Abstract

The irregular and truncated statistical characteristics of wind power uncertainty lead to unknown influences on the power system operation. In this paper, we propose a new probabilistic optimal power flow (POPF) framework, which can cope with such characteristics while taking into account the correlation among the wind power in different wind farms. A truncated multivariate Gaussian mixture model (Trun-MultiGMM) is designed to describe the actual wind power distributions with truncation feature. We then develop an efficient Markov Chain quasi-Monte Carlo (MCQMC) sampler to deliver wind power samples from the customized Trun-MultiGMM. Numerical simulations are conducted on the real wind generation datasets and the modified IEEE 118-bus benchmark system, whose results verify the effectiveness and efficiency of the proposed POPF framework.