2017
DOI: 10.1016/j.solener.2017.09.037
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A multi-step scheme for spatial analysis of solar and photovoltaic production variability and complementarity

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Cited by 30 publications
(18 citation statements)
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“…The system includes characteristics of a general PV module and inverter, the arrangement of the generator and some efficiency losses. The characteristics of the general PV system are the same as the ones described in [29,30]. Power from the PV generator also depends on the cell's temperature.…”
Section: Pv Potentialmentioning
confidence: 99%
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“…The system includes characteristics of a general PV module and inverter, the arrangement of the generator and some efficiency losses. The characteristics of the general PV system are the same as the ones described in [29,30]. Power from the PV generator also depends on the cell's temperature.…”
Section: Pv Potentialmentioning
confidence: 99%
“…Power from the PV generator also depends on the cell's temperature. Mean temperature from climate models is also used to compute the cell's temperature using the procedure explained in [30]. The whole process and the R package, solaR, used for the computation are described in detail in [31].…”
Section: Pv Potentialmentioning
confidence: 99%
“…The identification of power generation patterns based on high-resolution data allows synthesizing the information of each point of the grid by extracting the main pattern features in the region under analysis. With this approach, it is possible to filter the local effects of the individual grid points and group grid points with a similar power production behavior providing a better understanding of generation features [31,32]. Moreover, instead of detecting specific grid points that could be unfeasible due to constraints (e.g., protected zones), using this approach, the most suitable It should be noted that the proposed methodology only addresses the temporal and spatial integration aspects of wind and solar PV within a region of control (of the power system) and the benefits of the complementarity assessment.…”
Section: Identification Of Power Generation Patternsmentioning
confidence: 99%
“…Regarding the clustering analysis applied to solar resource assessment (Zagouras et al, 2013(Zagouras et al, , 2014a(Zagouras et al, , 2014bGutiérrez et al, 2017;Rodríguez-Benítez et al, 2018), this was accomplished to study the spatial variability by dividing the territory, with regard to the temporal variability of the global solar radiation, into coherent zones. Global solar radiation estimations over a region from satellites or numerical weather models usually need to be corrected by using scarce measurements and thus measuring sites for solar radiation over this region must be determined (Polo et al, 2016).…”
Section: Introductionmentioning
confidence: 99%