Biblio

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.

Smart appliances, or Internet of Things devices, participate autonomously in electricity markets and improve grid efficiency, but their remote access and control capabilities also introduce vulnerabilities. We show how an adverse generator can manipulate market clearing prices and propose mitigation strategies to correct the impact.

Recent advances in machine learning enable wider applications of prediction models in cyber-physical systems. Smart grids are increasingly using distributed sensor settings for distributed sensor fusion and information processing. Load forecasting systems use these sensors to predict future loads to incorporate into dynamic pricing of power and grid maintenance. However, these inference predictors are highly complex and thus vulnerable to adversarial attacks. Moreover, the adversarial attacks are synthetic norm-bounded modifications to a limited number of sensors that can greatly affect the accuracy of the overall predictor. It can be much cheaper and effective to incorporate elements of security and resilience at the earliest stages of design. In this paper, we demonstrate how to analyze the security and resilience of learning-based prediction models in power distribution networks by utilizing a domain-specific deep-learning and testing framework. This framework is developed using DeepForge and enables rapid design and analysis of attack scenarios against distributed smart meters in a power distribution network. It runs the attack simulations in the cloud backend. In addition to the predictor model, we have integrated an anomaly detector to detect adversarial attacks targeting the predictor. We formulate the stealthy adversarial attacks as an optimization problem to maximize prediction loss while minimizing the required perturbations. Under the worst-case setting, where the attacker has full knowledge of both the predictor and the detector, an iterative attack method has been developed to solve for the adversarial perturbation. We demonstrate the framework capabilities using a GridLAB-D based power distribution network model and show how stealthy adversarial attacks can affect smart grid prediction systems even with a partial control of network.

With the advent of remarkable development of solar power panel and inverter technology and focus on reducing greenhouse emissions, there is increased migration from fossil fuels to carbon-free energy sources (e.g., solar, wind, and geothermal). A new paradigm called Transactive Energy (TE) has emerged that utilizes economic and control techniques to effectively manage Distributed Energy Resources (DERs). Another goal of TE is to improve grid reliability and efficiency. However, to evaluate various TE approaches, a comprehensive simulation tool is needed that is easy to use and capable of simulating the power-grid along with various grid operational scenarios that occur in the transactive energy paradigm. In this research, we present a web-based design and simulation platform (called a design studio) targeted toward evaluation of power-grid distribution system and transactive energy approaches. The design studio allows to edit and visualize existing power-grid models graphically, create new power-grid network models, simulate those networks, and inject various scenario-specific perturbations to evaluate specific configurations of transactive energy simulations. The design studio provides (i) a novel Domain-Specific Modeling Language (DSML) using the Web-based Generic Modeling Environment (WebGME) for the graphical modeling of power-grid, cyber-physical attacks, and TE scenarios, and (ii) a reusable cloud-hosted simulation backend using the Gridlab-D power-grid distribution system simulation tool.

Today's smart-grids have seen a clear rise in new ways of energy generation, transmission, and storage. This has not only introduced a huge degree of variability, but also a continual shift away from traditionally centralized generation and storage to distributed energy resources (DERs). In addition, the distributed sensors, energy generators and storage devices, and networking have led to a huge increase in attack vectors that make the grid vulnerable to a variety of attacks. The interconnection between computational and physical components through a largely open, IP-based communication network enables an attacker to cause physical damage through remote cyber-attacks or attack on software-controlled grid operations via physical- or cyber-attacks. Transactive Energy (TE) is an emerging approach for managing increasing DERs in the smart-grids through economic and control techniques. Transactive Smart-Grids use the TE approach to improve grid reliability and efficiency. However, skepticism remains in their full-scale viability for ensuring grid reliability. In addition, different TE approaches, in specific situations, can lead to very different outcomes in grid operations. In this paper, we present a comprehensive web-based platform for evaluating resilience of smart-grids against a variety of cyber- and physical-attacks and evaluating impact of various TE approaches on grid performance. We also provide several case-studies demonstrating evaluation of TE approaches as well as grid resilience against cyber and physical attacks. 

