The Potential of Plug&Play PV in Switzerland
Year: 2024
Type: conference-paper
Abstract: Plug-in photovoltaic systems for balconies are becoming increasingly popular. This paper analyses the energy potential for such systems in Switzerland. Based on the known potential for facade PV systems, a randomized sample of different building categories is used to investigate whether balconies are present and what their photovoltaic potential is. The potential is then extrapolated to Switzerland's building stock. In this project, supported by the Swiss Federal Office of Energy, the authors propose to clarify the relevant issues regarding Plug & Play PV systems in Switzerland. Part of this project is the calculation of the energy potential of such systems in Switzerland. The basis of the work is the solar cadastral data (www.sonnenfassade.ch) of Switzerland. In total 950 facades of the Sonnenfassade.ch dataset were used to assess the suitability for a Plug & Play PV system and determine the potential. This test dataset is representative in terms of house types, sizes and regions. To reach the 950 test examples, 1300 facades were randomly chosen and analyzed by humans regarding the presence and size of balconies with help of publicly available images and maps. 27% of the 1300 facades couldn’t be analyzed due to insufficient imagery. The analysis showed, that 60% of the chosen facades don’t have any balconies. The example dataset has been extrapolated to all facades of Switzerland. The areas usable for Plug & Play PV systems on balconies have been defined based on the typical size of such installations (PDC_STC: 400 W on a balcony with approx. 1x2 m and a module efficiency of 20%). Three options were considered: 1. PDC_STC: 800 W with limitation to 600 W AC inverter nominal power; 2. PDC_STC: 1200 W with limitation to 600 W AC inverter nominal power; 3. PDC_STC: 1200 W DC with no AC limitation. The potential is 894 GWh, 1004 GWh and 1040 GWh respectively.
Source: EU PVSEC
Authors: Remund, Jan, Weber, Anne-Kathrin, Meyer, Lukas, Joss, David, Bucher, Christof, Zwahlen, Theo
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6948151244
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