Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Probabilistic load flow for radial distribution networks with photovoltaic generators

Probabilistic load flow for radial distribution networks with photovoltaic generators

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Renewable Power Generation — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

In this study, analytical techniques and the Monte Carlo method were both applied to solve a probabilistic load flow in radial distribution networks with photovoltaic-distributed generation, but considering the technical constraints that apply to the networks (e.g. voltage regulation). The analytical technique used in this study combined the method of cumulants with the Gram-Charlier expansion to resolve probabilistic load flow. This was performed by modelling the loads and the photovoltaic (PV) distributed generation as random variables. For this purpose, the authors developed a new probabilistic model that took into account the random nature of solar irradiance and load. The results obtained demonstrate that this new analytical technique can be applied to keep voltages within standard limits at all load nodes of radial distribution networks with photovoltaic-distributed generation. A computational cost reduction has demonstrated that the analytical technique used in this study performed better than the Monte Carlo method. Acceptable solutions were reached with a smaller number of iterations. Convergence was thus rapidly attained with a lower computational cost than that needed with the Monte Carlo method.

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rpg.2010.0180
Loading

Related content

content/journals/10.1049/iet-rpg.2010.0180
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address