Emerging industrial platforms such as the Internet of Things (IoT), Industrial Internet (II) in the US and Industrie 4.0 in Europe have tremendously accelerated the development of new generations of Cyber-Physical Systems (CPS) that integrate humans and human organizations (H-CPS) with physical and computation processes and extend to societal-scale systems such as traffic networks, electric grids, or networks of autonomous systems where control is dynamically shifted between humans and machines. Although such societal-scale CPS can potentially affect many aspect of our lives, significant societal strains have emerged about the new technology trends and their impact on how we live. Emerging tensions extend to regulations, certification, insurance, and other societal constructs that are necessary for the widespread adoption of new technologies. If these systems evolve independently in different parts of the world, they will ‘hard-wire’ the social context in which they are created, making interoperation hard or impossible, decreasing reusability, and narrowing markets for products and services. While impacts of new technology trends on social policies have received attention, the other side of the coin – to make systems adaptable to social policies – is nearly absent from engineering and computer science design practice. This paper focuses on technologies that can be adapted to varying public policies and presents (1) hard problems and technical challenges and (2) some recent research approaches and opportunities. The central goal of this paper is to discuss the challenges and opportunities for constructing H-CPS that can be parameterized by social context. The focus in on three major application domains: connected vehicles, transactive energy systems, and unmanned aerial vehicles.Abbreviations: CPS: Cyber-physical systems; H-CPS: Human-cyber-physical systems; CV: Connected vehicle; II: Industrial Internet; IoT: Internet of Things

Emerging industrial platforms such as the Internet of Things (IoT), Industrial Internet (II) in the US and Industrie 4.0 in Europe have tremendously accelerated the development of new generations of Cyber-Physical Systems (CPS) that integrate humans and human organizations (H-CPS) with physical and computation processes and extend to societal-scale systems such as traffic networks, electric grids, or networks of autonomous systems where control is dynamically shifted between humans and machines. Although such societal-scale CPS can potentially affect many aspect of our lives, significant societal strains have emerged about the new technology trends and their impact on how we live. Emerging tensions extend to regulations, certification, insurance, and other societal constructs that are necessary for the widespread adoption of new technologies. If these systems evolve independently in different parts of the world, they will ‘hard-wire’ the social context in which they are created, making interoperation hard or impossible, decreasing reusability, and narrowing markets for products and services. While impacts of new technology trends on social policies have received attention, the other side of the coin – to make systems adaptable to social policies – is nearly absent from engineering and computer science design practice. This paper focuses on technologies that can be adapted to varying public policies and presents (1) hard problems and technical challenges and (2) some recent research approaches and opportunities. The central goal of this paper is to discuss the challenges and opportunities for constructing H-CPS that can be parameterized by social context. The focus in on three major application domains: connected vehicles, transactive energy systems, and unmanned aerial vehicles.Abbreviations: CPS: Cyber-physical systems; H-CPS: Human-cyber-physical systems; CV: Connected vehicle; II: Industrial Internet; IoT: Internet of Things

Owing¹ to an immense growth of internet-connected and learning-enabled cyber-physical systems (CPSs) [1], several new types of attack vectors have emerged. Analyzing security and resilience of these complex CPSs is difficult as it requires evaluating many subsystems and factors in an integrated manner. Integrated simulation of physical systems and communication network can provide an underlying framework for creating a reusable and configurable testbed for such analyses. Using a model-based integration approach and the IEEE High-Level Architecture (HLA) [2] based distributed simulation software; we have created a testbed for integrated evaluation of large-scale CPS systems. Our tested supports web-based collaborative metamodeling and modeling of CPS system and experiments and a cloud computing environment for executing integrated networked co-simulations. A modular and extensible cyber-attack library enables validating the CPS under a variety of configurable cyber-attacks, such as DDoS and integrity attacks. Hardware-in-the-loop simulation is also supported along with several hardware attacks. Further, a scenario modeling language allows modeling of alternative paths (Courses of Actions) that enables validating CPS under different what-if scenarios as well as conducting cyber-gaming experiments. These capabilities make our testbed well suited for analyzing security and resilience of CPS. In addition, the web-based modeling and cloud-hosted execution infrastructure enables one to exercise the entire testbed using simply a web-browser, with integrated live experimental results display.