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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
Abstract
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/ | ||||||||||||||||
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| Document Type: | Article | ||||||||||||||||
| Title: | Artificial Learning Dispatch Planning for Flexible Renewable-Energy Systems | ||||||||||||||||
| Authors: |
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| Date: | 24 March 2020 | ||||||||||||||||
| Journal or Publication Title: | Energies | ||||||||||||||||
| Refereed publication: | Yes | ||||||||||||||||
| Open Access: | Yes | ||||||||||||||||
| Gold Open Access: | Yes | ||||||||||||||||
| In SCOPUS: | Yes | ||||||||||||||||
| In ISI Web of Science: | Yes | ||||||||||||||||
| Volume: | 13 | ||||||||||||||||
| DOI: | 10.3390/en13061517 | ||||||||||||||||
| Page Range: | p. 1517 | ||||||||||||||||
| Publisher: | Multidisciplinary Digital Publishing Institute (MDPI) | ||||||||||||||||
| ISSN: | 1996-1073 | ||||||||||||||||
| Status: | Published | ||||||||||||||||
| 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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