Modeling microgrid power flow dynamics with covariance based realization

Abstract

Reducing transient effects of power quality in the distribution network of a microgrid requires a better understanding of the dynamics of power flow and distributed energy resources (DERs) present within the microgrid. Instead of labor intensive and detailed physics-based distribution network and DER modeling, this paper shows how power flow dynamics can also be modeled in a dynamic equivalent with a data-driven approach that exploits the time dependent covariance of power flow at different locations within a microgrid. The proposed approach results in multi-input multi-output (MIMO) linear time invariant (LTI) discrete-time filter concatenated by a static nonlinearity to model both dynamics and power losses. Estimation of the MIMO LTI dynamics is done by the Covariance Based Realization Algorithm (CoBRA) that can handle noisy perturbed measurements and provide a low order MIMO LTI filter with a normalized steady-state gain. The additional static nonlinearity is found by the estimation of the admittance matrix of the distribution network within the microgrid. The main contribution of this paper is proposing a physics-informed data-driven approach to modeling microgrid power flow dynamics, where the CoBRA method is implemented to learn parameters from data while observing a priori physical constraints. The approach is illustrated on experimental data from a microgrid with multiple DERs and it is shown that an excellent agreement in power flow dynamics is obtained over a large operating range, which demonstrates its advantage over the N4SID method.