الفهرس 
IntroductionOnly 14 pages are availabe for public view
 |  | from | 172 |  |  |
| | | from | 172 | | |
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.