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  5. Residential Load Modeling of Price Based Demand Response for Network Impact Studies
 
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Residential Load Modeling of Price Based Demand Response for Network Impact Studies

Author(s)
McKenna, Killian  
Keane, Andrew  
Uri
http://hdl.handle.net/10197/7431
Date Issued
2015-06-10
Date Available
2016-02-03T17:01:18Z
Abstract
This paper presents a comprehensive low-voltage residential load model of price based demand response. High-resolution load models are developed by combing Monte CarloMarkov Chain bottom-up demand models, hot water demand models, discrete state space representation of thermal appliances and composite time-variant electrical load models. Price based demand response is then modeled through control algorithms for thermostatically controlled loads, optimal scheduling of wet appliances and price elasticity matrices for representing the inherent elastic response of the consumer. The developed model is used in a case study to examine the potential distribution network impacts of the introduction of dynamic pricing schemes. The effects of cold load pick-up, rebound peaks, decrease in electrical and demand diversity and impacts on loading and voltage are presented.
Sponsorship
Science Foundation Ireland
Type of Material
Journal Article
Publisher
IEEE
Journal
IEEE Transactions on Smart Grid
Volume
7
Issue
5
Start Page
2285
End Page
2294
Copyright (Published Version)
2015 IEEE
Subjects

Demand response

Distribution networks...

Dynamic pricing

Load modelling

Residential load sect...

Smart grids

DOI
10.1109/TSG.2015.2437451
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
File(s)
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McKenna_(2015)_Residential_Load_Modelling_of_Price_Based_Demand_Response_for_Network_Impact_Studies.pdf

Size

5.97 MB

Format

Adobe PDF

Checksum (MD5)

9f4ab78448cba39d05c8c17c05c32b74

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/.
All other content is subject to copyright.

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