The research team used real-world energy consumption data from 4,190 Swiss households to analyze how diﬀerent cost scenarios may inﬂuence optimal photovoltaic-battery (PVB) system deployment. the techno-economic model is conceived to optimize the net present value for each household in the dataset based on a set of input parameters, such as weather, load proﬁles, tariﬀs, physical properties, and component costs.
A group of researchers from Switzerland’s ETH Zurich – Swiss Federal Institute of Technology and Germany’s University of Bamberg has developed a techno-economic simulation model based on a machine learning algorithm, which is aimed at optimizing conﬁguration and proﬁtability of residential solar-plus-storage power systems. In the paper, Economic assessment of photovoltaic battery systems based on household load profiles, the research team has created its model on the basis of real-world energy consumption data from 4,190 Swiss households, which were taken under current electricity rates and weather conditions in Zurich. The authors of the study stressed, however, that their algorithm is based only on a limited set of features, and on shorter measurement time-frames of smart-meter data.
Several cost scenarios were presented in the research, the most optimistic of which envisages that the installation of a residential photovoltaic-battery (PVB) system, with a mean installed PV power of 4.4 kW and a mean battery size of 9.6 kWh, will be profitable for 99.9% of Swiss households. Under this scenario, the cost of residential solar is expected to be under €1,000 per kW of PV installed, while that of the battery will not exceed €250 per kWh.
Under the current scenario, however – where costs of a solar power generator are around €2,000 per kW, and those of a residential storage system at approximately €1,000 per kWh in Switzerland – only 40% of the analyzed households are suitable for a profitable PV system (without any subsidy), while only 0.1% of them can enable a bankable deployment of a battery. The Swiss scientists said the techno-economic model is conceived to optimize the net present value (NPV) for each household in the dataset based on a set of input parameters, such as weather, load proﬁles, tariﬀs, physical properties, and component costs.
“Many parameters depend on the local building properties such as available roof surface area, roof orientation, and roof tilting,” the study notes. The researchers also said that annual demand is, in general, a key predictor of profitability for solar-plus-storage solutions, although the profitability of a residential solar-plus-solar project may be variable, even for households with a similar annual total demand. Read more from pv-magazine.com…
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