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Moving target defense (MTD) in power grids is an emerging defense technique that has gained prominence in the recent past. It aims to solve the long-standing problem of securing the power grid against stealthy attacks. The key idea behind MTD is to introduce periodic/event-triggered controlled changes to the power grid's SCADA network/physical plant, thereby invalidating the knowledge attackers use for crafting stealthy attacks. In this paper, we provide a comprehensive overview of this topic and classify the different ways in which MTD is implemented in power grids. We further introduce the guiding principles behind the design of MTD, key performance metrics, and the associated trade-offs in MTD and identify the future development of MTD for power grid security., Comment: 10 pages, 3 figures, survey

The electricity grid has evolved from a physical system to a cyber-physical system with digital devices that perform measurement, control, communication, computation, and actuation. The increased penetration of distributed energy resources (DERs) that include renewable generation, flexible loads, and storage provides extraordinary opportunities for improvements in efficiency and sustainability. However, they can introduce new vulnerabilities in the form of cyberattacks, which can cause significant challenges in ensuring grid resilience. %, i.e. the ability to rapidly restore grid services in the face of severe disruptions. We propose a framework in this paper for achieving grid resilience through suitably coordinated assets including a network of Internet of Things (IoT) devices. A local electricity market is proposed to identify trustable assets and carry out this coordination. Situational Awareness (SA) of locally available DERs with the ability to inject power or reduce consumption is enabled by the market, together with a monitoring procedure for their trustability and commitment. With this SA, we show that a variety of cyberattacks can be mitigated using local trustable resources without stressing the bulk grid. The demonstrations are carried out using a variety of platforms with a high-fidelity co-simulation platform, real-time hardware-in-the-loop validation, and a utility-friendly simulator., Comment: Submitted to the Proceedings of the National Academy of Sciences (PNAS), under review

The growing adoption of Internet-of-Things (IoT)-enabled energy smart appliances (ESAs) at the consumer end, such as smart heat pumps, electric vehicle chargers, etc., is seen as key to enabling demand-side response (DSR) services. However, these smart appliances are often poorly engineered from a security point of view and present a new threat to power grid operations. They may become convenient entry points for malicious parties to gain access to the system and disrupt important grid operations by abruptly changing the demand. Unlike utility-side and SCADA assets, ESAs are not monitored continuously due to their large numbers and the lack of extensive monitoring infrastructure at consumer sites. This article presents an in-depth analysis of the demand side threats to power grid operations including (i) an overview of the vulnerabilities in ESAs and the wider risk from the DSR ecosystem and (ii) key factors influencing the attack impact on power grid operations. Finally, it presents measures to improve the cyber-physical resilience of power grids, putting them in the context of ongoing efforts from the industry and regulatory bodies worldwide.

Advancements in digital automation for smart grids have led to the installation of measurement devices like phasor measurement units (PMUs), micro-PMUs ($\mu$-PMUs), and smart meters. However, a large amount of data collected by these devices brings several challenges as control room operators need to use this data with models to make confident decisions for reliable and resilient operation of the cyber-power systems. Machine-learning (ML) based tools can provide a reliable interpretation of the deluge of data obtained from the field. For the decision-makers to ensure reliable network operation under all operating conditions, these tools need to identify solutions that are feasible and satisfy the system constraints, while being efficient, trustworthy, and interpretable. This resulted in the increasing popularity of physics-informed machine learning (PIML) approaches, as these methods overcome challenges that model-based or data-driven ML methods face in silos. This work aims at the following: a) review existing strategies and techniques for incorporating underlying physical principles of the power grid into different types of ML approaches (supervised/semi-supervised learning, unsupervised learning, and reinforcement learning (RL)); b) explore the existing works on PIML methods for anomaly detection, classification, localization, and mitigation in power transmission and distribution systems, c) discuss improvements in existing methods through consideration of potential challenges while also addressing the limitations to make them suitable for real-world applications.

