In this paper stochastic collocation method is proposed to solve probabilistic power flow (PPF) model of South Australia (SA). And this model is based upon historical acquisition of power system data of SA. In SA, numbers of wind farms are installed and the variability of wind speed brings more uncertainties into the power system. The traditional deterministic power flow (DPF) computation does not take into account the probabilistic nature of power system uncertainties, so PPF computation is imperative. However, as a commonly used PPF simulation method, Monte Carlo simulation (MCS) has a very high computation cost. Hence, in this paper sparse grid interpolation (SGI) is presented to accomplish PPF analysis with striking high computation efficiency. Meanwhile, instead of using theoretical wind power generation model, probabilistic collocation method (PCM) is used to construct realistic relationship between wind speed and wind power. In addition, fuzzy logic optimization is applied to PCM to improve the accuracy of the model output. The paper concludes with presentation of an aggregated DC power flow model of SA to compare the computation efficiency of the SGI and MC.
|Seiten (von - bis)||160-170|
|Fachzeitschrift||International Journal of Electrical Power & Energy Systems|
|Publikationsstatus||Veröffentlicht - Jän 2018|