Abstract: In Particle Swarm Optimization (PSO) algorithm, although taking an
active role to guide particles moving toward optimal solution, the most-fit
candidate does not have a guide itself and only moves along its velocity vector
in every iteration. This may yield a noticeable number of agents converge into
local optima if the guide (i.e. the most fit candidate) agent cannot explore
the best solution. In this paper, an attempt is made to get the advantage of
the Ant Colony Optimization (ACO) methodology to assist the PSO algorithm for
choosing a proper guide for each particle. This will strengthen the PSO
abilities for not getting involved in local optima. As a result, we present a
promising new hybrid particle swarm optimization algorithm, called ACPSO. The
capability of the presented algorithm to solve a nonlinear optimization problem
is demonstrated using different case studies carried out for optimal reactive
power procurement. The IEEE 14-bus and 118-bus systems are adopted for reactive
power market simulation. The main objective of the market is to minimize total
generation costs of reactive power and transmission losses at different voltage
stability margins. Based on the GAMS modeling language and the CONOPT solver,
solutions are obtained for different models using a conventional non-linear
optimization technique. Compared with the solutions found by the GAMS, Genetic
Algorithm (GA) and the original PSO, the proposed ACPSO algorithm can provide
promising results in terms of robustness and overall efficiency when it is
applied to the reactive power market.
Keywords: Particle swarm optimization, ant colony optimization, deregulation, OPF, reactive power market, system security
