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Satellite image-based generation of high frequency solar radiation time series for the assessment of solar energy systems

Schreck, Sebastian and Schroedter-Homscheidt, Marion and Klein, Martin and Cao, Karl-Kien (2020) Satellite image-based generation of high frequency solar radiation time series for the assessment of solar energy systems. Meteorologische Zeitschrift. Borntraeger Science Publishers. doi: 10.1127/metz/2020/1008. ISSN 0941-2948.

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Official URL: http://dx.doi.org/10.1127/metz/2020/1008


Solar energy is envisaged as a major pillar of the global transition to a climate-friendly energy system. Variability of solar radiation requires additional balancing measures to ensure a stable and secure energy supply. In order to analyze this issue in detail, solar radiation time series data of appropriate temporal and spatial resolution is necessary. Common weather models and satellites are only delivering solar surface irradiance with temporal resolutions of up to 15 min. Significant short-term variability in irradiances within seconds to minutes however is induced by clouds. Ground-based measurements typically used to capture this variability are costly and only sparsely available. Hence, a method to synthetically generate time series from currently available satellite imagery is of value for researchers, grid operators, and project developers. There are efforts to increase satellite resolution to 1 min, but this is not planned everywhere and will not change the spatial resolution. Therefore, the fundamental question remains if there are alternative strategies to obtain high temporal resolution observations at a pinpoint. This paper presents a method to generate 1 min resolved synthetic time series of global and direct normal irradiances for arbitrary locations. A neural network based on satellite image derived cloud structure parameters enables to classify high-frequency solar radiation variability. Combined with clear-sky radiation data, 1 min time series which reflect the typical variability characteristics of a site are reproduced. Testing and validation against ground observations (BSRN) show that the method can accurately reproduce characteristics such as frequency and ramp distributions. An application case demonstrates the usage in low-voltage grid studies.

Item URL in elib:https://elib.dlr.de/135581/
Document Type:Article
Title:Satellite image-based generation of high frequency solar radiation time series for the assessment of solar energy systems
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Schroedter-Homscheidt, MarionUNSPECIFIEDhttps://orcid.org/0000-0002-1854-903XUNSPECIFIED
Klein, MartinUNSPECIFIEDhttps://orcid.org/0000-0001-7283-4707UNSPECIFIED
Cao, Karl-KienUNSPECIFIEDhttps://orcid.org/0000-0002-9720-0337UNSPECIFIED
Date:13 May 2020
Journal or Publication Title:Meteorologische Zeitschrift
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Publisher:Borntraeger Science Publishers
Keywords:Solar radiation variability, 1 min time series, neural networks, synthetic time series, distribution grid, voltage violations
HGF - Research field:Energy
HGF - Program:Technology, Innovation and Society
HGF - Program Themes:Renewable Energy and Material Resources for Sustainable Futures - Integrating at Different Scales
DLR - Research area:Energy
DLR - Program:E SY - Energy Systems Analysis
DLR - Research theme (Project):E - Systems Analysis and Technology Assessment (old)
Location: Stuttgart
Institutes and Institutions:Institute of Engineering Thermodynamics > Energy Systems Analysis
Institute of Networked Energy Systems > Energy Systems Analysis
Deposited By: Cao, Dr.-Ing. Karl-Kien
Deposited On:04 Aug 2020 15:23
Last Modified:24 Oct 2023 14:16

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