At least 55% of the 9.6 million rooftops in Switzerland could host solar PV panels. And even if the panels were installed only mainly on south-facing rooftops, Switzerland would be able to cover more than 40% of its electricity demand, claims a research from the Solar Energy and Building Physics Laboratory (LESO-PB) at the Ecole Polytechnique Fédérale de Lausanne and University of Oxford's Environmental Change Institute. Their findings have been published in Applied Energy and can be viewed on ScienceDirect magazine's website.
The researchers say the country has currently been able to tap into only a tenth of its estimated 24 TWh/year PV potential leaving scope for another 90% to be unlocked. Margin of error of this 24 TWh is 9 TWh, which factors in variability of solar radiation and the methodology used.
To arrive at this estimate, the team developed a methodology bringing together machine learning algorithms with geographical information systems and physical models to assess the PV potential by the hour. This algorithm judged the size of the roof, its orientation, location among other parameters.
"We have a surplus of energy in the summer and a shortage in the winter, and no energy at all at night. To address that imbalance, we need to consider other forms of renewable energy to make up for the shortfalls and allow energy to be stored," said Alina Walch, head of this research project. "Hydroelectric power is an attractive way of storing energy, but the content of storage dams varies with the seasons. Wind power, used on a large scale, could fill the gaps."
The researchers of this work hope their findings can enable formation of effective policies to encourage solar PV installation on rooftops. Their work comes at a time when LESO-PB, the Swiss National Science Foundation, Innosuisse and the Swiss Federal Office of Energy are developing a platform to enable cities, cantons, municipalities and individuals to visualize renewable energy potential of their buildings.
They also believe the method employed can also be used for large-scale assessments of future energy systems with decentralized electricity grids.