In recent years, population and government concerns about environmental protection
have increased. At the same time, the use of fossil fuels to the production of electricity is still
high, due to the high availability of this kind of energy and the consolidated technology of the
thermal plants. In that context, it has become increasingly common to adopt methodologies
to optimize the operation of thermoelectric plants, not only in terms of fuel costs, but also the
emissions of pollutants generated, with a positive impact on the reduction of environmental
pollution for electric power generation by thermal systems based on fossil fuels. This process
establishes the need for research in the field of energy planning, usually based on the
application of optimization methods that consider the two objectives mentioned in an
integrated way. Optimization tools based on metaheuristic characteristics, such as simulated
annealing, are well suited to these types of problems. In this context, the aim of this PhD thesis
was to apply multiobjective optimization in the environmental economic dispatch (DEA) of
thermal plants using simulated annealing, comparing the results obtained with other
metaheuristic techniques. This tool was used with an aptitude function that involves two
objectives (cost and emissions), to find the optimal result, taking into account the shutdown
of less efficient engines, thus ensuring the reduction of financial costs and pollution.
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