An algorithmic approach for creating diverse stochastic feeder datasets for power systems co-simulations

Abstract

Performing co-simulation studies on transmission and distribution systems software environments requires linking the transmission system elements in the former with highly detailed distribution network models and probabilistic load models in the latter. This paper presents a two-step algorithm for: (i) creating a scalable distribution system in a bottom-up approach to match the connected load at the transmission system bus; and (ii) populating the distribution system with active power loads possessing time varying load profiles. We demonstrate an application of the algorithm by creating a detailed distribution topology populated with residential loads expanded from a selected transmission node in a standard test system. All codes are written using the Python coding environment.