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Changing in Economic Value of Photovoltaic Systems Based on Penetration Level

2019· article· en· W3036391187 on OpenAlexaff
M.M.S. Dezfouli, Mousa Sheikhhoseini, Masoud Rashidinejad, Alireza Bakhshai

Bibliographic record

Venue2019 IEEE 6th International Conference on Engineering Technologies and Applied Sciences (ICETAS) · 2019
Typearticle
Languageen
FieldComputer Science
TopicSolar Radiation and Photovoltaics
Canadian institutionsQueen's University
Fundersnot available
KeywordsPhotovoltaic systemIncentiveGridElectricityTariffEnvironmental economicsElectricity generationComputer scienceBusinessAutomotive engineeringEnvironmental scienceElectrical engineeringEngineeringEconomicsMicroeconomicsPower (physics)Mathematics

Abstract

fetched live from OpenAlex

In Iran, residential Photovoltaic (PV) generation has received continuous support from government in the recent for several years. Government gives an incentive program in the form of feed in tariff (FIT) mechanism. At more penetration, PV output creates major operational problems to the current grid that is operated for unidirectional power flow, which in this case unbalancing between demand and PV output increases. Thus, with enhancing penetration level of PV units, the support polices should be modified. In this context, the present paper aims to concentrate on designing a suitable incentive mechanism with respect to the penetration theory of PV systems in distribution systems. In doing so, first, the economic terms including the value of generated energy and capacity value are estimated using the electricity generation profile of PV systems and electricity grid conditions. By the way, to analyze the PV output, direct insolation simulation code (DISC) is applied for prediction of solar radiation as the key contribution of system advisor model (SAM) software. The findings show that the level of incentives is crucially linking with the RES penetration rate.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score0.601

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.032
GPT teacher head0.245
Teacher spread0.213 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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