This paper addresses the load restoration problem after power outage events. Our primary proposed methodology is using multi-agent deep reinforcement learning to optimize the load restoration process in distribution systems, modeled as networked microgrids, via determining the optimal operational sequence of circuit breakers (switches). An innovative invalid action masking technique is incorporated into the multi-agent method to handle both the physical constraints in the restoration process and the curse of dimensionality as the action space of operational decisions grows exponentially with the number of circuit breakers. The features of our proposed method include centralized training for multi-agents to overcome non-stationary environment problems, decentralized execution to ease the deployment, and zero constraint violations to prevent harmful actions. Our simulations are performed in OpenDSS and Python environments to demonstrate the effectiveness of the proposed approach using the IEEE 13, 123, and 8500-node distribution test feeders. The results show that the proposed algorithm can achieve a significantly better learning curve and stability than the conventional methods., Comment: 12 pages, 19 figures, journal under review

In this paper, we aim to leverage peer to peer (P2P) transactive energy framework for optimal control of rooftop or neighborhood solar power with battery electric storage systems (BESS). Here we propose that the multiple neighboring customers would interconnect to form a community DC grid while still being connected to the main utility grid. The proposed infrastructure would (i) increase the resilience of the customers in case the utility grid becomes unavailable, (ii) reduce AC/DC conversion losses since a majority of the DER generation is in DC, and (iii) with the help of BESS, the customers will have a lesser impact on the utility grid in terms of California 'duck curve.' The exchange of resources and further utilization of economies of scale would significantly reduce the cost of this new infrastructure. Customers will be able to fall back on the utility AC grid in case of a cyber-attack on the community DC grid, improving cyber-security further. The proposed P2P transactive energy scheme is based on cooperation among the homeowners, using additional metering nodes to allow the sale or purchase of energy among residents. While it is expected that the scope will be limited to customers within a neighborhood, multiple neighborhoods can still interact via a utility AC grid. While it is likely that the P2P controller carrying out optimal control would work seamlessly, customers' interactions will be based on a zero-trust approach. As a part of this approach, multifactor authentication, role-based access control, etc., would be utilized, significantly improving overall cybersecurity. We examine these three strategies as case studies: (i) single home with solar power, (ii) single home with Solar power and BESS, and (iii) two or more homes with solar power and community energy storage to facilitate the policymakers making the recommendation., Comment: in Cigre Annual Meeting, Paris, Aug. 2022

Power grid operation subject to an extreme event requires decision-making by human operators under stressful condition with high cognitive load. Decision support under adverse dynamic events, specially if forecasted, can be supplemented by intelligent proactive control. Power system operation during wildfires require resiliency-driven proactive control for load shedding, line switching and resource allocation considering the dynamics of the wildfire and failure propagation. However, possible number of line- and load-switching in a large system during an event make traditional prediction-driven and stochastic approaches computationally intractable, leading operators to often use greedy algorithms. We model and solve the proactive control problem as a Markov decision process and introduce an integrated testbed for spatio-temporal wildfire propagation and proactive power-system operation. We transform the enormous wildfire-propagation observation space and utilize it as part of a heuristic for proactive de-energization of transmission assets. We integrate this heuristic with a reinforcement-learning based proactive policy for controlling the generating assets. Our approach allows this controller to provide setpoints for a part of the generation fleet, while a myopic operator can determine the setpoints for the remaining set, which results in a symbiotic action. We evaluate our approach utilizing the IEEE 24-node system mapped on a hypothetical terrain. Our results show that the proposed approach can help the operator to reduce load loss during an extreme event, reduce power flow through lines that are to be de-energized, and reduce the likelihood of infeasible power-flow solutions, which would indicate violation of short-term thermal limits of transmission lines.

