Probabilistic direct load flow algorithm for unbalanced distribution networks considering uncertainties of PV and load. Issue 11 (13th June 2019)

• Record Type: Journal Article
• Authors: Fu, Yang; Liu, Huanhuan; Su, Xiangjing; Mi, Yang; Tian, Shuxin
• Is Part Of: IET renewable power generation. Volume 13:Issue 11(2019)
• Page Start: 1968
• Page End: 1980
• Publication Date: 2019-06-13
• Subjects: Monte Carlo methods -- probability -- distributed power generation -- distribution networks -- load flow -- power distribution planning; probabilistic direct load flow algorithm -- unbalanced distribution networks -- distributed generations -- PVs -- distribution network planning -- great uncertainty -- intermittence -- probabilistic load flow approaches -- handle uncertainties -- novel PLF algorithm -- network loss -- advanced direct load flow approach -- unbalanced distribution PLF problems -- Australian unbalanced distribution network; Renewable energy sources -- Periodicals; 333.79405
• DOI: 10.1049/iet-rpg.2018.5802
• Languages: English
• ISSNs: 1752-1416

Abstract

Abstract : The increasing penetration of distributed generations such as rooftop photovoltaics (PVs) brings about great challenges to distribution network planning and operation, due to the great uncertainty and intermittence. Although many probabilistic load flow (PLF) approaches have been proposed to handle uncertainties in power systems, PLFs for unbalanced distribution networks with superior computational performance are still under demand. Therefore, this study presents a novel PLF algorithm for unbalanced distribution networks considering the uncertainties introduced by PVs and loads. Taking PVs and loads as the random inputs while three‐phase voltages and network loss as the random outputs, the proposed PLF integrates the cumulant method and the Gram–Charlier expansion with the advanced direct load flow (DLF) approach to effectively address the unbalanced distribution PLF problems. Finally, the effectiveness and superior performance in terms of efficiency, accuracy, and robustness of the proposed PLF algorithm are verified by detailed simulations and compared with the Monte–Carlo Simulation method on a real Australian unbalanced distribution network.