4.4 Article

Three-phase probabilistic load flow in radial and meshed distribution networks

Journal

IET GENERATION TRANSMISSION & DISTRIBUTION
Volume 9, Issue 16, Pages 2743-2750

Publisher

INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-gtd.2015.0521

Keywords

load flow; demand side management; probability; wind turbines; higher order statistics; Monte Carlo methods; distributed power generation; three phase probabilistic load flow; meshed distribution network; radial distribution network; deterministic power system tool; demand response program; fundamental load flow analysis; discrete probability density function; nodal voltage; power flows; IEEE 13 node test feeder; radial configuration; modified mesh configuration; wind turbine; cumulants; Monte Carlo simulation; PLF method; stochastic problems; power distribution system

Funding

  1. Science Foundation Ireland [09/SRC/E1780]
  2. U.S. Department of Energy [DE-AC0500OR22725]

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With the introduction of higher levels of renewables and demand response programs, traditional deterministic power system tools fall short of expectations. Probabilistic load flow (PLF) takes into account the inconsistency or the unknown loads, and generation in the fundamental load flow analysis. This study proposes a PLF solution for both balanced and unbalanced, radial and weakly meshed networks without explicitly using the Y-bus matrix. It allows for discrete probability density functions as input variables without having to assume a predefined distribution. The nodal voltages and the power flows can be calculated independently from one another. The proposed method is applied to the IEEE 123 Node Test Feeder and the IEEE 13 Node Test Feeder in both its original radial configuration and a modified mesh configuration, including a load replaced with a wind turbine. The results are validated by comparison of the proposed method's solutions to those obtained using cumulants and Monte Carlo simulation. The proposed PLF method provides an accurate and practical way for finding the solution to stochastic problems occurring in power distribution systems allowing for real-system data to be analysed.

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