Probabilistic Power Flow Method Considering Continuous and Discrete Variables

This paper proposes a probabilistic power flow (PPF) method considering continuous and discrete variables (continuous and discrete power flow, CDPF) for power systems. The proposed method—based on the cumulant method (CM) and multiple deterministic power flow (MDPF) calculations—can deal with contin...

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Bibliographic Details
Main Authors: Xuexia Zhang, Zhiqi Guo, Weirong Chen
Format: Article
Language:English
Published: MDPI AG 2017-04-01
Series:Energies
Subjects:
Online Access:http://www.mdpi.com/1996-1073/10/5/590
Description
Summary:This paper proposes a probabilistic power flow (PPF) method considering continuous and discrete variables (continuous and discrete power flow, CDPF) for power systems. The proposed method—based on the cumulant method (CM) and multiple deterministic power flow (MDPF) calculations—can deal with continuous variables such as wind power generation (WPG) and loads, and discrete variables such as fuel cell generation (FCG). In this paper, continuous variables follow a normal distribution (loads) or a non-normal distribution (WPG), and discrete variables follow a binomial distribution (FCG). Through testing on IEEE 14-bus and IEEE 118-bus power systems, the proposed method (CDPF) has better accuracy compared with the CM, and higher efficiency compared with the Monte Carlo simulation method (MCSM).
ISSN:1996-1073