MVMO for Parameter Identification of Dynamic Equivalents for Active Distribution Networks

Abstract

Power system dynamic studies rely on model-based simulations. With increasing penetration of DG, modeling of a network that is not only geographically diverse but is also technologically mixed entails great efforts and accuracy for such studies. Despite the availability of highly powerful simulation tools and increased processing power, performing time domain simulations on such interconnected and complex system remains a great hurdle. Reducing the complexity of the model using aggregation can help with these problems. The purpose of this chapter is twofold. First, we go through the concept of network aggregation and introduce a white-box DE suitable for system studies with high penetration of distributed PV. Next, we introduce a sophisticated approach for solving the nonlinear optimization problem of parameter identification of the developed DE. This approach is based on a new optimization technique called mean-variance mapping optimization (MVMO). The parameters of the DE are identified by measuring the response of DE to certain disturbances and comparing them with reference signal sets. By using the proposed approach, we determine the parameters of our suggested DE that entails high accuracy. The accuracy of the DE is measured by calculating the root mean square error (RMSE) value. The time required to simulate faults and perform time domain simulations using a DE is considerably less than for a full-scale model. This has significance for system planners and researchers who want to analyze the effect of increasing penetration of DE into distribution grid on the system as a whole with regard to strategy development