Ahmad Shukri Fazil Rahman
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Preferred name
Ahmad Shukri Fazil Rahman
Official Name
Fazil Rahman, Ahmad Shukri
Alternative Name
Rahman, Ahmad Shukri Fazil
Rahman, Ahmad Shukribin Fazil
Main Affiliation
Scopus Author ID
8328081000
Researcher ID
CQM-4923-2022

Research Output
Now showing 1 - 3 of 3
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Optimal sizing of a hybrid system through particle swarm optimization for rural areas in Iraq

2022-11-01 , Al-Shammari Z.W. , Algeboory A.H. , Al-Jebory S.H. , Taha I.A. , Almukhtar H. , Azizan M.M. , Ahmad Shukri Fazil Rahman , Hasikin K.

In today's modern world, any community has the right to access basic electricity. With this in mind, efforts are being made to provide electric power to even the most remote locations. Solar and wind energy are examples of renewable energy sources that are both clean and versatile. For a distant rural school in south-eastern Iraq, this research presents particle swarm optimization (PSO) to reduce the cost of energy (COE) according to the maximum dependability of a hybrid renewable energy system (HRES) by utilizing an integrated electrical generation system. The suggested hybrid system consists of photovoltaics (PV), wind turbines (WT), and batteries (BT), all of which are subject to a specific investment restriction. Results showed that the optimal sizing of the number of photovoltaics (NPV) is equal to (9), the number of wind turbines (NWT) equal to (6), the number of batteries (NBT) of (29), the cost of energy (COE) (0.536 US$/kwh), loss of power supply probability (LPSP) (0.091%), reliability (REL) (99.909%) and renewable factors (RF) (100%) with (59%) PV penetration, and (41%) WT penetration. As a result, the use of the hybrid system is justified from a technological, economic, and humanitarian standpoint.

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Publication

Assessment of renewable energy sources to generate electricity for remote areas, South Iraq

2022-12-01 , Kother A.H. , Jawad Z.W.A.S.W. , Kother S. , Taha I.A. , Almukhtar H. , Azizan M.M. , Ahmad Shukri Fazil Rahman , Hasikin K.

With the rising need for utilizing renewable energy instead of traditional energies in electricity generation across the world, a broad assessment of these energies' performance is required to make the most of them everywhere. This paper looks at the techno-economics of renewable energy resources for a distant health clinic in a rural location of southern Iraq. Cost, dependability, and availability are the parameters that were considered in this study, which took into consider the power load in this scenario. Because of its efficacy, the particle swarm optimization (PSO) technique was chosen for the suggested study. Results showed that the respective optimal values for number of photovoltaics (NPV) equal to (10), number of wind turbines (NWT) equal to (5), and number of batteries (NBT) of (33), cost of energy (COE) of (0.518 US$/kWh), loss power supply probability (LPSP) of (0.073%), reliability (REL) of (99.927%) and renewable factors (RF) of (100%) with (66 %) solar energy penetration, and (34%) wind energy penetration. Finally, it was discovered that implementing a hybrid renewable energy system (HRES) is an effective way to address the electrical demands of remote rural regions in Iraq and other developing countries with similar climates.

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Techno-Economic Feasibility to Generate Electricity by Using PSO Technique for the Urban City in Iraq: Case Study

2020-01-01 , AL-Shammari Z.W.J. , Azizan M.M. , Ahmad Shukri Fazil Rahman

For developing nations such as Iraq, electricity access in rural areas, especially those which are remote, is limited. Thus, the present study explores the electrical needs of the city of Zerbattiya, Iraq. The proposed system's components include solar panels, wind turbines, diesel generators, and batteries. This research proposes a techno-economically feasible and optimal sizing for each component to generate electricity for the city. Particle swarm optimization (PSO) algorithm was used in this research by using MATLAB. The ideal setting of a hybrid renewable energy system (HRES) is achieved by considering the lowest possible cost of energy (COE) with the highest reliability (REL) and possible value of renewable energy penetration (REP). Results showed that the respective optimal values for NPV (30), NWT (30), NDG (3), NBT (281), cost of energy (COE) was (0.142 US$/KWh), loss of power supply probability (LPSP) was (0.0534 %), reliability (REL) was (99.9466 %) and renewable energy penetration (REP) was (56.35 %). The findings further demonstrate that the algorithm was able to achieve optimal solutions to reduce overall cost, quickly and accurately. In conclusion, implementation of HRES was found to be an apt method of meeting electrical needs of remote rural areas in Iraq, and other developing nations with similar climates.

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