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Solar energy plays a central role in the energy transition. Clouds generate locally large fluctuations in the generation output of photovoltaic systems, which is a major problem for energy systems such as microgrids, among others. For an optimal design of a power system, this work analyzed the variability using a spatially distributed sensor network at Stuttgart Airport. It has been shown that the spatial distribution partially reduces the variability of solar radiation. A tool was also developed to estimate the output power of photovoltaic systems using irradiation time series and assumptions about the photovoltaic sites. For days with high fluctuations of the estimated photovoltaic power, different energy system scenarios were investigated. It was found the approach can be used to have a more realistic representation of aggregated PV power taking spatial smoothing into account and that the resulting PV power generation profiles provide a good basis for energy system design considerations like battery sizing.
A former remote area power supply was converted to a smart cogeneration subnet with combined heat and power to develop and validate a forecast based energy management at the University of Applied Sciences in Offenburg/Germany. Locally processed weather forecasts and forecasted demand profiles are integrated to allow a precise reaction to changes of fluctuating power sources, changes in scheduled demand profiles and to improve the energy efficiency of the supply. The management of the electrical and thermal storages is influenced by the forecasted energy contributions and the forecasted demand. Further approaches should improve the accuracy of forecasting algorithms and integrate parameter models gained of a detailed monitoring to realize predictive controllers.