Optimal Power Flow (OPF) has been widely used for both the operation and planning
of a power system. Therefore, a typical OPF solution adjusts the appropriate control
variables so that a specific objective in operating a power system network is optimized
(maximizing or minimizing) with respect to the power system constraints dictated
by the electrical network.
The OPF has had a long history in its development. It was first discussed by Carpentier
in 1962, and took a long time to become a successful algorithm that could be applied
in everyday use. Current interest in the OPF centers around its ability to provide
the optimal solution that takes into account the security of the system.
In order to solve the OPF problem, there are various classical methods such as Non-Linear
Programming (NLP), Linear Programming (LP), Quadratic Programming (QP), Newton-based
techniques and interior point methods (Aguado and Quintana, 1999). But these methods
suffer from certain drawbacks, such as insecure convergence, algorithm complexity
and weak handling of qualitative constraints. Thus it becomes essential to develop
optimization techniques that are efficient to overcome these drawbacks and handle
such difficulties. PSO is one of the best strategies for solving such problems because
of its inherent fast search and convergence capability (Abido, 2002).
For optimization, Particle Swarm Optimization (PSO) is used in this paper. PSO is
a relatively new evolutionary algorithm that may be used to find optimal (or near
optimal) solutions to numerical and qualitative problems.
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