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  5. Discrete Elastic Residential Load Response under Variable Pricing Schemes
 
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Discrete Elastic Residential Load Response under Variable Pricing Schemes

Author(s)
McKenna, Killian  
Keane, Andrew  
Uri
http://hdl.handle.net/10197/7432
Date Issued
2014-10-15
Date Available
2016-02-03T17:03:24Z
Abstract
The introduction of variable pricing schemes has potential impacts for low voltage (LV) distribution networks with regards to load diversity and peak demand. Households are being equipped communication systems that give consumers a greater visibility of electricity prices facilitating greater market engagement and demand response. This paper presents a bottom-up load model coupled with a novel methodology to capture the discrete, bounded and uncertain consumer response to variable prices. The model uses Monte Carlo simulation techniques and price elasticity matrices to affect the probability of consumption, taking into account detailed consumer characteristics and appliance operation. The model is used to run a high resolution simulation of residential load response and to quantify behavioral changes using standard load metrics.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
IEEE
Copyright (Published Version)
2014 IEEE
Subjects

Demand response

Price-response

Distribution networks...

Load modelling

Smart grids

Residential load sect...

DOI
10.1109/ISGTEurope.2014.7028769
Language
English
Status of Item
Peer reviewed
Conference Details
2014 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Istanbul, Turkey, 12 - 15 October, 2014
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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McKenna_(2014)_Discrete_Elastic_Residential_Load_Response_under_Variable_Pricing.pdf

Size

532.07 KB

Format

Adobe PDF

Checksum (MD5)

949cdf79c49706b61b1355ca4e3d81d2

Owning collection
Electrical and Electronic Engineering Research Collection
Mapped collections
ERC Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
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