Probabilistic power flow for distribution networks with photovoltaic generators

Author

Zhouyang Ren ; Wei Yan ; Xia Zhao ; Yiming Li ; Yu Juan

Author_Institution

State Key Lab. of Power Transm. Equip. & Syst. Security & New Technol., Chongqing Univ., Chongqing, China

fYear

2013

fDate

21-25 July 2013

Firstpage

1

Lastpage

5

Abstract

Based on Monte Carlo technique, this paper develops a probabilistic power flow (PPF) algorithm to evaluate the influence of photovoltaic (PV) generation uncertainty on distribution networks. Not only the randomness, but also the correlation of PV power and the moments when PV generators start and stop producing power in a day are taken into account with the presented method using the theory of conditional probability and nonparametric kernel density estimation. The measured power data of photovoltaic generator in Oregon State, USA and 34 node distribution test network are used to demonstrate the application of the presented method in PPF analysis.

Keywords

Monte Carlo methods; distributed power generation; load flow; photovoltaic power systems; probability; Monte Carlo technique; PPF algorithm; PV generation uncertainty; conditional probability theory; distribution networks; node distribution test network; nonparametric kernel density estimation; photovoltaic generators; probabilistic power flow algorithm; Distributed power generation; Estimation; Generators; Photovoltaic systems; Power measurement; Monte Carlo; correlation; photovoltaic generation; probabilistic power flow; random;

fLanguage

English

Publisher

ieee

Conference_Titel

Power and Energy Society General Meeting (PES), 2013 IEEE

Conference_Location

Vancouver, BC

ISSN

1944-9925

Type

conf

DOI

10.1109/PESMG.2013.6672803

Filename

6672803