The increasing penetration of intermittent renewables, storage devices, and flexible loads is introducing operational challenges in distribution grids. The proper coordination and scheduling of these resources using a distributed approach is warranted, and can only be achieved through local retail markets employing transactive energy schemes. To this end, we propose a distribution-level retail market operated by a Distribution System Operator (DSO), which schedules DERs and determines the real-time distribution-level Locational Marginal Price (d-LPM). The retail market is built using a distributed Proximal Atomic Coordination (PAC) algorithm, which solves the optimal power flow model while accounting for network physics, rendering locationally and temporally varying d-LMPs. A numerical study of the market structure is carried out via simulations of the IEEE-123 node network using data from ISO-NE and Eversource in Massachusetts, US. The market performance is compared to existing retail practices, including demand response (DR) with no-export rules and net metering. The DSO-centric market increases DER utilization, permits continual market participation for DR, lowers electricity rates for customers, and eliminates the subsidies inherent to net metering programs. The resulting lower revenue stream for the DSO highlights the evolving business model of the modern utility, moving from commoditized markets towards performance-based ratemaking.

Electrical energy is a vital part of modern life, and expectations for grid resilience to allow a continuous and reliable energy supply has tremendously increased even during adverse events (e.g., Ukraine cyber-attack, Hurricane Maria). The global pandemic COVID-19 has raised the electric energy reliability risk due to potential workforce disruptions, supply chain interruptions, and increased possible cybersecurity threats. The pandemic introduces a significant degree of uncertainly to the grid operation in the presence of other extreme events like natural disasters, unprecedented outages, aging power grids, high proliferation of distributed generation, and cyber-attacks. This situation increases the need for measures for the resiliency of power grids to mitigate the impacts of the pandemic as well as simultaneous extreme events. Solutions to manage such an adverse scenario will be multi-fold: a) emergency planning and organizational support, b) following safety protocol, c) utilizing enhanced automation and sensing for situational awareness, and d) integration of advanced technologies and data points for ML-driven enhanced decision support. Enhanced digitalization and automation resulted in better network visibility at various levels, including generation, transmission, and distribution. These data or information can be utilized to take advantage of advanced machine learning techniques for automation and increased power grid resilience. In this paper, a) we review the impact of COVID-19 on power grid operations and actions taken by operators/organizations to minimize the impact of COVID-19, and b) we have presented the recently developed tool and concepts using natural language processing (NLP) in the domain of machine learning and artificial intelligence that can be used for increasing resiliency of power systems in normal and in extreme scenarios such as COVID-19 pandemics.

Increased power demands, push for economics and limited investment in grid infrastructure have led utilities to operatepower systems closer to their stability limits. Voltage instability may trigger cascade tripping, wide-area voltage collapse and powerblackouts. Real-time voltage stability monitoring possible with deployment of phasor measurement units (PMUs) is essential totake proactive control actions and minimize the impact on system. This paper presents a novel online algorithm for a) hybridperturbation analysis-based voltage stability monitoring (HPVSM), b) including Q-limit in voltage stability index and c) real timesecurity analysis using voltage stability index. HPVSM based voltage stability index is computed using the data obtained fromlinear state estimator and PMU measurements. Typically measurement-based schemes ignore the impact of generator Q-limitsand security analysis is not feasible. The proposed HPVSM based index considers the impact of generator Q-limits violationsby anticipating the critical generators using real-time PMU measurements. Contingencies are ranked using the proposed voltagestability index for security analysis. Results simulated for the 9-bus WECC, IEEE 14, 57 and 118 bus systems highlights thesuperiority of the proposed method in real time voltage stability and security analysis

Transactive energy systems (TES) are emerging as a transformative solution for the problems that distribution system operators face due to an increase in the use of distributed energy resources and rapid growth in scalability of managing active distribution system (ADS). On the one hand, these changes pose a decentralized power system control problem, requiring strategic control to maintain reliability and resiliency for the community and for the utility. On the other hand, they require robust financial markets while allowing participation from diverse prosumers. To support the computing and flexibility requirements of TES while preserving privacy and security, distributed software platforms are required. In this paper, we enable the study and analysis of security concerns by developing Transactive Energy Security Simulation Testbed (TESST), a TES testbed for simulating various cyber attacks. In this work, the testbed is used for TES simulation with centralized clearing market, highlighting weaknesses in a centralized system. Additionally, we present a blockchain enabled decentralized market solution supported by distributed computing for TES, which on one hand can alleviate some of the problems that we identify, but on the other hand, may introduce newer issues. Future study of these differing paradigms is necessary and will continue as we develop our security simulation testbed., Comment: Appeared in MSCPES 2019

