Next Article in Journal
Experimental Study of a Centralized Control Strategy of a DC Microgrid Working in Grid Connected Mode
Previous Article in Journal
Numerical Study of the Gas-Liquid Two-Phase Flow in a Self-Designed Mixer for a Ga-R113 MHD System
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
Energies 2017, 10(10), 1623; https://doi.org/10.3390/en10101623

A Novel Probabilistic Optimal Power Flow Method to Handle Large Fluctuations of Stochastic Variables

School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
*
Author to whom correspondence should be addressed.
Received: 13 September 2017 / Revised: 2 October 2017 / Accepted: 13 October 2017 / Published: 17 October 2017
(This article belongs to the Section Electrical Power and Energy System)
Full-Text   |   PDF [2809 KB, uploaded 17 October 2017]   |  

Abstract

The traditional cumulant method (CM) for probabilistic optimal power flow (P-OPF) needs to perform linearization on the Karush–Kuhn–Tucker (KKT) first-order conditions, therefore requiring input variables (wind power or loads) varying within small ranges. To handle large fluctuations resulting from large-scale wind power and loads, a novel P-OPF method is proposed, where the correlations among input variables are also taken into account. Firstly, the inverse Nataf transformation and Cholesky decomposition are used to obtain samples of wind speeds and loads with a given correlation matrix. Then, the K-means algorithm is introduced to group the samples of wind power outputs and loads into a number of clusters, so that in each cluster samples of stochastic variables have small variances. In each cluster, the CM for P-OPF is conducted to obtain the cumulants of system variables. According to these cumulants, the moments of system variables corresponding to each cluster are computed. The moments of system variables for the total samples are obtained by combining the moments for all grouped clusters through the total probability formula. Then, the moments for the total samples are used to calculate the corresponding cumulants. Finally, Cornish–Fisher expansion is introduced to obtain the probability density functions (PDFs) of system variables. IEEE 9-bus and 118-bus test systems are modified to examine the proposed method. Study results show that the proposed method can produce more accurate results than traditional CM for P-OPF and is more efficient than Monte Carlo simulation (MCS). View Full-Text
Keywords: cumulant method (CM); probabilistic optimal power flow (P-OPF); large fluctuations; K-means algorithm cumulant method (CM); probabilistic optimal power flow (P-OPF); large fluctuations; K-means algorithm
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Deng, X.; He, J.; Zhang, P. A Novel Probabilistic Optimal Power Flow Method to Handle Large Fluctuations of Stochastic Variables. Energies 2017, 10, 1623.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top