Soft clustering based probabilistic power flow with correlated inter temporal events

Abstract

There is a growing demand to deploy renewable energy sources (RES) and plug-in electric vehicle (PEV) charging station (EVCS) in distribution networks. This, however, will change power grid landscapes and potentially impose uncertainties in short and long term grid planning. The solution to quantify randomness of intermittent events is to implement probabilistic power flow (PPF) analysis. The computation of PPF due to myriad sources of unknown uncertainties is very expensive. This computation analysis becomes more complex when inter-period temporal-spatial correlation and multivariate uncertainties are considered. This paper addresses the issue by resorting to a novel hybrid nonparametric algorithm for short-term PPF analysis in radial distribution networks. The proposed algorithm deals with probabilistic nonlinear loads, unit generators including PV panels, Wind farms, and EVCS charging pattern. Temporal correlation characterized by memory coefficient is used to model inter-event temporal correlation (IETC) over multiple time steps. Detailed uncertainty modeling of PEVs driving and their queues at EVCSs are included. The proposed method is superior over other existing numerical methods as it accounts for the multivariate inter temporal uncertainty events of RES and PEVs charging. Simulation results in a spatio-temporally correlated IEEE 34 node radial distribution system revealed that using IETC coefficients leads to improved prediction accuracy with average error less than 2.5% and improves the computation burden. The proposed method is applied to IEEE 123 node to test the performance of our proposed method. A comparative analysis is done with other well-known analytical/numerical methods. The PPF Computation time of our proposed method is shown to be faster than other methods when IETC is implemented. The results show insensitivity of our proposed method to the size of the system.