Stochastic model predictive control of photovoltaic battery systems using a probabilistic forecast model. (November 2020)

• Record Type: Journal Article
• Authors: Groß, Arne; Wittwer, Christof; Diehl, Moritz
• Is Part Of: European journal of control. Volume 56(2020)
• Page Start: 254
• Page End: 264
• Publication Date: 2020-11
• Subjects: Stochastic MPC -- Stochastic dynamic programming -- Photovoltaic battery systems; Control theory -- Periodicals; Automatic control -- Periodicals; Automatic control -- Mathematics -- Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.ejcon.2020.02.004
• Languages: English
• ISSNs: 0947-3580

Abstract

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.