Design and Implementation of Scalable Communication Interfaces for Reliable and Stable Real-time Co-Simulation of Power Systems

TL;DR

This paper develops a scalable communication interface for real-time power system co-simulation, enabling reliable integration of inverter-based resources through local and remote data exchange methods, with stability enhancements.

Contribution

It introduces a novel scalable communication interface design for real-time power system co-simulation, including a data extrapolation method to improve stability and reliability.

Findings

Demonstrates effective local and remote communication methods using OPAL-RT

Proposes a real-time data extrapolation technique to enhance stability

Provides open-source implementation for further research

Abstract

Co-simulation offers an integrated approach for modeling the large-scale integration of inverter-based resources (IBRs) into transmission and distribution grids. This paper presents a scalable communication interface design and implementation to enable reliable and stable real-time co-simulation of power systems with high IBR penetration. The communication interface is categorized into two types: local and remote. In local scenarios, where subsystems are connected within a single local area network (LAN), low-latency communication facilitates the seamless integration of electromagnetic transient (EMT) and phasor-domain models, enabling efficient interactions with power and energy management algorithms. For remote scenarios, data exchange is achieved via internet-based file sharing or VPN-enabled communication. The performance of both methods is evaluated using OPAL-RT as a real-time simulator, demonstrating scalability, effectiveness, and challenges specific to real-time co-simulation applications. To mitigate instability arising from data resolution mismatches in time-sensitive co-simulations, a real-time data extrapolation method is proposed. This approach significantly enhances stability and reliability, ensuring more accurate simulation outcomes. The implementation code is available on GitHub, providing researchers the tools to replicate and expand upon this work.