Data-driven model predictive control of battery storage units

TL;DR

This paper introduces data-driven model predictive control methods for battery storage in power systems, eliminating the need for explicit dynamic models and improving prediction accuracy for nonlinear battery behavior.

Contribution

It develops both linear and nonlinear data-driven MPC schemes for battery control, enhancing accuracy without explicit system identification.

Findings

Linear data-driven MPC effectively approximates nonlinear dynamics.

Input-nonlinear MPC achieves higher prediction accuracy.

Proposed methods outperform traditional model-based approaches.

Abstract

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.