Fluxo de carga probabilístico.

Abstract

tion is to describe and apply in large systems, a method that allows loads and generation to be probabilistically modeled. In planning and operating a power system, it is necessary to perform power flow studies, and there are several methods that do it quickly and efficiently. However, these methods are deterministic and for each solution of a load stream, the system configuration, nodal loads and generation are assumed constant. The problem with this deterministic approach method is that, while very precious, they are as reliable as the input data. Some variation in the input data causes changes in the load flow solution, as the deterministic hypothesis is a simplification of the problem. To overcome this difficulty, the problem can be probabilistically modeled. From the knowledge of statistical variations of nodal load and generation, which are modeled as random variables, we obtain the output variables of a load stream, in terms of a probability density function. This dissertation also describes a methodology for the statistical modeling of the active and reactive loads of a power system. Probability density functions, which model the randomness of output variables, provide more information, providing insights for better system planning, system expansion and operation. This method then results in a probabilistic rather than deterministic analysis of the charge flow problem. The method uses system state results obtained from a solution of a deterministic load flow as the point around which the probabilistic load flow equations will be linearized.