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Energy-based stochastic MPC for integrated electricity-hydrogen VPP in real-time markets

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Virtual Power Plants (VPPs) comprising renewables and hydrogen production through power-to-gas technologies can help to increase renewable penetration and to improve operational flexibility and economic performance. However, the uncertainty inherent to forecasts of renewable generation and energy prices renders cost effective operation difficult. The present paper approaches the issue by means of receding-horizon stochastic optimization (i.e. by stochastic Model Predictive Control (MPC)). Differently from previous works, we do not tackle computational tractability with a sampling-based approach, but by mapping quantile forecasts of virtual energy profiles to the mode of operation that has the highest probability of being optimal. This way, we reduce the computational load and the forecasting burden. Furthermore, simulation studies show that the proposed algorithm can attain a significant percentage of the revenue of optimal control with perfect forecasts.

Author(s):

Riccardo Remo Appino    
Karlsruhe Institute of Technology
Germany

Han Wang    
University of Melbourne
Australia

Jorge Ángel González Ordiano    
Colorado State University
United States

Timm Faulwasser    
Department of Electrical Engineering and Information Technology, TU Dortmund University
Germany

Ralf Mikut    
Karlsruhe Institute of Technology
Germany

Veit Hagenmeyer    
Karlsruhe Institute of Technology
Germany

Pierluigi Mancarella    
University of Melbourne
Australia

 

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