Better Data Structures for Co-simulation of Distribution System with GridLAB-D and Python - Guddanti, Kishan Prudhvi; Ye, Yanzhu; Chongfuangprinya, Panitarn; Yang, Bo; Weng, Yang (IEEE Power & Energy Society General Meeting, 2020)
Due to the high penetration of distributed energy resources (DERs) in the distribution system, there is an increasing need for advanced tools to thoroughly study the impacts of DERs on distribution networks under various DER control/modeling scenarios. This type of tools not only requires a powerful network simulation engine in distribution grids, but also a flexible and interactive environment for easy development of advanced analysis/control algorithms, e.g., cutting-edge machine learning packages. If the software can be open-sourced, the power industry can further enjoy transparency and faster-time-to-market transition to expedite renewable integration. Past work does not give a fully independent data structure to separate the simulation layer and the application layer. Therefore, this work aims at providing full independence while integrating the two most powerful open-source tools in distribution grid simulation and an extremely popular programming language: GridLAB-D and Python. Specifically, we carefully create (1) an open and flexible design, (2) easy-to-develop analytical application scenarios, and (3) compatibility with a variety of third-party tools. We demonstrate features (1) and (2) of this co-simulation framework with a use case study on integration capacity analysis (ICA) and we demonstrate feature (3) as an example to conduct graphical analysis in Python for distribution system analysis with a near-zero effort. A highly accurate and fast system-wide ICA result demonstrates the supreme data structure and easy-to-extend architecture for speeding renewable integration. The code is available for download.