Biblio

Cyber-physical systems (CPS) are smart systems that consist of highly interconnected networks of physical and computational components. The tight integration of a wide range of heterogeneous components enables new functionality and quality of life improvements in critical infrastructures such as smart cities, intelligent buildings, and smart energy systems. One approach to study CPS uses both simulations and hardware-in-theloop (HIL) to test the physical dynamics of hardware in a controlled environment. However, because CPS experiment design may involve domain experts from multiple disciplines who use different simulation tool suites, it can be a challenge to integrate the heterogeneous simulation languages and hardware interfaces into a single HIL simulation. The National Institute of Standards and Technology (NIST) is working on the development of a universal CPS environment for federation (UCEF) that can be used to design and run experiments that incorporate heterogeneous physical and computational resources over a wide geographic area. This development environment uses the High Level Architecture (HLA), which the Department of Defense has advocated for co-simulation in the field of distributed simulations, to enable communication between hardware and different simulation languages such as Simulink and LabVIEW. This paper provides an overview of UCEF and motivates how the environment could be used to develop energy applications using an illustrative example of an emulated heat pump system.

Modern electric grids that integrate smart grid technologies require different approaches to grid operations. There has been a shift towards increased reliance on distributed sensors to monitor bidirectional power flows and machine learning based load forecasting methods (e.g., using deep learning). These methods are fairly accurate under normal circumstances, but become highly vulnerable to stealthy adversarial attacks that could be deployed on the load forecasters. This paper provides a novel model-based Testbed for Simulation-based Evaluation of Resilience (TeSER) that enables evaluating deep learning based load forecasters against stealthy adversarial attacks. The testbed leverages three existing technologies, viz. DeepForge: for designing neural networks and machine learning pipelines, GridLAB-D: for electric grid distribution system simulation, and WebGME: for creating web-based collaborative metamodeling environments. The testbed architecture is described, and a case study to demonstrate its capabilities for evaluating load forecasters is provided.

NIST, in collaboration with Vanderbilt University, has assembled an open-source tool set for designing and implementing federated, collaborative and interactive experiments with cyber-physical systems (CPS). These capabilities are used in our research on CPS at scale for Smart Grid, Smart Transportation, IoT and Smart Cities. This tool set, "Universal CPS Environment for Federation (UCEF)," includes a virtual machine (VM) to house the development environment, a graphical experiment designer, a model repository, and an initial set of integrated tools including the ability to compose Java, C++, MATLABTM, OMNeT++, GridLAB-D, and LabVIEWTM based federates into consolidated experiments. The experiments themselves are orchestrated using a ‘federation manager federate,’ and progressed using courses of action (COA) experiment descriptions. UCEF utilizes a method of uniformly wrapping federates into a federation. The UCEF VM is an integrated toolset for creating and running these experiments and uses High Level Architecture (HLA) Evolved to facilitate the underlying messaging and experiment orchestration. Our paper introduces the requirements and implementation of the UCEF technology and indicates how we intend to use it in CPS Measurement Science.