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NASA NTRS2022

Landslide Likelihood Prediction using Machine Learning Algorithms

The supply of electricity via power plants is criticalto the operation of many critical infrastructure systems in mod-ern society. Natural hazards can disrupt the power supply, causepower outages that can halt economic growth, and impede emer-gency response until power is restored. The proposed work aimsto predict the landslides likelihood in these critical infrastructurelocations in the Northeastern USA using integrated databases ofexplanatory variables and machine learning algorithms. First,data related to landslides are obtained and merged, includingtopographic, soil moisture, and precipitation-related data. Fiveregression algorithms, namely: Random Forest, Extreme Gradi-ent Boosting (XGBoost), K-Nearest Neighbor regression (KNN),Linear Support Vector Regressor (SVR), and Linear regression,are utilized to predict the landslide probability and evaluatedon the dataset. The accuracy of the models is assessed by usingstatistical metrics such as mean absolute error (MAE), meansquared error (MSE), and root mean squared error (RMSE).The study results show that Random Forest outperformed othermodels with the mutual information feature selection method.It achieved an MSE of 0.0011 with mutual information-basedfeature selection and an MSE of 0.00157 without feature selection.KNN regressor outperformed the other models with an MSEof 0.00139 with correlation-based information selection. Theproposed landslide identification model with Random Forestalgorithm shows outstanding robustness and great potential intackling the landslide likelihood prediction by employing MLalgorithms.

Vasundhara Acharya↗

DOE OSTI2023

Enabling Cybersecurity, Situational Awareness and Resilience in Distribution Grids with High Penetration of Photovoltaics (CARE-PV) (Final Report)

Since legacy distribution systems have very limited visibility beyond the substation, high penetration of PV at the grid edge presents some unique operational challenges. One approach to address these challenges is to use information from advanced metering infrastructure (AMI) and µPMUs. However, exploiting this information is impacted by a number of factors, including multi-timescale measurements, volume of data generated, communication network impairments (e.g., information loss and latency) and susceptibility to cyber-attacks. Therefore, one of the critical tasks involved in the management of a distribution grid is to develop complete situational awareness by integrating cyber-security mechanisms with state estimation strategies and leveraging this situational awareness to assure energy services at strategic locations while exploiting AMI/PV inverter/ µPMU data. This CARE-PV project addresses the fundamental challenges in situational awareness and resilience to cyber and physical vectors by exploiting the synergy between innovative modeling, estimation, data analytics, testing and validation using smart PV inverters designed at K-State and facilities at NREL. Specifically, the project involved the development, testing and validation of the following novel enabling technologies: (Thrust 1) Resilience to cyber vectors that impact data integrity was addressed via a two-level defense strategy that combines cyber intrusion detection using self-learning, cooperative smart PV inverters, and a novel moving target defense framework to combat data integrity attacks. (Thrust 2) Resilience to cyber-physical vectors that impact situational awareness by limiting data availability was addressed via novel centralized and decentralized, sparsity-based static and dynamic state estimation approaches that enhance observability even when the underlying system is unobservable. (Thrust 3) Leveraging a unique probabilistic sensitivity analysis approach accompanied by one-of-a-kind dominant influencer set computation, the vulnerability of critical infrastructure at strategic locations was evaluated so that proactive PV-based control strategies can be used to support operations under normal/outage scenarios. These CARE-PV project innovations were demonstrated on both small-scale IEEE and larger utility-scale testbeds (Thrust 4). Feedback from Industry Advisory Board members was used to formulate a commercialization pathway for a subset of CARE-PV technologies. These CARE-PV technologies will ultimately lead to reliable and secure, large-scale integration of renewable energy and mitigate the risk of energy disruption resulting from cyber incidents and other emerging threats within the energy environment.

