Probabilistic Distribution Power Flow Based on Finite Smoothing of Data Samples Considering Plug-in Hybrid Electric Vehicles

TL;DR

This paper introduces a fast, accurate probabilistic power flow algorithm using finite data smoothing, effectively modeling plug-in hybrid electric vehicle charging behavior and residential load overlaps, outperforming Monte Carlo methods.

Contribution

A novel finite sample-based algorithm for probabilistic power flow that improves speed and accuracy in modeling PHEV charging impacts.

Findings

Faster than Monte Carlo simulation

Maintains adequate accuracy

Effective in different test systems

Abstract

The ever increasing penetration of plug-in hybrid electric vehicles in distribution systems has triggered the need for a more accurate and at the same time fast solution to probabilistic distribution power flow problem. In this paper a novel algorithm is introduced based on finite sample points to determine probabilistic density function of probabilistic distribution power flow results. A modified probabilistic charging behavior of plug-in hybrid electric vehicles at charging stations and their overlap with residential peak load is evaluated in probabilistic distribution power flow problem. The proposed algorithm is faster than Monte Carlo Simulation and at the same time keeps adequate accuracy. It is applied to solve probabilistic distribution power flow for two dimensionally different test systems and is compared with recent probabilistic solutions. Simulation results show the accuracy and efficiency of the proposed algorithm to calculate probability density function of uncertain outputs.