Mohamed Abdallah Mahmoud Shaheen

Zia Ullah, Marcos Tostado-Veliz, Haoran Ji, Mohammed H. Qais, Saad Alghuwainem, Francisco Jurado

02/05/2023

https://www.sciencedirect.com/science/article/pii/S0360544223011052?dgcid=author

Mohamed Abdallah Mahmoud Shaheen

Zia Ullah, Mohammed H. Qais, K.J. Chua, Marcos Tostado-Véliz, Francisco Jurado, and Mohamed R. Elkadeem

07/11/2022

https://www.mdpi.com/1996-1073/15/21/8303

Mohamed Abdallah Mahmoud Shaheen

Alsaidan, Ibrahim; Alaraj, Muhannad; Alnafisah, Abrar S.

01/11/2022

https://reader.elsevier.com/reader/sd/pii/S2090447922000600?token=1ACCFC21DC000B3B0D8A41756802E2155AF099478CD200DC3DE8189F10E03357367270343253380749D5ADF59E1729E2&originRegion=eu-west-1&originCreation=20220413210527

Mohamed Abdallah Mahmoud Shaheen

Mohammed H. Qais, Saad Alghuwainem, Zia Ullah, Marcos Tostado-Véliz, Francisco Jurado and Mohamed R. Elkadeem

23/08/2022

https://www.mdpi.com/2227-7390/10/17/3036/htm

Mohamed Abdallah Mahmoud Shaheen

Mohammed H. Qais; Saad Alghuwainem; Salah Kamel; Marcos Tostado-Véliz; Francisco Jurado

01/05/2022

www.elsevier.com/locate/energy

Mohamed Abdallah Mahmoud Shaheen

25/10/2021

https://onlinelibrary.wiley.com/doi/full/10.1002/er.6987

Mohamed Abdallah Mahmoud Shaheen

01/03/2021

https://www.sciencedirect.com/science/article/pii/S2090447920301428

Mohamed Abdallah Mahmoud Shaheen

16/02/2021

https://ieeexplore.ieee.org/abstract/document/9354775

Mohamed Abdallah Mahmoud Shaheen

01/01/2021

https://www.mdpi.com/2071-1050/13/14/7911

Mohamed Abdallah Mahmoud Shaheen

01/01/2021

https://www.mdpi.com/1996-1073/14/21/6962

Mohamed Abdallah Mahmoud Shaheen

15/09/2020

https://ieeexplore.ieee.org/abstract/document/9242932

Mohamed Abdallah Mahmoud Shaheen

H. M. Hasanien

01/12/2019

This paper presents a new endeavor of using the Hybrid Firefly and Particle Swarm Optimization (HFPSO) technique in tackling the optimal power flow (OPF) problem for electric power networks. The fuel cost optimization represents the main target considering the system constraints. The decision variable of the OPF problem is chosen to be the generators output real power. The HFPSO technique is chosen to optimize the objective function and to determine the optimal solutions of the problem. Many IEEE test systems are included in this study to assure the soundness of the introduced technique such as the IEEE 14-bus, 30-bus, and 57-bus grids. To acquire a sensible outcome, actual load curves are taken into account during the examination. Simulation results are examined then investigated. They show the appropriateness and privilege of the presented HFPSO -based OPF problem over the genetic algorithm (GA) and the particle swarm optimization (PSO).

Mohamed Abdallah Mahmoud Shaheen

H. M. Hasanien

01/08/2019

This article introduces a new attempt of utilizing the sunflower optimization (SFO) algorithm in solving the problem of optimal power flow (OPF) in the field of power systems. The principle target is to optimize the generating units' fuel cost under the system constraints. At initial stage, the objective function is solved to find the optimal siting of Distributed Generation (DG) units within the system under study. Then, different scenarios are performed to solve the OPF problem including and excluding DG units. The generators' real output power defines the exploration field for the OPF problem. The SFO algorithm is used to minimize the fitness function and yields the best solutions of the problem. More than one electric grid is tested to check the validity of the proposed algorithm such as the IEEE 14-bus, and 30-bus networks. Simulations included different scenarios are implemented in these two networks. To obtain a realistic result, real daily load curve is considered in this study. The results of simulations are investigated and analyzed. Results confirm the flexibility, validation, and applicability of the introduced SFO-based OPF methodology when compared with the genetic algorithm.