Solving Non-Smooth Optimal Power Flow Problems Using a Developed Grey Wolf Optimizer
The optimal power flow (OPF) problem is a non-linear and non-smooth optimization problem. OPF problem is a complicated optimization problem, especially when considering the system constraints. This paper proposes a new enhanced version for the grey wolf optimization technique called Developed Grey W...
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doaj-8643c93f425741fb9d09e280e5f2318b2020-11-24T23:58:59ZengMDPI AGEnergies1996-10732018-06-01117169210.3390/en11071692en11071692Solving Non-Smooth Optimal Power Flow Problems Using a Developed Grey Wolf OptimizerMostafa Abdo0Salah Kamel1Mohamed Ebeed2Juan Yu3Francisco Jurado4Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, EgyptDepartment of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, EgyptDepartment of Electrical Engineering, Faculty of Engineering, Sohag University, Sohag 82524, EgyptState Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400030, ChinaDepartment of Electrical Engineering, University of Jaén, EPS Linares, 23700 Jaén, SpainThe optimal power flow (OPF) problem is a non-linear and non-smooth optimization problem. OPF problem is a complicated optimization problem, especially when considering the system constraints. This paper proposes a new enhanced version for the grey wolf optimization technique called Developed Grey Wolf Optimizer (DGWO) to solve the optimal power flow (OPF) problem by an efficient way. Although the GWO is an efficient technique, it may be prone to stagnate at local optima for some cases due to the insufficient diversity of wolves, hence the DGWO algorithm is proposed for improving the search capabilities of this optimizer. The DGWO is based on enhancing the exploration process by applying a random mutation to increase the diversity of population, while an exploitation process is enhanced by updating the position of populations in spiral path around the best solution. An adaptive operator is employed in DGWO to find a balance between the exploration and exploitation phases during the iterative process. The considered objective functions are quadratic fuel cost minimization, piecewise quadratic cost minimization, and quadratic fuel cost minimization considering the valve point effect. The DGWO is validated using the standard IEEE 30-bus test system. The obtained results showed the effectiveness and superiority of DGWO for solving the OPF problem compared with the other well-known meta-heuristic techniques.http://www.mdpi.com/1996-1073/11/7/1692power system optimizationoptimal power flowdeveloped grew wolf optimizer |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mostafa Abdo Salah Kamel Mohamed Ebeed Juan Yu Francisco Jurado |
spellingShingle |
Mostafa Abdo Salah Kamel Mohamed Ebeed Juan Yu Francisco Jurado Solving Non-Smooth Optimal Power Flow Problems Using a Developed Grey Wolf Optimizer Energies power system optimization optimal power flow developed grew wolf optimizer |
author_facet |
Mostafa Abdo Salah Kamel Mohamed Ebeed Juan Yu Francisco Jurado |
author_sort |
Mostafa Abdo |
title |
Solving Non-Smooth Optimal Power Flow Problems Using a Developed Grey Wolf Optimizer |
title_short |
Solving Non-Smooth Optimal Power Flow Problems Using a Developed Grey Wolf Optimizer |
title_full |
Solving Non-Smooth Optimal Power Flow Problems Using a Developed Grey Wolf Optimizer |
title_fullStr |
Solving Non-Smooth Optimal Power Flow Problems Using a Developed Grey Wolf Optimizer |
title_full_unstemmed |
Solving Non-Smooth Optimal Power Flow Problems Using a Developed Grey Wolf Optimizer |
title_sort |
solving non-smooth optimal power flow problems using a developed grey wolf optimizer |
publisher |
MDPI AG |
series |
Energies |
issn |
1996-1073 |
publishDate |
2018-06-01 |
description |
The optimal power flow (OPF) problem is a non-linear and non-smooth optimization problem. OPF problem is a complicated optimization problem, especially when considering the system constraints. This paper proposes a new enhanced version for the grey wolf optimization technique called Developed Grey Wolf Optimizer (DGWO) to solve the optimal power flow (OPF) problem by an efficient way. Although the GWO is an efficient technique, it may be prone to stagnate at local optima for some cases due to the insufficient diversity of wolves, hence the DGWO algorithm is proposed for improving the search capabilities of this optimizer. The DGWO is based on enhancing the exploration process by applying a random mutation to increase the diversity of population, while an exploitation process is enhanced by updating the position of populations in spiral path around the best solution. An adaptive operator is employed in DGWO to find a balance between the exploration and exploitation phases during the iterative process. The considered objective functions are quadratic fuel cost minimization, piecewise quadratic cost minimization, and quadratic fuel cost minimization considering the valve point effect. The DGWO is validated using the standard IEEE 30-bus test system. The obtained results showed the effectiveness and superiority of DGWO for solving the OPF problem compared with the other well-known meta-heuristic techniques. |
topic |
power system optimization optimal power flow developed grew wolf optimizer |
url |
http://www.mdpi.com/1996-1073/11/7/1692 |
work_keys_str_mv |
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