Resumo:
The regulatory reform of the electric power industry creates an entirely new
competitive environment. In this new electricity market, the reliability of services plays
a very important role to establish non-deterministic criteria, to be applied to both
operation and expansion planning of electric power systems. The utilization of these
criteria, however, is being slowly incorporated into the decision-making processes of
most utilities. Due to the difficulties of interpreting numerical risk indices, system
operators and planners are still averse to the use of probabilistic techniques, being
more confident with the traditional deterministic criteria.
The well-being analysis has been recently proposed as a new technique to measure
the degree of adequacy of power systems, which incorporates deterministic criteria in
a probabilistic framework. The combination of the basic deterministic and probabilistic
concepts is established through the classification of the system operating states into
three categories: healthy, marginal and at risk. In order to identify these states, the
system is analyzed according to a deterministic criterion based on a pre-specified list
of equipment contingencies.
In this dissertation, a new methodology is proposed to evaluate well-being indices
considering composite generation and transmission power systems. The new
methodology uses a non-sequential Monte Carlo simulation, a non-aggregate
Markovian load model and a new process to estimate failure frequency indices,
named one step forward state transition process. New test functions are proposed to
calculate the well-being indices, including the frequency of marginal states. In order
to test the accuracy and efficiency of the proposed methodology, the IEEE Reliability
Test System with some modifications is used.
