Amir Abiri Jahromi, Ankur Srivastava, Astha Chawla, Bang Nguyen, Bendong Tan, Bu Siqi, Chendan Li, Charalambos Konstantinou, Fei Teng, Goli Preetham, Ioannis Zografopoulos, Juan Ospina, Junbo Zhao, Linh Vu, Luo Xu, Mohammad Asim Aftab, Mohammadreza Arani, Ömer Sen, Panayiotis Moutis, Pudong Ge, Qinglai Guo, Subham Sahoo, Subhash Lakshminarayana, Suman Rath, Tuyen Vu, Wentao Zhu, Zhaoyuan Wang
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This Task Force (TF) report aims to analyze the dependence of cyber and physical systems on power system operation and control. The study of cyber and physical system interdependency has become increasingly important with the evolution of energy systems. Modeling and analyzing the interdependency between cyber and physical components in energy systems is crucial for ensuring their reliability, resiliency, and security. The TF aims to cover different layers of interdependence between cyber and physical systems through different modeling techniques and analysis methods to identify potential threats and vulnerabilities. Co-simulation methods and tools are effective in analyzing the dependence between cyber and physical systems. Co-simulation is an approach to modeling and simulation that combines different simulation models to create a more comprehensive system model. With the integration of distributed energy resources (DERs) and advancements in sensing devices and communication networks, co-simulation methods and tools can help energy systems operators to better understand the complex interactions between cyber and physical components. This TF report also covers the use of digital twin technology to model and simulate the interactions between cyber and physical components. Digital twins create a virtual replica of the physical system and integrate it with real-time data and simulation models, which allows operators to monitor the system's performance, identify potential vulnerabilities, and develop strategies to improve its resilience and security. Operation and stability control, which consider the interactions between cyber and physical components in an energy system, involve a coordinated approach. This is crucial because the dependable and efficient operation of modern energy systems relies heavily on communication and computer infrastructures for sensing, protection, control, and real-time operation. The objective of this TF is to utilize data analytics, artificial intelligence, and machine learning (ML) to attain optimal operation and stability control in an interdependent cyber-physical energy system.