Optical coherence tomography (OCT) is being increasingly adopted as a label-free and non-invasive technique for biomedical applications such as cancer and ocular disease diagnosis. Diagnostic information for these tissues is manifest in textural and geometric features of the OCT images, which are used by human expertise to interpret and triage. However, it suffers delays due to the long process of the conventional diagnostic procedure and shortage of human expertise. Here, a custom deep learning architecture, LightOCT, is proposed for the classification of OCT images into diagnostically relevant classes. LightOCT is a convolutional neural network with only two convolutional layers and a fully connected layer, but it is shown to provide excellent training and test results for diverse OCT image datasets. We show that LightOCT provides 98.9% accuracy in classifying 44 normal and 44 malignant (invasive ductal carcinoma) breast tissue volumetric OCT images. Also, >96% accuracy in classifying public datasets of ocular OCT images as normal, age-related macular degeneration and diabetic macular edema. Additionally, we show ~96% test accuracy for classifying retinal images as belonging to choroidal neovascularization, diabetic macular edema, drusen, and normal samples on a large public dataset of more than 100,000 images. The performance of the architecture is compared with transfer learning based deep neural networks. Through this, we show that LightOCT can provide significant diagnostic support for a variety of OCT images with sufficient training and minimal hyper-parameter tuning. The trained LightOCT networks for the three-classification problem will be released online to support transfer learning on other datasets.

The adsorption and desorption of carbon dioxide (CO2) molecule by alkaline earth metal (AEM) (Mg+2, Ca+2, Sr+2 and Ba+2) functionalized on graphitic boron nitride (g-B4N3) nanosheet have been analyzed by using density functional theory (DFT) approach includes long-range correlation (DFT+D). This method has been implemented in such a way to understand the switchable or capture/release mechanism of CO2 molecule by computing the electron mobility, electronic properties, charge accumulation, charge transfer (e-) and adsorption energy (Kcal/mol). The g-B4N3 nanosheet yields high carrier mobility (8020 cm2 V-1s-1) at 300 K. The positive alkaline earth adatoms on the nanosheet of g-B4N3 has been provided external energy to do the capture/release process of greenhouse gas CO2. Here, Mg positive ion work as adatom which confirms physisorption while others show chemisorption behaviors. Therefore, due to the weak absorption of CO2, it makes possible to discharge from the g-B4N3 nanosheet and shows instantaneous switching mechanism. Briefly, the negatively charged g-B4N3 nanosheets are highly sensitive for CO2., Comment: 20 pages 9 figures

Growing cybersecurity risks in the power grid require that utilities implement a variety of security mechanism (SM) composed mostly of VPNs, firewalls, or other custom security components. While they provide some protection, they might contain software vulnerabilities which can lead to a cyber-attack. In this paper, the severity of a cyber-attack has been decreased by employing a diverse set of SM that reduce repetition of a single vulnerability. This paper focuses on the allocation of diverse SM and tries to increase the security of the cyber assets located within the electronic security perimeter(ESP) of a substation. We have used a graph-based coloring game in a distributed manner to allocate diverse SM for protecting the cyber assets. The vulnerability assessment for power grid network is also analyzed using this game theoretic method. An improved, diversified SMs for worst-case scenario has been demonstrated by reaching the Nash equilibrium of graph coloring game. As a case study, we analyze the IEEE-14 and IEEE-118 bus system, observe the different distributed coloring algorithm for allocating diverse SM and calculating the overall network criticality., Comment: 8 pages, 6 tables and 8 figures