الفهرس 
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Abstract
The problem of LFC design is considered as a very imperative issue
facing the researchers in the power system operation. This problem is
represented in the selection of the suitable controller. Thus, the proper tuning of
the controller parameters by an efficient method is required for getting the better
performance of the controller. Furthermore, the simulation of power system
with co-operative strategy is demanded to reduce the computation time and the
accumulation operation which may be obstacles the performance of the system
in real time operation. This thesis applied the MPC, a new smart supervisor
controller, MPC with SMES and MPC with PHEV to solve the LFC problem in
a linear and nonlinear interconnected two, three area power system.
Furthermore, the proposed controllers were applied on a smart grid including
nonlinear interconnected two, three area with RES penetration and load
fluctuations. The parameters of the candidate controllers were selected by the
using of the ICA, GSA and BIA as new AI techniques based on different
objective functions. The performances of the candidate controllers were
compared with the performance of the conventional PI controller. The proposed
systems were simulated by parallel processing and multi-parallel agents’
strategies.
Chapter7 CONCLUSIONS
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7.2 Contributions
The contributions of this thesis are summarized in the following:
1- The application of MPC, a new smart supervisor controller, MPC with
SMES and MPC with PHEV on a linear and nonlinear interconnected
two, three area power system and smart grid with RES penetration and
load fluctuations.
2- The optimal tuning of MPC, MPC with SMES and MPC with PHEV
parameters by the using of the ICA, GSA and BIA.
3- The consideration of interconnected two, three area power system
nonlinearities which represented in GRCs, GDBs, and the communication
time delay and boiler dynamics.
4- The comparison between the ICA, GSA, and BIA in the optimal selection
of MPC, MPC with SMES, and PHEV parameters based on the applying
of different objective functions and select the best one.
5- The investigation of the performance of each applied AI technique with
the increasing of the system complication and the changing of the
objective function. Thus, it is proven that the performance of an AI
technique may be the best in a simple system but not the best in a
complicated system. Furthermore, it has been shown that the performance
AI technique may be the best with an objective function but not the best
with another objective function or not valid with an objective function.
6- The testing of the best controller under the change of load disturbance,
time delay and the parameters uncertainties.
7- The simulation of the proposed system by parallel processing and multiparallel
agents’ strategies for reducing the computation time and the
accumulation operation.
8- Application of a new smart supervisor controller to combine the
advantages of two different controllers.