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The penetration of electric vehicles (EVs) and photovoltaic (PV) systems has increased globally in the last decade. For planning purposes, the spatiotemporal variability of distributed PV power generation and EV charging needs to be quantified for urban and rural areas. This study introduces a state-of-the-art, open, and generally applicable model framework for assessing the spatiotemporal mismatch between EV load and PV generation for urban and rural areas. The model is applied to a rural and an urban area, both 16 km × 16 km and located in Sweden, and is evaluated for the extreme months of January and July. The results show that an energy deficit of, at most, 86% and an up to ten times surplus took place in January and July, respectively. A high self-consumption (SC) of 77% was observed for January and a high self-sufficiency (SS) of 69% for July. This is to say that during July, PV can fulfill 69% of the EV charging load. Moreover, there were no observed correlations between the PV-EV temporal matching scores (the SS and the SC) and the dominant type of charging, e.g., workplace charging in each grid cell (1 km × 1 km) of the areas. This can be partially attributed to the wide distribution of the rooftop orientations in both areas. This challenges the assumption of low PV-EV temporal matching in residential parts of the city. Applying the proposed methodology to other regions is incentivized., Correction in: Journal of Renewable and Sustainable Energy 12, 059903 (2020); https://doi.org/10.1063/5.0032078

Precipitable water (PW) is an influential variable in regards to clear-sky radiation modelling and solar resource assessment. Thus, the accuracy of solar energy estimates depends on the accuracy of PW measurements. Gridded satellite information is commonly used for solar modelling because of its benefit of a broad geographical coverage, thus a global validation of commonly utilised PW products is imperative. Here, all Level-3 Moderate Resolution Imaging Spectroradiometer (MODIS) daily PW products from the Aqua and Terra satellites (at 1° × 1° spatial resolution) from 01/2000 to 02/2018 are compared and validated against all of NASA’s ground-sensing Aerosol Robotic NETwork (AERONET) V3 Level-2 PW daily averages from sites that have at least one year of observa- tions during 2000–2018 (452 sites representing 675,158 observations). Furthermore, sub-categorisation by Kö- ppen-Geiger climate regions enables climate specific validation to ascertain any distinct climatic influence. The results demonstrate significant climatological influences that impact the derived PW products. It is found that the Terra PW is more accurate than the Aqua PW, and that blending these two products yields a higher accuracy of daily PW estimates. The MODIS PW product also suffers from overestimation at larger magnitudes (>3 cm). The absolute errors do not reduce linearly with the PW magnitude, so that relative errors are far worse in areas of low PW, such as the polar climate. The equatorial climate, with the highest PW records, behaves best. Finally, a simple sensitivity test using the REST2 clear-sky radiation model shows that the global PW RMSE (0.511 cm) of the combined MODIS data would result in a 1.5-2.5% under- or overestimate on direct normal irradiance (DNI) de- pending on latitude relative to using the AERONET mean PW of 1.8971 cm. It is thus concluded that the daily MODIS PW product is not ideal for clear-sky radiation modelling, at least whenever accurate DNI predictions are nec

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 the 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 parameterised 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 cases 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 fo