Stochastic model predictive control of photovoltaic battery systems using a probabilistic forecast model. (November 2020) - Groß, Arne; Wittwer, Christof; Diehl, Moritz
Abstract: Photovoltaic (PV) battery systems allow citizens to take part in a more sustainable energy system. Using the electric energy produced on-site usually entails a financial benefit for the consumer. Furthermore, feed-in peaks during high photovoltaic generation sometimes cause local voltage violations. Therefore, a feed-in limit applies to PV battery systems. In our study, we present a method to generate an optimal control that takes into account the forecast uncertainties. To that end, a stochastic forecast model is developed and used in a dynamic programming framework. We carry out a simulation study assuming the regulatory constraints in Germany. In this setup, our method is shown to mitigate the effects of the forecast uncertainties better than comparable methods.