Digital twins of multiple energy networks based on real-time simulation using holomorphic embedding method, Part II: Data-driven simulation.

Digital twins can act as a transformative role in improving the operational performance of multiple energy networks (MEN) by examining the impact of implementing newer technologies, extra equipment, control strategies, etc. The objective of this series of papers is to present digital twins of MEN that can be simulated in real-time using the holomorphic embedding method. While Part I concentrated on mechanism-driven modeling of the holomorphic embedding-based model (HEM), this paper (Part II) focuses on data-driven simulation to ensure the twin is synchronized with actual physical objects. A parametric synchronization method (PSM) is proposed, which assists HEM in closely matching the actual dynamic behavior with time-varying characteristics. A machine learning surrogate model (MLSM) is proposed to accelerate the search of HEM's convergence radius, which is critical to maintaining the twin's real-time computational performance. Finally, the finalized digital twins are tested on the OPAL-RT simulation platform equipped with a real-time simulator. In a medium-sized MEN test case with a minor time step of 0.01 s, the digital twins can be validated with a faster than real-time performance even without the assistance of parallel computing. • An architecture of digital twins for multiple energy networks is proposed. • Parametric synchronization method is proposed to maintain parameters up-to-date. • Machine learning surrogate model is proposed to accelerate the execution speed. • Digital twin is validated on simulation platform equipped with real-time simulator. [ABSTRACT FROM AUTHOR]