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Lukac N.,University of Maribor | Lukac N.,Laboratory for Geometric Modeling and Multimedia Algorithms | Seme S.,University of Maribor | Zlaus D.,University of Maribor | And 5 more authors.

One of the major challenges today is assessing the suitability of PV (photovoltaic) systems' installations on buildings' roofs regarding the received solar irradiance. The availability of aerial laser-scanning, namely LiDAR (Light Detection And Ranging), means that assessment can be performed automatically over large-scale urban areas in high accuracy by considering surfaces' topographies, long-term direct and diffuse irradiance measurements, and influences of shadowing. The solar potential metric was introduced for this purpose, however it fails to provide any insights into the production of electrical energy by a specific PV system. Hence, the PV potential metric can be used that integrates received instantaneous irradiance which is then multiplied by the PV system's efficiency characteristics. Many existing PV potential metrics over LiDAR data consider the PV modules' efficiencies to be constant, when in reality they are nonlinear. This paper presents a novel PV potential estimation over LiDAR data, where the PV modules' and solar inverter's nonlinear efficiency characteristics are approximated by modelled functions. The estimated electrical energy production from buildings' roofs within an urban area was extensively analysed by comparing the constant and nonlinear efficiency characteristics of different PV module types and solar inverters. The obtained results were confirmed through measurements performed on an existing PV system. © 2014 Elsevier Ltd. Source

Sreckovic N.,University of Maribor | Sreckovic N.,Power Engineering Laboratory | Lukac N.,University of Maribor | Lukac N.,Laboratory for Geometric Modeling and Multimedia Algorithms | And 4 more authors.

Proliferation of distributed generation units, integrated within the distribution network requires increased attention to their proper placements. In urban areas, buildings' rooftops are expected to have greater involvement in the deployment of PV (photovoltaic) systems. This paper proposes a novel procedure for determining roof surfaces suitable for their installation. The PV potential of roof surfaces is assessed based on Light Detection And Ranging (LiDAR) data and pyranometer measurements. Then, the time-dependent PV generation profiles, electricity distribution network configuration, and time-dependent loading profiles are used together over time-steps for selecting those roof surfaces with the highest PV potential, which would lead to the highest reduction of network losses per year. The presented procedure was implemented within a real urban area distribution network. The results obtained confirmed that PV potential assessment could be an insufficient criterion when selecting those roof surfaces suitable for the installation of PV systems. In order to obtain relevant results, network configuration and time-dependent loading and generation profiles must be considered as well. © 2015 Elsevier Ltd. Source

Lukac N.,University of Maribor | Lukac N.,Laboratory for Geometric Modeling and Multimedia Algorithms | Zlaus D.,University of Maribor | Zlaus D.,Laboratory for Geometric Modeling and Multimedia Algorithms | And 6 more authors.
Applied Energy

The roof surfaces within urban areas are constantly attracting interest regarding the installation of photovoltaic systems. These systems can improve self-sufficiency of electricity supply, and can help to decrease the emissions of greenhouse gases throughout urban areas. Unfortunately, some roof surfaces are unsuitable for installing photovoltaic systems. This presented work deals with the rating of roof surfaces within urban areas regarding their solar potential and suitability for the installation of photovoltaic systems. The solar potential of a roof's surface is determined by a new method that combines extracted urban topography from LiDAR data with the pyranometer measurements of global and diffuse solar irradiances. Heuristic annual vegetation shadowing and a multi-resolution shadowing model, complete the proposed method. The significance of different influential factors (e.g. shadowing) was analysed extensively. A comparison between the results obtained by the proposed method and measurements performed on an actual PV power plant showed a correlation agreement of 97.4%. © 2012 Elsevier Ltd. Source

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