The integration of Information and Communications Technology (ICT) enables real-time communication for smart meters to participate in power system operations. However, Advanced Metering Infrastructures (AMI) are vulnerable to cyber attacks. Both utilities and power consumers may become victims of cyber intrusions. In this paper, a two-stage cyber intrusion protection system is proposed. At the first stage of intrusion detection, a Support Vector Machine (SVM) is used as a detection algorithm to discover suspicious behaviors inside a smart meter. At the second stage, the Temporal Failure Propagation Graph (TFPG) technique is used to generate attack routes for identifying attack events. Finally, the proposed pattern recognition algorithm is used to calculate the similarity between a detected abnormal event and pre-defined cyber attacks. A higher similarity value implies a higher chance that a smart meter is under attack. An AMI security test platform has been developed to: (1) Collect training/testing data for SVM, (2) Simulate and analyze cyber attack events, and (3) Validate the proposed cyber attack protection system. The test platform consists of Network-Simulator 3 (NS-3) software to simulate an AMI network environment and single board computers (SBCs) to emulate the IEEE 802.15.4 communication between a grid router and a smart meter.

Digital twin is often viewed as a technology that can assist engineers and researchers make data-driven system and network-level decisions. Across the scientific literature, digital twins have been consistently theorized as a strong solution to facilitate proactive discovery of system failures, system and network efficiency improvement, system and network operation optimization, among others. With their strong affinity to the industrial metaverse concept, digital twins have the potential to offer high-value propositions that are unique to the energy sector stakeholders to realize the true potential of physical and digital convergence and pertinent sustainability goals. Although the technology has been known for a long time in theory, its practical real-world applications have been so far limited, nevertheless with tremendous growth projections. In the energy sector, there have been theoretical and lab-level experimental analysis of digital twins but few of those experiments resulted in real-world deployments. There may be many contributing factors to any friction associated with real-world scalable deployment in the energy sector such as cost, regulatory, and compliance requirements, and measurable and comparable methods to evaluate performance and return on investment. Those factors can be potentially addressed if the digital twin applications are built on the foundations of a scalable and interoperable framework that can drive a digital twin application across the project lifecycle: from ideation to theoretical deep dive to proof of concept to large-scale experiment to real-world deployment at scale. This paper is an attempt to define a digital twin open architecture framework that comprises a digital twin technology stack (D-Arc) coupled with information flow, sequence, and object diagrams. Those artifacts can be used by energy sector engineers and researchers to use any digital twin platform to drive research and engineering. This paper also provides critical details related to cybersecurity aspects, data management processes, and relevant energy sector use cases.

The high penetration of renewable energy means the grid is increasingly dependent on consumer-owned devices operation, providing a growing nexus between the IoT and the smart grid. However, these devices are much more vulnerable as they are connected, through interconnections to utility, manufacturers, third-party operators, and other consumer IoT devices. Therefore, novel security mechanisms are needed to protect these devices, especially ensuring the integrity of critical measurements and control messages. Fortunately, the growing prevalence of hardware-enforced Trusted Execution Environments (TEEs) provides an opportunity to utilize their secure storage and cryptographic functions to provide enhanced security to various IoT platforms. This paper will demonstrate a TEE-based architecture for smart inverters that utilizes hardware and software-based isolation to prevent tampering of inverter telemetry data. Furthermore, it provides an implementation of the proposed architecture on an ARM TrustZone-enabled platform using OP-TEE on a Raspberri-PI. The developed implementation is evaluated under a set of cybersecurity metrics.

The growing number of consumer-grade network-enabled Distributed Energy Resources (DER) installations introduces new attack vectors that could impact grid operations through coordinated attacks. This work presents a cyber-physical model and risk assessment methodology for analyzing the emerging nexus between Internet of Things-based energy devices and the bulk transmission grid. The cyber model replicates the device-level interconnectivity and software components interaction found within these architectures to understand the feasibly of coordinated attacks, while the physical model is used to assess the attack’s impacts on the grid. The manuscript questions the validity of previous papers’ claims regarding IoT-based grid attacks by addressing key limitations in both the power grid and cyberinfrastructure models of those works. The resulting methodology is then evaluated using the Western Electricity Coordinating Council (WECC) electrical model coupled with DER’s operational statistics from California. The results suggest that current DER penetration rates are not yet significant enough to present serious risk, but continued DER growth may be problematic. Furthermore, the work identifies policies that mitigate these risks through increased device diversity and cybersecurity requirements.