Evidence map›Paper›PMID 41381615›Full record

ArticleScientific reports2025

Optimal sizing and rule-based management of hybrid microgrids using SSA for rural electrification.

Abdel Djabar Bouchaala, Ahcene Boukadoum, Ahmed A Zaki Diab, Eman Abdullah Aldakheel, Doaa Sami Khafaga, Asma Benhemine

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Abdel Djabar BouchaalaDepartment of Electrical Engineering, Electrotechnical Laboratory Skikda (LES), University 20 August 1955, 26 Road El Hadaiek, 21000, Skikda, Algeria.
Ahcene BoukadoumDepartment of Electrical Engineering, Electrotechnical Laboratory Skikda (LES), University 20 August 1955, 26 Road El Hadaiek, 21000, Skikda, Algeria.
Ahmed A Zaki DiabElectrical Engineering Department, Faculty of Engineering, Minia University, Minia, 61111, Egypt.
Eman Abdullah AldakheelDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
Doaa Sami KhafagaDepartment of Laser Engineering Techniques, Al-Farahidi University, Baghdad, 10021, Iraq. dskhafga@pnu.edu.sa.
Asma BenhemineDepartment of Electric Engineering, Smart Grid and Renewable Energy, University of Bechar Tahri Mohamed, Bechar, Algeria.

Funding

Princess Nourah bint Abdulrahman University Researchers Supporting Project PNURSP2025R409
6 · The paper itself

Abstract

Microgrids play a crucial role in integrating renewable energy sources (RES) into hybrid renewable energy systems (HRES), enabling reliable and sustainable power supply for remote and rural areas. This study investigates the optimal sizing and energy management of an off-grid HRES consisting of photovoltaic (PV) panels, wind turbines (WT), diesel generators (DG), and battery storage systems (BSS), designed to meet the electricity demand of 100 residential homes in a rural area of Skikda, northern Algeria. A rule-based energy management strategy is applied to coordinate power distribution among the microgrid components (PV/WT/DG/BSS), ensuring real-time demand satisfaction. The analysis is based on hourly meteorological data (solar radiation, temperature, and wind speed) over a full year, combined with a hypothetical residential load profile. The Salp Swarm Algorithm (SSA) is employed as the primary optimization technique to minimize the cost of energy (COE) and loss of power supply probability (LPSP). MATLAB-based simulations yield optimal results with a COE of 0.24804 $/kWh, an LPSP of 0.2412% (specifically 0.002412), a total annual cost (TAC) of 245,230 $, and an annual dummy load of 230.57 kWh. To validate the effectiveness of SSA, its performance is compared with Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Moth-Flame Optimization (MFO), and Artificial Rabbits Optimization (ARO). The results demonstrate that SSA achieves faster convergence and superior optimization of the objective function, ensuring efficient energy distribution and reduced operational costs. These findings provide valuable insights for researchers and energy system designers, contributing to the development of cost-effective and reliable off-grid hybrid microgrids for rural electrification.

Indexed as

Cost of energyEnergy management strategy.Hybrid microgridOptimal sizingSalp swarm algorithm

Identifiers

PMID41381615
PMCPMC12800237

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.