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Record W2793855145 · doi:10.3390/en11040709

Energy Non-Availability in Distribution Grids with Heavy Penetration of Solar Power: Assessment and Mitigation through Solar Smoother

2018· article· en· W2793855145 on OpenAlexaboutno aff
Tathagata Sarkar, Ankur Bhattacharjee, K. Mukhopadhyay, Konika Das Bhattacharya, Hiranmay Saha

Bibliographic record

VenueEnergies · 2018
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsnot available
FundersDepartment of Science and Technology, Ministry of Science and Technology, IndiaMinistry of New and Renewable Energy IndiaIndian Institute of Engineering Science and Technology, Shibpur
KeywordsSolar irradiancePhotovoltaic systemEnvironmental scienceGrid-connected photovoltaic power systemIrradianceSolar powerSolar energyGrid parityGridRenewable energyDistributed generationPower (physics)Electrical engineeringEngineeringMaximum power point trackingVoltageMeteorologyPhotovoltaicsPhysicsMathematics

Abstract

fetched live from OpenAlex

Rapid fluctuation of solar irradiance due to cloud passage causes corresponding variations in the power output of solar PV power plants. This leads to rapid voltage instability at the point of common coupling (PCC) of the connected grid which may cause temporary shutdown of the plant leading to non-availability of energy in the connected load and distribution grid. An estimate of the duration and frequency of this outage is important for solar energy generators to ensure the generation and performance of the solar power plant. A methodology using PVsyst (6.6.4, University of Geneva, Geneva, Switzerland) and PSCAD (4.5, Manitoba HVDC Research Centre, Winnipeg, MB, Canada) simulation has been developed to estimate the duration and frequency of power outages due to rapid fluctuation of solar irradiance throughout the year. It is shown that the outage depends not only on the solar irradiance fluctuation, but also on the grid parameters of the connected distribution grid. A practical case study has been done on a 500 kilo Watt peak (kWp) solar PV power plant for validation of the proposed methodology. It is observed that the energy non-availability for this plant is about 13% per year. This can be reduced to 8% by incorporating a solar smoother. A financial analysis of this outage and its mitigation has also been carried out.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.529
Threshold uncertainty score0.873

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 head0.007
GPT teacher head0.252
Teacher spread0.245 · 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".

Quick stats

Citations12
Published2018
Admission routes1
Has abstractyes

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