期刊
IET GENERATION TRANSMISSION & DISTRIBUTION
卷 13, 期 2, 页码 181-188出版社
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-gtd.2018.5832
关键词
pattern clustering; genetic algorithms; stochastic processes; probability; evolutionary computation; Monte Carlo methods; load flow; data clustering; probabilistic optimal power flow; probabilistic OPF; Monte Carlo simulation method; evolutionary based optimisation problems; MCS method; alternative method; probabilistic assessment; Cholesky decomposition method; 118-bus standard test systems; two-point estimate method
Ever increasing use of renewable energies beside other uncertain parameters in power systems makes it necessary to evaluate power system issues, probabilistically. One of the important studies in power systems operating is optimal power flow (OPF), which should be considered as probabilistic OPF (POPF). However, the Monte Carlo simulation (MCS) method can be efficiently used for handling any type of uncertainty but this method suffers from large calculation requirements and cannot be implemented in various studies. Especially, in evolutionary based optimization problems the use of MCS method is completely restricted. Data clustering is an alternative method which keeps the accuracy of the MCS method and requires very less calculation burden. In this study, data clustering is used for probabilistic assessment of power system in POPF problem. The proposed method is very fast, accurate and can easily handle any type of correlation between stochastic input variables. The correlated input variables are generated by the Cholesky decomposition method. The Genetic Algorithm (GA) is used as optimization tool. In order to demonstrate and validate the performance of the proposed method, IEEE 30- and 118-bus standard test systems are studied and the results are compared with a modified version of the two-point estimate method.
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