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Artificial Learning Dispatch Planning for Flexible Renewable-Energy Systems

do amaral Burghi, Ana Carolina and Hirsch, Tobias and Pitz-Paal, Robert (2020) Artificial Learning Dispatch Planning for Flexible Renewable-Energy Systems. Energies, 13, p. 1517. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/en13061517. ISSN 1996-1073.

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Official URL: https://doi.org/10.3390/en13061517


Environmental and economic needs drive the increased penetration of intermittent renewable energy in electricity grids, enhancing uncertainty in the prediction of market conditions and network constraints. Thereafter, the importance of energy systems with flexible dispatch is reinforced, ensuring energy storage as an essential asset for these systems to be able to balance production and demand. In order to do so, such systems should participate in wholesale energy markets, enabling competition among all players, including conventional power plants. Consequently, an effective dispatch schedule considering market and resource uncertainties is crucial. In this context, an innovative dispatch optimization strategy for schedule planning of renewable systems with storage is presented. Based on an optimization algorithm combined with a machine-learning approach, the proposed method develops a financial optimal schedule with the incorporation of uncertainty information. Simulations performed with a concentrated solar power plant model following the proposed optimization strategy demonstrate promising financial improvement with a dynamic and intuitive dispatch planning method (up to 4% of improvement in comparison to an approach that does not consider uncertainties), emphasizing the importance of uncertainty treatment on the enhanced quality of renewable systems scheduling.

Item URL in elib:https://elib.dlr.de/134508/
Document Type:Article
Title:Artificial Learning Dispatch Planning for Flexible Renewable-Energy Systems
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
do amaral Burghi, Ana CarolinaUNSPECIFIEDhttps://orcid.org/0000-0002-5058-9162UNSPECIFIED
Hirsch, TobiasUNSPECIFIEDhttps://orcid.org/0000-0003-0063-0128UNSPECIFIED
Pitz-Paal, RobertUNSPECIFIEDhttps://orcid.org/0000-0002-3542-3391UNSPECIFIED
Date:24 March 2020
Journal or Publication Title:Energies
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Page Range:p. 1517
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Keywords:dispatch optimization concentrating solar power CSP artificial learning
HGF - Research field:Energy
HGF - Program:Renewable Energies
HGF - Program Themes:Concentrating Solar Thermal Technology
DLR - Research area:Energy
DLR - Program:E SW - Solar and Wind Energy
DLR - Research theme (Project):E - Advanced Heat Transfer Media (old)
Location: Stuttgart
Institutes and Institutions:Institute of Solar Research > Linienfokussierende Systeme
Deposited By: Hirsch, Dr.-Ing. Tobias
Deposited On:30 Mar 2020 08:31
Last Modified:25 Oct 2023 08:18

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