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Spatial Aggregation of Small-Scale Photovoltaic Generation Using Voronoi Decomposition
In this article, a methodology based in Voronoi decomposition is proposed to spatially aggregate small-scale solar generation. The locations of relevant electrical infrastructure are used to manage the uncertainty on locating solar photovoltaic installations. The known coordinates of step-down high-...
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Published in: | IEEE transactions on sustainable energy 2020-10, Vol.11 (4), p.2677-2686 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | In this article, a methodology based in Voronoi decomposition is proposed to spatially aggregate small-scale solar generation. The locations of relevant electrical infrastructure are used to manage the uncertainty on locating solar photovoltaic installations. The known coordinates of step-down high- to medium-voltage electrical substations (i.e. bulk supply points) are used to divide the territory and find multiple representative locations where solar resource can be assessed. Modelling solar photovoltaic generation from global solar radiation observations permits the estimation of power output and degradation factor due to age for the entire small-scale photovoltaic fleet. The results are validated against multiple solar installations across the region of study (Northern Ireland, UK) and show a relatively low root mean square error with monthly values ranging from 0.036 to 0.123 kW/kWp. The proposed method is scalable to larger and smaller geographical areas and transferable to other categories of solar photovoltaic installations. This methodology can serve as a basis for multiple applications, such as solar generation forecasting. System operators could utilise this method to improve knowledge of when, where and in what amount additional resources would be required to manage solar penetration in favour of a robust, low-carbon and efficient power network. |
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ISSN: | 1949-3029 1949-3037 |
DOI: | 10.1109/TSTE.2020.2970217 |