Resumo:
Nowadays, distribution networks operate under a new re-regulated market
structure. In this new context, utilities should reduce their costs, which generally
occur through measures that may significantly decrease the system reliability.
Aware of this problem, the regulatory agencies have created targets for the
reliability indices. If any of these standards are violated, penalties are applied to
utilities. In order to reduce the amount of penalties, the system must become
less susceptible to failures. Thus, reliability evaluations of distribution systems
represent important studies nowadays, since they can reveal the weak parts of
the systems, or, in other words, those areas that should be reinforced. One of
the most relevant techniques used for this proposal is the sequential Monte
Carlo simulation (MCS). This method can represent chronological transitions
among all possible system operational states and, consequently, provide
density probability functions of the performance indices.
The methodology to analyze the distribution system behavior should use an
amount of reliable database, which represents the reliability aspects of the
system components. Therefore, data calibration procedures have to be
implemented with MCS technique in order to ensure predicted results with
higher degree of credibility.
The systems can be better evaluated for their operational condition if a
mechanism known as “performance based rates”, PBR, is used. The PBR
mechanism rewards the utilities that provide good reliability, and penalizes the
ones with poor reliability behavior. PBR contracts introduce attractive
investment scenarios to the utilities by offering benefits for those robust
systems.
The objectives of this research is to present an efficient sequential MCS,
propose a new methodology for data calibration based on the probability
distribution functions of the indices, and finally discuss two different PBR
mechanisms. All proposed methodologies will be applied to a test system, know
as IEEE – RBTS, and to a real system.
