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Identification of PV system shading using a LiDAR-based solar resource assessment model: an evaluation and cross-validation
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (Built Environment Energy Systems Group)ORCID iD: 0000-0001-6586-4932
Australian National University.ORCID iD: 0000-0001-8197-5181
Australian National University.ORCID iD: 0000-0003-3966-3058
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (Built Environment Energy Systems Group)
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2018 (English)In: Solar Energy, ISSN 0038-092X, E-ISSN 1471-1257, Vol. 159, p. 157-172Article in journal (Refereed) Published
Abstract [en]

Photovoltaic (PV) systems are subject to several different systematic de-rating factors, such as soiling, degradation, inverter mismatch and shading. With increasing penetration of PV in the local grid, Distribution Network Service Providers (DNSPs) are inclined to assess such losses, in order to accurately estimate the total regional power output of distributed PV. The most influential de-rating factor is shading, which can cause ramps on the generated power output, similar to clouds. In this study we evaluate and compare two fundamentally different methods for module orientation parametrisation and shading analysis of PV systems that have been developed in previous work. In the first method, LiDAR (Light Detection and Ranging) data are used to derive the PV module orientation and shading, referred to herein as LiDAR model. The second method, referred to as the QCPV-Tuning model, is based on reported PV power generation, which is firstly quality controlled and parametrised in order to derive module orientation and a loss factor, LF, representing systematic de-rating factors. Secondly, variations in de-ratings throughout the day, mainly due to shading, are explored in a process referred to as Tuning. For both methods, binary time series are derived expressing the presence of shading, which are used to evaluate how the methods corroborate. We evaluate four cases; case 1) evaluates the original versions of the LiDAR and QCPV-Tuning models, while in case 2-4 improvements to the models are introduced. A new filter for extracting representative LiDAR data points for the shading analysis was introduced for the LiDAR model (case 2). For the QCPV-Tuning model significant inaccuracies in the parametrisation of the module orientation were identified due to strong shading in either morning or evening and were thus corrected to observed parameters (case 3). For case 4) improvements on both models were introduced. The Pearson correlation coefficients of shading events for the methods were 0.28, 0.36, 0.42 and 0.50 for case 1-4, respectively. A mismatch in the timing of shading events motivated the comparison of the mean hourly shading, with correlation coefficients of 0.34, 0.43, 0.49 and 0.57 for case 1-4, respectively. The results of this study show that both methods can confidently be used for solar resource assessment, given the suggested improvements.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 159, p. 157-172
Keyword [en]
Solar resource assessment, Shading, PV Tuning, LiDAR
National Category
Energy Systems
Research subject
Engineering Science
Identifiers
URN: urn:nbn:se:uu:diva-332235DOI: 10.1016/j.solener.2017.10.061ISI: 000423007300016OAI: oai:DiVA.org:uu-332235DiVA, id: diva2:1152570
Available from: 2017-10-25 Created: 2017-10-25 Last updated: 2018-03-12Bibliographically approved
In thesis
1. Solar Variability Assessment in the Built Environment: Model Development and Application to Grid Integration
Open this publication in new window or tab >>Solar Variability Assessment in the Built Environment: Model Development and Application to Grid Integration
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Variationer i Solelgenerering i den Byggda Miljön : Modellutveckling och Integration i Elnätet
Abstract [en]

During the 21st century there has been a rapid increase in grid-connected photovoltaic (PV) capacity globally, due to falling system component prices and introduction of various economic incentives. To a large extent, PV systems are installed on buildings, which means they are widely distributed and located close to the power consumer, in contrast to conventional power plants. The intermittency of solar irradiance poses challenges to the integration of PV, which may be mitigated if properly assessing the solar resource. In this thesis, methods have been developed for solar variability and resource assessment in the built environment on both national and local level, and have been applied to grid integration studies. On national level, a method based on building statistics was developed that reproduces the hourly PV power generation in Sweden with high accuracy; correlation between simulated and real power generation for 2012 and 2013 were 0.97 and 0.99, respectively. The model was applied in scenarios of high penetration of intermittent renewable energy (IRE) in the Nordic synchronous power system, in combination with similar models for wind, wave and tidal power. A mix of the IRE resources was sought to minimise the variability in net load (i.e., load minus IRE, nuclear and thermal power). The study showed that a fully renewable Nordic power system is possible if hydropower operation is properly planned for. However, the contribution from PV power would only be 2-3% of the total power demand, due to strong diurnal and seasonal variability. On local level, a model-driven solar resource assessment method was developed based on low-resolution LiDAR (Light Detection and Ranging) data. It was shown to improve the representation of buildings, i.e., roof shape, tilt and azimuth, over raster-based methods, i.e., digital surface models (DSM), which use the same LiDAR data. Furthermore, the new method can provide time-resolved data in contrast to traditional solar maps, and can thus be used as a powerful tool when studying the integration of high penetrations of PV in the distribution grid. In conclusion, the developed methods fill important gaps in our ability to plan for a fully renewable power system.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2017. p. 92
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1598
Keyword
Solar Variability, Photovoltaics, Grid Integration, Distributed Generation, LiDAR, GIS
National Category
Energy Systems
Research subject
Engineering Science
Identifiers
urn:nbn:se:uu:diva-332714 (URN)978-91-513-0149-5 (ISBN)
Public defence
2017-12-21, Häggsalen, Lägerhyddsvägen 1, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2017-11-29 Created: 2017-11-01 Last updated: 2018-03-07

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