Resumo:
Electric energy utilities around the world have made use of interactive
methodologies, in order to plan the expansion of their systems, based on
deterministic criteria such as the “N-1”. Usually, it can be observed the lack of
more sophisticated methodologies based on optimization models, which can
significantly reduce the number of expansion alternatives to be appreciated by
planners and, consequently, provide the most adequate solutions bearing in mind
the cost-benefit relation. This procedure can become a very interesting planning
option that should be assimilated by utilities of the world electric sector in the
coming years.
In the specific case of the Transmission Expansion Planning (TEP), it is
recognized that this problem has a huge complexity not only due to the
dimension of the actual systems but also to the involved uncertainties, which
include those related with the new rules of electric energy markets. The TEP
problem has been treated at two environments: static and dynamic. In the static
case, the best expansion alternatives are evaluated conditioned to a specific year
of the planning horizon, while in the dynamic case, the whole expansion period is
taken into account. Bearing in mind the complexity of the TEP problem, heuristic
and metaheuristic models have shown promising results. The success of these
models is related to their ability of avoiding local minima and, therefore, exploring
a wide region within the possible range of each problem.
This Dissertation presents a new methodology to solve the TEP problem based on
the metaheuristic known as Ant Colony Optimization (ACO). The main objective is
to obtain the set of best transmission expansion alternatives, in the long term,
using the ACO metaheuristic. All studies are carried out considering a deterministic
framework, at both environments: static and dynamic. The efficiency of the
proposed approach is illustrated through its application to a test system and also to
a real subtransmission network.
