A model of correlated interval–probabilistic conditional value-at-risk and optimal dispatch with spatial correlation of multiple wind power generations
Xiaohong Ran, Jiahao Zhang, Wanjun Zhu, Kaipei Liu, Yangsheng Liu
Abstract
The correlated uncertain variables with the probability density function have been conducted in previous studies. However, the probability density functions of random variables are not always available, but their boundaries are more readily obtained. Accordingly, this paper adopts the interval variables with spatial correlation in multiple wind generations to achieve more practical scheduling. First, a novel risk qualification model, that is, correlated interval-probabilistic-based conditional value-at-risk, can be proposed to reduce the conservativeness of the traditional risk assessment methods. Then, an uncertain optimization model is built that considers multiple uncertainties, including probabilistic uncertainties of the load and interval uncertainties of coupled adjacent wind farms. Moreover, two spinning reserves are developed and integrated into the dispatch model. Finally, the influence of the wind power generation's correlation and fluctuation levels on the dispatch results is also investigated. Simulations on IEEE-39 and IEEE-118 bus systems validate the accuracy of the proposed risk assessment method.