Uncertainty Quantification of Power Flow in Distributed System Considering the Random and Fuzzy Characteristics

Accurately quantifying the uncertainty brought by distributed generation (DG) is very important for the safe and stable operation of power grid. Uncertainty is mainly divided into randomness and fuzziness. Traditional model of DG only considers its randomness, neglecting the influence of fuzzy parameters and fuzzy correlation, which sometimes leads to analysis results deviating from objective facts. To deal with this problem, an uncertainty quantification method considering both random and fuzzy characteristics of DG is proposed in this paper. Firstly, a DG model that considers the random and fuzzy parameters, as well as fuzzy correlation is established. Then, based on Nataf transform and point estimation method, probabilistic load flow is carried out in different membership degrees. And global sensitivity analysis based on membership function is applied for discussing the contribution to the output response of each fuzzy parameter. Finally, modified IEEE 33-bus and IEEE 118-bus systems are used for simulation. The results show that DG model considering both randomness and fuzziness can more scientifically depict the impact of uncertainty.