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Price allocation of transmission line usage in open access system using mega volt ampere kilometer and mega volt ampere cost method for integrated Nepal power system

2020· article· en· W3015276644 on OpenAlexaff
Prannab Acharya, Namrata Tusuju Shrestha, Brajesh Mishra, Hamidreza Zareipour, Pramish Shrestha

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2020
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsVoltAC powerElectric power systemElectric power transmissionTransmission (telecommunications)Mega-Computer scienceCost allocationPower (physics)Electrical engineeringEngineeringVoltageEconomics

Abstract

fetched live from OpenAlex

Abstract The price allocation of transmission line usage for an open access system considering Integrated Nepal Power System (INPS) has been discussed in this paper using the MVA (mega volt ampere) - KM (kilometer) and MVA cost method. The price allocation has been compared for INPS (Integrated Nepal Power System) and IEEE 14 bus system. The transmission line costs in IEEE 14 bus system is based on average construction and operation cost whereas, the costs in the INPS is an actual cost of the present transmission system. The price has been first calculated for different bus with reference to slack bus and for different bilateral and multilateral transactions, using MW KM - MW cost and MVA KM - MVA cost methods. The active and apparent powers for the base case and transaction cases have been calculated using Newton Raphson Method. The prices from MVA KM - MVA cost method are higher than MW KM - MW cost method for both bilateral transaction and multi-lateral transaction indicating more reactive power support in addition to the real power loading due to transactions in the system. The result shows that MVA KM - MVA cost method requires incentives for reactive power support to the system such as INPS.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.846
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.029
GPT teacher head0.267
Teacher spread0.238 · 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 designBench or experimental
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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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