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Design of an Optimal Hybrid Energy System for a Captive Power Plant in Pakistan

2021· article· en· W4200513501 on OpenAlexaff
Luqman Ahsan, M. Tariq Iqbal

Bibliographic record

Venue2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) · 2021
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsRenewable energyPhotovoltaic systemAutomotive engineeringEnvironmental scienceFossil fuelHybrid systemElectricityCost of electricity by sourceHybrid powerGridEnergy storageCapital costComputer scienceEnvironmental economicsEngineeringElectricity generationPower (physics)Electrical engineeringWaste managementMathematics

Abstract

fetched live from OpenAlex

This paper is about the design and feasibility of a grid-connected hybrid power system for an industrial unit. Due to the increase in greenhouse gases by burning fossil fuels, generating electricity from renewable resources is necessary. Solar energy is dependent on solar irradiance, which varies from site to site. A site (Shafi Texcel Limited) is selected, which is situated on Raiwind Manga Road Lahore, Pakistan. The average load demand is 2415 kW, for which a hybrid captive power plant has been designed. The sources of electricity are Grid, CATERPILLER Gas & Diesel GENSET, and the proposed solar system. For this system, optimization analysis has been carried out using HOMER and PVWatt software. Three different grid-connected cases are considered with 0% renewable energy (RE) constraints, 70% RE constraints, and with battery storage. The system parameters are different for each case, and land requisition analysis has been done using PVWatt. The NPC, cost of energy, capital cost, replacement cost for each case has been discussed in detail. The result shows that the proposed system is suitable for a selected site and can provide a significant saving. At the end, final results and possible future work has been discussed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.755
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.249
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.

Study designTheoretical or conceptual
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

Citations2
Published2021
Admission routes1
Has abstractyes

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