Optimising Load Flexibility for the Day Ahead in Distribution Networks with Photovoltaics
|Title:||Optimising Load Flexibility for the Day Ahead in Distribution Networks with Photovoltaics||Authors:||Velasco, Jose Angel; Rigoni, Valentin; Soroudi, Alireza; Keane, Andrew; Amaris, Hortensia||Permanent link:||http://hdl.handle.net/10197/11173||Date:||27-Jun-2019||Online since:||2019-10-25T10:45:00Z||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.||Funding Details:||Science Foundation Ireland||metadata.dc.description.othersponsorship:||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||Other versions:||https://attend.ieee.org/powertech-2019/||Language:||en||Status of Item:||Peer reviewed||Conference Details:||The 13th IEEE PowerTech (POWERTech 2019), Milan, Italy, 24-27 June 2019|
|Appears in Collections:||Electrical and Electronic Engineering Research Collection|
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