Resumo:
The well-being analysis was recently proposed as a new framework to measure the
degree of adequacy of power systems, which has as the main objective the
incorporation of deterministic criteria into the reliability analysis process. The
conceptual basis for this framework is obtained through the classification of the
system states into three groups: healthy, marginal and at risk. For the identification of
these operation states, the system is submitted to a deterministic criterion.
In composite generation and transmission systems, the identification of a healthy or
marginal state becomes much more complex than that one used, for example, in
generation systems. Any deterministic criterion to be used must consider a list of
contingencies. In principle, for each considered operation state, it is necessary to
carry out a number of additional performance analyses equal to the number of
elements in the list. Moreover, these adequacy analyses involve load flow runs with
corrective measure optimizations. Therefore, the major difficulty found in assessing
well-being indices consists of conciliating the deterministic criterion and the
combinatorial nature of the problem.
In this thesis, the evaluation of well-being indices for bulk composite generation and
transmission power systems is focused. For this purpose, the following techniques
are considered: non-sequential Monte Carlo simulation with a new test function;
interior point method for optimal power flow with reduced constraints; probabilistic
equivalent network; and incorporation of artificial neural nets in the classification of
the operation states. These techniques can provide considerable reductions in the
computational cost demanded during the classification of states. In order to verify the
proposed concepts and models, the developed methodology is applied to several test
systems, including a configuration of the Brazilian power network.
