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  5. Optimising Load Flexibility for the Day Ahead in Distribution Networks with Photovoltaics
 
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Optimising Load Flexibility for the Day Ahead in Distribution Networks with Photovoltaics

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Author(s)
Velasco, Jose Angel 
Rigoni, Valentin 
Soroudi, Alireza 
Keane, Andrew 
Amaris, Hortensia 
Uri
http://hdl.handle.net/10197/11173
Date Issued
27 June 2019
Date Available
25T10:45:00Z October 2019
Abstract
In this paper a methodology is proposed to calculate the load demand flexibility that could be activated within the next 24-hours for solving the technical impacts of contingencies that may come up in an unbalanced low voltage distribution networks with high penetration of intermittent DG sources. The methodology is formulated within a Demand Response program environment via load shifting as flexibility enabler mechanism. To achieve that, a non-linear optimisation problem is formulated based on an unbalanced optimal power flow, which allows the determination of the load flexibility that each Demand Response customer could provide at the request of the Distribution System Operator. The demand as well as weather conditions are forecasted for the day ahead. The optimisation problem is solved in a sequence fashion, within a daily framework, splitting the whole problem in optimisation blocks. In each block, the flexible load demand is obtained and the load demand forecasting its updated for the upcoming blocks based on the changes in the scheduled load demand. The methodology is applied to a real distribution network with the load data received from the smart metering infrastructure. The results obtained show the strength of the methodology in solving the technical problems of the network under high unbalanced operation.
Sponsorship
Science Foundation Ireland
Other Sponsorship
Spanish Ministry of Economy and Competiveness
Type of Material
Conference Publication
Publisher
IEEE
Copyright (Published Version)
2019 IEEE
Keywords
  • Load modeling

  • Reactive power

  • Optimization

  • Forecasting

  • Load management

  • Predictive models

  • Power cables

DOI
10.1109/ptc.2019.8810963
Web versions
https://attend.ieee.org/powertech-2019/
Language
English
Status of Item
Peer reviewed
Description
The 13th IEEE PowerTech (POWERTech 2019), Milan, Italy, 24-27 June 2019
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Owning collection
Electrical and Electronic Engineering Research Collection
Scopus© citations
2
Acquisition Date
Feb 1, 2023
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