The increased participation of variable renewable energy sources (VRES) in
Brazil s electricity matrix brings several challenges to the planning and operation
of the Brazilian Power System (BPS), due to the VRES stochasticity. Such
challenges involve the modeling and simulation of intermittent generation
processes and, in this context, a considerable amount of research has been
directed to the theme. In this context, a topic of increasing importance in the
literature is related to the development of methodologies for joint stochastic
simulation of intermittent resources with complementary characteristics, such as
wind and solar sources. Aiming to contribute to this theme, this work proposes
improvements in a simulation model already established in the literature,
evaluating its applicability based on Brazilian Northeast data. The proposed
methodology is based on the discretization of energy time series applying the kmeans machine learning technique, construction of state transition matrices
based on the identified clusters, and Monte Carlo simulation to obtain the
scenarios. The synthetic series obtained are compared to the results generated
by the model already established in the literature from statistical techniques.
Regarding the scope of the research objectives, the proposed modeling
demonstrated more promising results, generating scenarios that satisfactorily
reproduced all the evaluated characteristics of the historical data.
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