Evidence map›Paper›PMID 38969774›Full record

ArticleScientific reports2024

Enhancing residential energy access with optimized stand-alone hybrid solar-diesel-battery systems in Buea, Cameroon.

Isaac Amoussou, Eriisa Yiga Paddy, Takele Ferede Agajie, Fuseini Seidu Ibrahim, Elsabet Ferede Agajie, Wirnkar Basil Nsanyuy, Mohit Bajaj, Shir Ahmad Dost Mohammadi

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PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. 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

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Isaac AmoussouDepartment of Electrical and Electronic Engineering, Faculty of Engineering and Technology, University of Buea, P.O. Box 63, Buea, Cameroon. amoussouisaac67@gmail.com.
Eriisa Yiga PaddyDepartment of Electrical and Electronic Engineering, Faculty of Engineering and Technology, University of Buea, P.O. Box 63, Buea, Cameroon.
Takele Ferede AgajieDepartment of Electrical and Electronic Engineering, Faculty of Engineering and Technology, University of Buea, P.O. Box 63, Buea, Cameroon.
Fuseini Seidu IbrahimDepartment of Electrical and Electronic Engineering, Faculty of Engineering and Technology, University of Buea, P.O. Box 63, Buea, Cameroon.
Elsabet Ferede AgajieDepartment of Electrical and Electronic Engineering, Faculty of Engineering and Technology, University of Buea, P.O. Box 63, Buea, Cameroon.
Wirnkar Basil NsanyuyDepartment of Electrical and Electronic Engineering, Faculty of Engineering and Technology, University of Buea, P.O. Box 63, Buea, Cameroon.
Mohit BajajDepartment of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun, 248002, India. thebestbajaj@gmail.com.
Shir Ahmad Dost MohammadiDepartment of Electrical and Electronics, Faculty of Engineering, Alberoni University, Kohistan, Kapisa, Afghanistan. sh_ahmad.dm@au.edu.af.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study examined the optimal size of an autonomous hybrid renewable energy system (HRES) for a residential application in Buea, located in the southwest region of Cameroon. Two hybrid systems, PV-Battery and PV-Battery-Diesel, have been evaluated in order to determine which was the better option. The goal of this research was to propose a dependable, low-cost power source as an alternative to the unreliable and highly unstable electricity grid in Buea. The decision criterion for the proposed HRES was the cost of energy (COE), while the system's dependability constraint was the loss of power supply probability (LPSP). The crayfish optimization algorithm (COA) was used to optimize the component sizes of the proposed HRES, and the results were contrasted to those obtained from the whale optimization algorithm (WOA), sine cosine algorithm (SCA), and grasshopper optimization algorithm (GOA). The MATLAB software was used to model the components, criteria, and constraints of this single-objective optimization problem. The results obtained after simulation for LPSP of less than 1% showed that the COA algorithm outperformed the other three techniques, regardless of the configuration. Indeed, the COE obtained using the COA algorithm was 0.06%, 0.12%, and 1% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively, for the PV-Battery configuration. Likewise, for the PV-Battery-Diesel configuration, the COE obtained using the COA algorithm was 0.065%, 0.13%, and 0.39% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively. A comparative analysis of the outcomes obtained for the two configurations indicated that the PV-Battery-Diesel configuration exhibited a COE that was 4.32% lower in comparison to the PV-Battery configuration. Finally, the impact of the LPSP reduction on the COE was assessed in the PV-Battery-Diesel configuration. The decrease in LPSP resulted in an increase in COE owing to the nominal capacity of the diesel generator.

Indexed as

COEDiesel generatorHybrid renewable energyLPSPOptimization algorithm

Identifiers

PMID38969774
PMCPMC11226657

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Registered trials

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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.