The rapidly increasing capacity of wind and solar power has increased the uncertainty in power systems

and therefore

there has been growing concern in probabilistic load flow. A method based on generalized polynomial chaos (gPC) is proposed for probabilistic load flow that uses the idea of orthogonal polynomial approximation from the gPC to move the system randomness to orthogonal polynomial basis. The load flow equations in rectangular coordinates are adopted to avoid high order truncation error in expanding nonlinear load flow equations. By the stochastic Galerkin method

the probabilistic load flow equations are transformed into a set of deterministic equations. After solving these equations

the orthogonal polynomial approximation coefficients are obtained and can be used to calculate the mean and variance of the load flow state variables. The probability density functions of relevant variables can be further estimated by Monte-Carlo simulation (MCS). Numerical results from IEEE 9 bus system show that the computation error of the proposed method decreases exponentially as the order of orthogonal polynomial approximation increases. It is found that three-order approximation can achieve a good accuracy under normal conditions. Compared with MCS

this method has high computation efficiency. This work is supported by National High Technology Research and Development Program of China (863 Program) (No. 2012AA050204) and National Natural Science Foundation of China (No. 51377143).