SMTD Co-Simulation Framework With HELICS for Future-Grid Analysis and Synthetic Measurement-Data Generation

Abstract

The power grid is transforming with large amounts of distributed energy resource (DER) integration that is impacting the bulk power system planning and operations. Grid regulators have identified that traditional power system analysis techniques are challenged under the changing grid conditions. Transmission and distribution (T&D) co-simulation is an effective methodology to accurately account for the changing power-grid. A scalable multitimescale T&D (SMTD) co-simulation framework is developed with commercial transmission system solver that can handle large-scale system models, and distribution system solver that can model the distribution systems in detail with dynamic models of load and DER. In this article, PSS/E and GridLAB-D are combined using a Python interface with Hierarchical Engine for Large-scale Infrastructure Co-Simulation software to enable T&D co-simulation. This framework is scalable and parallel computing compatible that enables large-scale system simulation and adoption of T&D co-simulation by the industry and utilities. The SMTD cosimulator is designed to integrate with existing system models and new ones. The article also highlights functional specifications for the T&D co-simulation solvers and models along with providing the algorithms for the steady state, quasi-steady state, and dynamic T&D co-simulation. In context of changing power grid, future power grid planning, validation of controls under critical contingencies and validation of various wide area monitoring and controls can effectively utilize the proposed framework that can capture the T&D interactions accurately. The SMTD cosimulator can also be used to generate realistic synthetic measurement-data with noise and packet drop for uncommon grid events enabling development and validation of power system data analytics and controls.