A Clustering-Based Method for Quantifying the Effects of Large On-Grid PV Systems

Analyzing the impacts of large on-grid photovoltaic (PV) systems on the performance of the electric network is an essential task prior to the installation of these systems. To quantify these impacts, a method based on chronological simulations can be used. The main advantage of this method is its ab...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on power delivery 2010-10, Vol.25 (4), p.2617-2625
Main Authors: Omran, Walid A, Kazerani, Mehrdad, Salama, Magdy M A
Format: Article
Language:English
Subjects:
Analytical models
Applied sciences
Chronological simulations
clustering
Costs
Direct energy conversion and energy accumulation
Electrical engineering. Electrical power engineering
Electrical power engineering
Exact sciences and technology
Fluctuations
Fossil fuels
Load flow
Miscellaneous
on-grid PV systems
Performance analysis
Photoelectric conversion
Photovoltaic cells
Photovoltaic systems
power fluctuations
Power generation
Power networks and lines
siting
sizing
Solar energy
Solar power generation
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Analyzing the impacts of large on-grid photovoltaic (PV) systems on the performance of the electric network is an essential task prior to the installation of these systems. To quantify these impacts, a method based on chronological simulations can be used. The main advantage of this method is its ability to provide information about the impacts of the fluctuation of the power generated from the PV systems. However, this method requires performing extensive analysis and simulations, making it impractical for utility studies, especially if long historical data with subhourly time resolution is used. In this paper, a new method that utilizes the data efficiently while preserving the temporal information of the generated PV power is proposed. The method takes advantage of the clustering techniques to group together segments of the output PV power having similar features. Hence, a representative segment for each group can be chosen and used in the analysis and simulations. This representative segment can provide information about the expected performance of other segments of the group. The validity and usefulness of the proposed method are demonstrated by identifying the suitable size and site of a large PV system.
ISSN:0885-8977
1937-4208
DOI:10.1109/TPWRD.2009.2038385