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This study investigates how the number and geographical distribution of solar installations may reduce aggregate irradiance variability and therefore lessen the overall impact of photovoltaic (PV) on grid distribution. The current distribution of UK solar farms is analysed. It is found that variability is linked to site clustering. Other factors may include distance and direction between sites, proximity to coast, local topography and weather patterns (i.e. wind, cloud etc.). These factors do not operate in isolation but form a complex and unpredictable system. The UK solar farm fleet currently comprises a range of system sizes which, when viewed en masse, reduces temporal variation in PV generation. The predominant southwest–northeast direction of solar farm groups is also beneficial in reducing output variability within grid supply point areas. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the effects of ultraviolet (UV) exposure on amorphous silicon (a-Si), bare crystalline silicon (c-Si), and epoxy resin encapsulated c-Si devices. The long-term reliability of photovoltaic (PV) modules is crucial in ensuring the viability of PV as a successful source of energy. Accelerated UV ageing methods are required to quickly evaluate the UV durability of module materials. A UV exposure unit was designed and constructed and provided an average of 45.7 W/m2 of UV irradiance over the exposure area with a nonuniformity of 14.9%. The a-Si devices lost up to 44% of maximum power (Pmax) at Standard Test Conditions over 500 hours of exposure to UV, with maximum losses of 11% in short-circuit current (Isc), 11% in open-circuit voltage (Voc), 23% in voltage at Pmax (Vmpp), and 29% in current at Pmax (Impp). The epoxy resin encapsulated samples lost up to 6.4% in Pmax, 6% in Isc, and 7% in Impp with the changes in Voc and Vmpp being random. The bare cells showed relatively little degradation. UV radiation thus accelerates the degradation of a-Si devices, deteriorates polymeric encapsulates of modules, and possibly affects the antireflective coatings applied on solar cells. [ABSTRACT FROM AUTHOR]

Large-scale adoption of solar photovoltaics (PV) in the built environment requires automation of roof suitability surveying over large geographical areas. Furthermore, as local PV installation density increases, electricity network operators require clearer information on the overall impact the large number of different rooftop PV systems will have on the stability of the local network. Knowledge of roof features (tilt angle, azimuth angle and area) and localised inplane irradiance data is essential to meet both of these requirements. Such information is currently not available (except by individual roof surveying by PV consultants) and has to be generated. This study demonstrates the automated extraction of building roof plane characteristics from existing wide-area, aircraft-based light detection and ranging data. These characteristics are then aggregated statistically and scaled-up to produce a UK-wide map of average roof tilt variation. Validation of roof tilt with site measurements taken by four different methods demonstrates a mean absolute error of 3°. For major roof plane azimuth angles, banded into compass octants, accurate detection was achieved in 100% of cases, validated by inspection of aerial photography. This is sufficient for calculating in-plane irradiance for a more detailed automated assessment. [ABSTRACT FROM AUTHOR]

Knowledge of roof geometry and physical features is essential for evaluation of the impact of multiple rooftop solar photovoltaic (PV) system installations on local electricity networks. The paper starts by listing current methods used and stating their strengths and weaknesses. No current method is capable of delivering accurate results with publicly available input data. Hence a different approach is developed, based on slope and aspect using aircraft-based Light Detection and Ranging (LiDAR) data, building footprint data, GIS (Geographical Information Systems) tools, and aerial photographs. It assesses each roof's suitability for PV deployment. That is, the characteristics of each roof are examined for fitting of at least a minimum size solar power system. In this way the minimum potential solar yield for region or city may be obtained. Accuracy is determined by ground-truthing against a database of 886 household systems. This is the largest validation of a rooftop assessment method to date. The method is flexible with few prior assumptions. It can generate data for various PV scenarios and future analyses. [ABSTRACT FROM AUTHOR]