  In many state-of-the-art control approaches for power systems with storage
units, an explicit model of the storage dynamics is required. With growing
numbers of storage units, identifying these dynamics can be cumbersome. This
paper employs recent data-driven control approaches that do not require an
explicit identification step. Instead, they use measured input/output data in
control formulations. In detail, we propose an economic data-driven model
predictive control (MPC) scheme to operate a small power system with
input-nonlinear battery dynamics. First, a linear data-driven MPC approach that
uses a slack variable to account for plant-model-mismatch is proposed. In a
second step, an input-nonlinear data-driven MPC scheme is deduced. Comparisons
with a reference indicate that the linear data-driven MPC approximates the
nonlinear plant in an acceptable manner. Even better results, however, can be
obtained with the input-nonlinear data-driven MPC scheme which provides
increased prediction accuracy.
