Mauricio, Granada E. [UNESP]Rider, Marcos J. [UNESP]Mantovani, J. R S [UNESP]2014-05-272014-05-272011-05-312010 IEEE/PES Transmission and Distribution Conference and Exposition: Latin America, T and D-LA 2010, p. 139-146.http://hdl.handle.net/11449/72446In this paper a framework based on the decomposition of the first-order optimality conditions is described and applied to solve the Probabilistic Power Flow (PPF) problem in a coordinated but decentralized way in the context of multi-area power systems. The purpose of the decomposition framework is to solve the problem through a process of solving smaller subproblems, associated with each area of the power system, iteratively. This strategy allows the probabilistic analysis of the variables of interest, in a particular area, without explicit knowledge of network data of the other interconnected areas, being only necessary to exchange border information related to the tie-lines between areas. An efficient method for probabilistic analysis, considering uncertainty in n system loads, is applied. The proposal is to use a particular case of the point estimate method, known as Two-Point Estimate Method (TPM), rather than the traditional approach based on Monte Carlo simulation. The main feature of the TPM is that it only requires resolve 2n power flows for to obtain the behavior of any random variable. An iterative coordination algorithm between areas is also presented. This algorithm solves the Multi-Area PPF problem in a decentralized way, ensures the independent operation of each area and integrates the decomposition framework and the TPM appropriately. The IEEE RTS-96 system is used in order to show the operation and effectiveness of the proposed approach and the Monte Carlo simulations are used to validation of the results. © 2011 IEEE.139-146engdecentralized coordinationDecomposition methodsMA-PPFmulti-area power systemsprobabilistic power flowDecentralized coordinationMulti area power systemsProbabilistic power flowAlgorithmsComputer simulationElectric power transmissionKnowledge managementMonte Carlo methodsPower transmissionProbability distributionsRandom variablesThermoelectric powerUncertainty analysisProblem solvingTrabalho apresentado em evento10.1109/TDC-LA.2010.5762874Acesso aberto2-s2.0-799575605080614021283361265