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Power grids are undergoing major changes due to the rapid adoption of intermittent renewable energy resources and the increased availability of energy storage devices. These trends drive smart-grid operators to envision a future where peer-to-peer energy trading occurs within microgrids, leading to the development of Transactive Energy Systems. Blockchains have garnered significant interest from both academia and industry for their potential application in decentralized TES, in large part due to their high level of resilience. In this paper, we introduce a novel class of attacks against blockchain based TES, which target the gateways that connect market participants to the system. We introduce a general model of blockchain based TES and study multiple threat models and attack strategies. We also demonstrate the impact of these attacks using a testbed based on GridLAB-D and a private Ethereum network. Finally, we study how to mitigate these attack. 

The emergence of blockchains and smart contracts have renewed interest in electrical cyber-physical systems, especially in the area of transactive energy systems. However, despite recent advances, there remain significant challenges that impede the practical adoption of blockchains in transactive energy systems, which include implementing complex market mechanisms in smart contracts, ensuring safety of the power system, and protecting residential consumers’ privacy. To address these challenges, we present TRANSAX, a blockchain-based transactive energy system that provides an efficient, safe, and privacy-preserving market built on smart contracts. Implementation and deployment of TRANSAX in a verifiably correct and efficient way is based on VeriSolid, a framework for the correct-by-construction development of smart contracts, and RIAPS, a middleware for resilient distributed power systems 

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. 

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) [3] 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 [1]. 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 [4]) 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 [2].