14-08-2012, 02:57 PM
Application of the ant colony search algorithm to reactive power pricing in an open electricity market
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Abstract
Reactive power management is essential to transfer real energy and support power system security.
Developing an accurate and feasible method for reactive power pricing is important in the electricity
market. In conventional optimal power flow models the production cost of reactive power was ignored.
In this paper, the production cost of reactive power and investment cost of capacitor banks were included
into the objective function of the OPF problem. Then, using ant colony search algorithm, the optimal
problem was solved. Marginal price theory was used for calculation of the cost of active and reactive
power at each bus in competitive electric markets. Application of the proposed method on IEEE 14-bus
system confirms its validity and effectiveness. Results from several case studies show clearly the effects
of various factors on reactive power price.
Introduction
The traditional regulated and monopoly structure of power
industry throughout the world is eroding into an open-access
and competitive environment. Thus, planning and operation of
the utilities are based on the economic principles of open-access
markets. In this new environment electric markets are essentially
competitive. Until now, effort has been directed primarily toward
developing methodologies to determine remuneration for the active
power of the generators. Although the investment in electric
power generation and the fuel cost, represent the most important
costs of power system operation, reactive power is becoming more
and more important, especially from the security point of view and
the economic effect caused by it [1].
Ant colony algorithm
Ant Colony Optimization method handles successfully various
combinatorial complex problems. Dorigo has proposed the first
ACO method in his PhD thesis [19]. ACO algorithms are developed
based on the observation of foraging behavior of real ants. Although
they are almost blind animals with very simple individual capacities,
they can find the shortest route between their nest(s) and a
source of food without using visual clues. They are also capable of
adapting to changes in the environment; for example, finding a
new shortest path once the old one is no longer feasible due to a
new obstacle. The studies by ethnologists reveal that such capabilities
are essentially due to what is called ‘‘pheromone trails”, which
ants use to communicate information among individuals regarding
path and to decide where to go. During their trips a chemical trail
(pheromone) is left on the ground. The pheromone guides other
ants towards the target point. Furthermore, the pheromone evaporates
over time (i.e. it loses quantity if other ants lay down no more
pheromone). If many ants choose a certain path and lay down pheromones,
the quantity of the trail increases and thus this trail attracts
more and more ants [20]. Each ant probabilistically prefers
to follow a direction rich in pheromone rather than a poorer one.
Conclusions
In the study of reactive power marginal price in this paper, both
active and reactive power production costs of generators and capital
cost of capacitors are considered in the objective function of
OPF problem. A new method based on ant colony algorithms and
advanced sequential quadratic programming is employed to solve
the OPF problem. The IEEE 14-bus system is used to verify the
validity of the methodology, considering three objective functions.
Test results may show that the reactive power production cost and
the capital investment of capacitors should be considered in reactive
power spot pricing for their noticeable impacts on reactive
power marginal price.
Results confirm that reactive power cost allocation based on
opportunity cost method may lead to wrong signals for market
participants. However, triangle reactive pricing method seems to
be accurate and fair when compared with opportunity cost method,
and hence, is more compatible with non-discriminatory philosophy
of open-access deregulated systems.