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The complete measure of dependence between input random variables should consider both the degree of dependence and dependence structure. The dependence relationships between different input random variables may be linear dependence or non-linear dependence. For example, wind power is influenced by the factors of wind direction, wind speed, power curve and control strategy of wind turbine. As a matter of fact, the input random variables in power system operation are affected by various factors. Most of these proposed PLF methods only consider the linear dependence between input random variables and adopt Pearson's linear correlation coefficient and covariance matrix to demonstrate the degree of linear dependence between them. Some PLF methods have been proposed in the technical literature to deal with the correlated input random variables, including convolution method, Gaussian mixture model method, unscented transformation, point estimate method, cumulant method and Monte Carlo simulation method (MCSM). In order to evaluate system load flow more accurately and comprehensively, and provide system operators with more valuable information, PLF computation should consider the dependence factor. The dependence factor affects load demands, generators’ power output and operation modes of power system, and further has an important influence on system load flow.Īs one of the most commonly used tools for system load flow analysis, probabilistic load flow (PLF) can effectively assess the performance of a power network over most of its working conditions taking into account the uncertainty of input random variables, such as wind power. Power output of different neighbouring wind farms is strongly correlated. For example, a group of loads existing in the same area will tend to increase and decrease in a similar manner due to the environmental factors and social ones. Plenty of stochastic dependence between power injections exists in power system operation, including linear and non-linear dependence. IET Generation, Transmission & Distribution.IET Electrical Systems in Transportation.IET Cyber-Physical Systems: Theory & Applications.IET Collaborative Intelligent Manufacturing.
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CAAI Transactions on Intelligence Technology.