%0 Journal Article
%A Holtwerth, Alexander
%A Xhonneux, André
%A Müller, Dirk
%T Closed loop model predictive control of a hybrid battery-hydrogen energy storage system using mixed-integer linear programming
%J Energy conversion and management: X
%V 22
%@ 2590-1745
%C Amsterdam
%I Elsevier
%M FZJ-2024-01942
%P 100561 -
%D 2024
%X The derivation of an efficient operational strategy for storing intermittent renewable energies using a hybrid battery-hydrogen energy storage system is a difficult task. One approach for deriving an efficient operational strategy is using mathematical optimization in the context of model predictive control. However, mathematical optimization derives an operational strategy based on a non-exact mathematical system representation for a specified prediction horizon to optimize a specified target. Thus, the resulting operational strategies can vary depending on the optimization settings. This work focuses on evaluating potential improvements in the operational strategy for a hybrid battery-hydrogen energy storage system using mathematical optimization. To investigate the operation, a simulation model of a hybrid energy storage system and a tailor-made mixed integer linear programming optimization model of this specific system are utilized in the context of a model predictive control framework. The resulting operational strategies for different settings of the model predictive control framework are compared to a rule-based  controller  to  show  the  potential  benefits  of  model predictive  control  compared  to  a  conventional approach.  Furthermore,  an  in-depth  analysis  of  different  factors  that  impact  the  effectiveness  of  the  model predictive controller is done. Therefore, a sensitivity analysis of the effect of different electricity demands and resource  sizes  on  the  performance  relative  to  a  rule-based  controller  is  conducted.  The  model  predictive controller  reduced  the  energy  consumption  by  at  least  3.9  %  and  up  to  17.9%  compared  to  a  rule-based controller. Finally, Pareto fronts for multi-objective optimizations with different prediction and control horions are derived and compared to the results of a rule-based controller. A cost reduction of up to 47 % is achieved by a model predictive controller with a prediction horizon of 7 days and perfect foresight.
%F PUB:(DE-HGF)16
%9 Journal Article
%U <Go to ISI:>//WOS:001206649000001
%R 10.1016/j.ecmx.2024.100561
%U https://juser.fz-juelich.de/record/1024052