الفهرس 
OPTIMAL POWER FLOW PRINCIPLES AND APPLICATIONS
OPTIMAL POWER FLOW PRINCIPLES AND APPLICATIONSOnly 14 pages are availabe for public view
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Abstract
he penetration level of Renewable Energy Resources
(RESs) in the electrical power system is increasing as they
provide a cleaner and a cheaper alternative as compared to
conventional electricity generators. The main challenge to the
spread of these RESs is that they are not dispatchable due to
their intermittent nature. Hence, their coincidence with demand
is not guaranteed, and this affects system reliability.
The main aim of this research is to assess the effectiveness
of utilizing new metaheuristic optimization algorithms; namely
the SunFlower Optimization (SFO) algorithm, the Hybrid
Firefly and Particle Swarm Optimization (HFPSO) technique,
and the Harris Hawks Optimization (HHO) in solving a
constrained Optimal Power Flow (OPF) problem. The principle
target is to minimize the generating units’ fuel cost under the
power system’ practical constraints. At initial stage, the
objective function is solved to find the optimal locations of
photovoltaic (PV) generators and/or wind generators within the
system under study. Then, different scenarios are performed to
solve the OPF problem including and excluding renewable
energy sources. The generators’ real output power defines the
exploration field for the OPF problem. The SFO, the HFPSO,
and the HHO algorithms are applied, one at a time, to minimize
the fitness function and yield the best solutions of the problem.
The suggested techniques are applied to four standard test
systems to check the validity of the proposed algorithms. These
test systems are the IEEE 14-bus, 30-bus, 57-bus, and 118-bus
networks respectively. Simulations, with different scenarios,
are implemented on these networks. To obtain a realistic result,
real daily load curves are considered in this study. The results
of simulations are investigated and analyzed. Results confirm
the feasibility, effectiveness, and superiority of the introduced
SFO, HFPSO, and HHO -based OPF methodologies, especially
when compared with the genetic algorithm and particle swarm
optimization.