A NOVEL DIFFERENTIAL EVOLUTION
ALGORITHMIC APPROACH TO
TRANSMISSION EXPANSION PLANNING
A thesis submitted for the degree of Doctor of Philosophy
Department of Electronic and Computer Engineering, Brunel University
Nowadays modern electric power systems consist of large-scale and highly complex
interconnected transmission systems, thus transmission expansion planning (TEP) is
now a significant power system optimisation problem. The TEP problem is a large-
scale, complex and nonlinear combinatorial problem of mixed integer nature where the
number of candidate solutions to be evaluated increases exponentially with system size.
The accurate solution of the TEP problem is essential in order to plan power systems in
both an economic and efficient manner. Therefore, applied optimisation methods
should be sufficiently efficient when solving such problems. In recent years a number
of computational techniques have been proposed to solve this efficiency issue. Such
methods include algorithms inspired by observations of natural phenomena for solving
complex combinatorial optimisation problems. These algorithms have been
successfully applied to a wide variety of electrical power system optimisation
problems. In recent years differential evolution algorithm (DEA) procedures have been
attracting significant attention from the researchers as such procedures have been
found to be extremely effective in solving power system optimisation problems.
The aim of this research is to develop and apply a novel DEA procedure
directly to a DC power flow based model in order to efficiently solve the TEP problem.
In this thesis, the TEP problem has been investigated in both static and dynamic form.
In addition, two cases of the static TEP problem, with and without generation resizing,
have also been investigated. The proposed method has achieved solutions with good
accuracy, stable convergence characteristics, simple implementation and satisfactory
computation time. The analyses have been performed within the mathematical
programming environment of MATLAB using both DEA and conventional genetic
algorithm (CGA) procedures and a detailed comparison has also been presented.
Finally, the sensitivity of DEA control parameters has also been investigated.
I would like to express my deep gratitude to Dr. Gareth A. Taylor for initiating this
exciting research topic and for his invaluable guidance and encouragement throughout
the duration of my research. I am also grateful to Professor Malcolm R. Irving and
Professor Yong H. Song for their valuable comments and suggestions throughout my
My deep appreciation is to Dr. Thanawat Nakawiro, Dr. Pathomthat Chiradeja
and Dr. Namkhun Srisanit for their valuable discussions, suggestions and great help
during my PhD study. I would like to thank Dr. Jeremy Daniel for his technical support
on computer hardware and software during my research. I am also thankful to the
Brunel Institute of Power Systems at Brunel University for providing me the
opportunity and resources to carry out this research work.
I gratefully acknowledge financial support from the Royal Thai Government
and Srinakharinwirot University.
Finally, I would like to thank my family, especially my mother, my father and
my lovely wife, for their love, support, patience and understanding.
The work described in this thesis has not been previously submitted for a degree in this
or any other university and unless otherwise referenced it is the author‟s own work.
STATEMENT OF COPYRIGHT
The copyright of this thesis rests with the author.
No parts from it should be published without his prior written consent,
and information derived from it should be acknowledged.
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