Evidence map›Paper›PMID 30984252›Full record

ArticleComputational intelligence and neuroscience2019

Optimal Operation of the Hybrid Electricity Generation System Using Multiverse Optimization Algorithm.

Muhammad Sulaiman, Sohail Ahmad, Javed Iqbal, Asfandyar Khan, Rahim Khan

Abstract read
In one paragraph

Article in Computational intelligence and neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Muhammad SulaimanDepartment of Mathematics, Abdul Wali Khan University Mardan, Mardan, KP, Pakistan.ORCID 0000-0002-4040-6211
Sohail AhmadDepartment of Mathematics, Abdul Wali Khan University Mardan, Mardan, KP, Pakistan.
Javed IqbalDepartment of Mathematics, Abdul Wali Khan University Mardan, Mardan, KP, Pakistan.
Asfandyar KhanDepartment of Mathematics, Abdul Wali Khan University Mardan, Mardan, KP, Pakistan.ORCID 0000-0002-0336-4809
Rahim KhanDepartment of Computer Science, Abdul Wali Khan University Mardan, Mardan, KP, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ongoing load-shedding and energy crises due to mismanagement of energy produced by different sources in Pakistan and increasing dependency on those sources which produce energy using expensive fuels have contributed to rise in load shedding and price of energy per kilo watt hour. In this paper, we have presented the linear programming model of 95 energy production systems in Pakistan. An improved multiverse optimizer is implemented to generate a dataset of 100000 different solutions, which are suggesting to fulfill the overall demand of energy in the country ranging from 9587 MW to 27208 MW. We found that, if some of the power-generating systems are down due to some technical problems, still we can get our demand by following another solution from the dataset, which is partially utilizing the particular faulty power system. According to different case studies, taken in the present study, based on the reports about the electricity short falls been published in news from time to time, we have presented our solutions, respectively, for each case. It is interesting to note that it is easy to reduce the load shedding in the country, by following the solutions presented in our dataset. Graphical analysis is presented to further elaborate our findings. By comparing our results with state-of-the-art algorithms, it is interesting to note that an improved multiverse optimizer is better in getting solutions with lower power generation costs.

Indexed as

AlgorithmsElectricityElectric Power SuppliesNeural Networks, ComputerPakistanProblem Solving

Identifiers

PMID30984252
PMCPMC6432705

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.