Toward data-driven predictive control of multi-energy distribution systems

Abstract

The necessity to obtain, to parametrize, and to maintain models of the underlying dynamics impedes predictive control of energy systems in many real-world applications. To alleviate the need for explicit model knowledge this paper proposes a framework for the operation of distributed multi-energy systems via data-driven predictive control with stochastic uncertainties. Instead of modeling the dynamics of the individual distributed energy resources, our approach relies on measured input–output data of the distributed resources only. Moreover, we combine data-driven predictive control with forecasts of exogenous signals (renewable generations and demands) by Gaussian processes. A simulation study based on realistic data illustrates the efficacy of the proposed scheme to handle mild non-linearities and measurement noise.