Identification of Dynamic Equivalents of Active Distribution Networks through MVMO

This paper introduces an approach for determining the parameters of aggregated dynamic equivalents for active distribution system from a reference set of signals associated with various disturbances using mean-variance mapping optimization (MVMO) algorithm. As the penetration of renewable energy sources in the LV and MV network increases, it has become extremely important that we have models that mimic actual system response with least computational overhead for bulk transmission system studies. Full scale detailed, complex, interconnected models are most accurate but carry significant simulation time, making analysis unfeasible. Dynamic equivalents (DE) are simplified representations of larger models that mimic the dynamic response of those models. They can be used to replace the neighbor area in a study while the area of focus is modeled with great detail. This reduces the computational burden. Our test system is a small part of the German MV/LV network whose dynamic equivalent is identified and compared. The dynamic equivalent is a Western Electric Coordination Council (WECC) developed distributed PV model (PVD1). This identification is done with the use of MVMO algorithm that utilizes a single parent crossover and a unique mapping function. The results show an almost identical response with good normalized root mean square error (NRMSE) between the detailed and aggregated model. The MVMO shows fast convergence and accurate results.