and GfG units operator. As such, the proposed model in this paper can open up new opportunities for proliferation of PtG and GfG systems and facilitate the integration of power and gas grids in the near future.

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: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.401

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 head0.005
GPT teacher head0.184
Teacher spread0.178 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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