الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Load frequency control (LFC) serves a critical purpose in modern
power systems, controlling system frequency and tie-line power flow.
Nowadays, the power systems are integrated with an effective contribution of
renewable energy sources (RESs) which complicates the task of LFC.
This thesis introduces the application of four recent optimization
algorithms for the purpose of optimally design the PID controllers for LFC of
interconnected power system. The LFC problem is formulated by using the
method of integral square error (ISE). Using ISE as an objective function has
a precious merit of reducing overshoots and undershoots which represents
one of the main targets of LFC.
This thesis presents the application of Harris Hawks optimization for
solving the LFC problem of a classical two-area system. The second applied
algorithm is the hybrid particle swarm- grey wolf optimization which is
employed for a classical three-area system. Moreover, Marine predators
optimization algorithm has been employed to design the optimal settings of
the PID controllers for solving the LFC problem in a two-area system
including renewable energy sources and energy storage units. Finally, Manta
ray optimization algorithm is applied with a three-area system including
renewable energy sources.
Also, this study provides a new modification for the Super conducting
magnetic energy storage (SMES) model involving SMES electrical losses.
Moreover, the study addresses a new criterion in the field of LFC studies,
where the responses are compared based on their harmonic spectrum and
total harmonic distortion (THD). The results obtained by the proposed
control strategies are compared with other competing strategies and the
simulation outcomes have proven a high efficiency and robustness of the
proposed control strategies in solving the LFC problem. The feasibility of the
proposed control approaches is validated by the simulation study, which
accomplished by using the MATLAB/Simulink environment.
Keywords: Load frequency control, wind energy, photovoltaic, Marine
predators optimization, Manta ray optimization, Harris hawks optimization,
Particle swarm optimization