14 SOLAR ENERGY↗

DOE OSTI2023

Dynamics of Water, Climate, and Infrastructure

Climate and its impacts on the natural environment, and on the ability of the natural environment to support population and the built environment, stands as a threat multiplier that impacts national and global security. The Water Intersections with Climate Systems Security (WICSS) Strategic Initiative is designed to improve understanding of water’s role in, among other topics, the connection of critical infrastructure to climate in light of competing national and global security interests (including transboundary issues and stability), and identifying research gaps aligned with Sandia, and Federal agency priorities. With this impetus in mind, the WICSS Strategic Initiative team conceptualized a causal loop diagram (CLD) of the relationship between and among climate, the natural environment, population, and the built environment, with an understanding that any such regionally focused system must have externalities that influence the system from beyond its’ control, and metrics for better understanding the consequences of the set of interactions. These are discussed in light of a series of worldviews that focus on portions of the overall systems relationship. The relationships are described and documented in detail. A set of reinforcing and balancing loops are then highlighted within the context of the model. Finally, forward-looking actions are highlighted to describe how this conceptual model can be turned into modeling to address multiple problems described under the purview of the Strategic Initiative.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Analyzing Infrastructure Interdependencies Using Network-Of-Networks Modeling

Infrastructure networks play a crucial role in our day-to-day lives, and modeling these infrastructure networks can help decisionmakers prepare for and respond to disruptions such as natural disasters or cyberattacks. Because these infrastructure networks depend on each other, it is not sufficient to model a single network in isolation. We build on previous single-network-modeling techniques to develop a methodology for modeling infrastructure interdependencies as a Network-of-Networks. Using distributionlevel data from a real U.S. city on the power grid, road geometry, and hospital locations, we show how to apply this methodology to modeling three of the U.S. Department of Homeland Security's Critical Infrastructure Sectors: Healthcare, Transportation, and Energy. We also analyze three primary metrics before and after a simulated disaster: 1) impact on hospital access; 2) road network impact with the change in betweenness centrality; 3) electric customer outage. We simulate three different disruptions: 1) road flooding from nearby rivers; 2) a malicious actor targeting the road networks; 3) a malicious actor targeting the electric grid. Finally, we discuss how our methodology can be applied to additional infrastructure networks and types of disruption, and how Artificial Intelligence (AI) techniques may be incorporated into this methodology for further research.

Scherrer, Evan [Drake University, Iowa]↗

DOE OSTI2021

Power System Resilience Metrics Augmentation for Critical Load Prioritization

One of the major goals of new grid operation regimes, such as transactive energy systems (TESs), is to make the power grid more resilient to withstand natural or man-made disasters and potential reliability events, and to continue to serve the maximum number of its customers. But it is a well-known fact to system operators that not all customers are the same. This implies that any discussion of TESs’ impacts on the resilience of the power system should consider the needs of its critical customers (such as the power system operation centers, fire and police stations, and hospitals) over those of other customers. When evaluating the resilience of the system, bonus points must be awarded to any system that could maintain its power supply to critical customers during a disturbance that may cause an outage. This report discusses critical infrastructure (CI) as found in the literature and then categorizes it based on the field to which the operations belong (such as human life/safety-related, operations management, necessary city operation, industrial customers, etc.). Each of these CI categories is further divided into types of critical customers (e.g., the human life/safety-related category has different types of customers like hospitals, fire and police stations, etc.). The entire demand of each of the critical customer types is not categorized as critical load (CL); instead, only a portion of the total load of these critical customers is characterized as critical load. This is done based on the categories of equipment, the function of which is crucial in the operation of the overall facility. CL categorization is performed to provide the ratio of the critical load portion to the overall load , so that it can serve as a parameter in the resilience evaluation of the grid through a metrics-based approach. Such categorization is important as it helps to augment the existing quantifiable resilience metrics with CL categorization. The metrics for a power system need to not only consider how well a system performed during a disturbance event, but also how it reduced strain and supplied power to its CLs. The first step in this process is characterize CLs in the system. After CL characterization, the next step is the inclusion of these loads in the resilience metrics. To that end, in this report weight-based augmentation of resilience metrics is proposed, where certain customers (the ones that are categorized as critical) are assigned higher weights than others. Though an overview of assigning weights to customers is discussed, there is no one-size-fits-all approach for every power system. The decisions made about assigning such weights to customers vary greatly from one operator to another, based on their unique systems and the current and predicted states of critical customers. This decision-making can include the type of disturbance event, which might only affect certain parts of the system. In general, analyzing critical customers before an event helps understand system vulnerabilities. It also helps in planning and conducting operations during the event, evaluating system performance after the event, and supporting better planning for future events. An alternative to the current practices of managing the grid for outages is an innovative TES, which has the potential to provide a platform for including distributed energy resources for managing CLs. This report also describes how TES qualities can help (1) to maintain power supply to critical customers for uninterrupted operations and (2) to restore lost power supply to the critical customers rapidly.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Hawaii Threat Brief

The Digital Energy Transformation is redefining how power systems operate, integrating physical infrastructure with digital technologies to enhance efficiency, visibility, and resilience. For islanded and digitally modernizing systems like Hawai‘i’s, this shift presents both new opportunities and evolving challenges in cybersecurity, supply chain assurance, and environmental resilience. This brief provides an overview of critical infrastructure dependencies and systemic risks associated with increasing digital integration. It summarizes recent energy-sector threat activity and case studies that illustrate adversary tactics and supply chain vulnerabilities, and identifies pathways to strengthen resilience.

25 - ENERGY STORAGE↗

DOE OSTI2024

A Visual Analytic Platform for Interactive Validation of Human Mobility Simulations

Human mobility insights guide domain experts in an array of decisions, including critical infrastructure design, disaster response, epidemic modeling, national security, and policy making. Due to the inherent noise and privacy concerns in real-world individual-level mobility data, it is often preferred to leverage simulators that generate synthetic mobility data instead. However, it is critical to inspect and validate the output of such simulators to ensure the synthetic data is aligned with the characteristics of the population and the area of interest known to domain experts. While there exist many quantitative approaches for validating synthetic data, we argue it is also important to also validate such data qualitatively to capture aspects that are known to domain experts but difficult to quantify. In this work, we demonstrate a visual analytic platform that empowers domain experts to interact with their simulation outputs along spatial and temporal dimensions. By augmenting automated techniques and human skills, our visual analytic platform is a step towards interactive capabilities for model steering and quality control of mobility simulators.

Monadjemi, Shayan↗

NASA NTRS2022

Preparing the Mars Relay Network for the Arrival of the Perseverance Rover at Mars

The Perseverance rover represents NASA’s latest achievement in Mars exploration. Landing successfully on 18 Feb 2021, the rover’s transmitted data during its entry, descent, and landing (EDL) were captured by the Mars Reconnaissance Orbiter (MRO) and the Mars Atmosphere and Volatile Evolution (MAVEN) orbiter. This data, broadcast in near-realtime to the world, allowed everyone to share in the excitement (and “terror”) of the day. The images returned thereafter included the first images of the new landing site, video of the landing itself taken from a variety of vantage points, and eventually the historic images of the first powered flight on another planet. Behind the scenes, the return of that data to Earth was accomplished via Mars orbiters operated by NASA and ESA, using three different ground tracking networks. Considered together, this Mars Relay Network (MRN) enabled the successful, timely, and unobtrusive return of the rover’s data. This paper describes the preparations taken by the participants of the MRN in anticipation of the arrival of Perseverance at Mars. These were not only focused on successfully acquiring the rover’s critical event telemetry during its EDL, but also on readying the network to return the rover’s data on an ongoing basis as it pursued its mission objectives. Included is a brief description of the MRN, which represents a highly successful international collaboration and continues as critical infrastructure for NASA’s and ESA’s ongoing Mars exploration. Also summarized are the activities performed prior to EDL, including landing site reconnaissance and mission test and training activities; those activities performed on EDL day, especially the recording, return, and processing of the rover’s critical event telemetry; and those activities that are now being performed on an ongoing basis during the rover’s surface operations, including an outline of the planning processes that enable relay services. Finally, a description of the performance of the network to-date on behalf of the Perseverance rover is given, summarizing the success of the network to provide support to both it and other spacecraft on the surface of Mars.

Srnka, Evan↗

DOE OSTI2023

Autonomous Intelligence Measurements and Sensor Systems (AIMS): Gaussian Processes in Remote Sensing: Literature Review

Power grid resilience and reliability is crucial to supporting critical infrastructure. Service interruptions can have devastating consequences to communication, emergency, and transportation services, just to name a few. In addition to infrastructure, power services are crucial to everyday life. Nearly every element of modern-day living relies on a stable and functioning power grid - heating and cooling systems to lighting, refrigeration, telecommunications, and internet access. Interruptions in the power supply can range from minor inconveniences, such as the temporary loss of internet or television services, to more significant problems, like the inability to access medical equipment or emergency services during critical situations. As climate conditions worsen, the reliability of the power grid becomes even more significant. Extreme weather events, such as hurricanes, wildfires, or severe storms, can cause serious damage to power infrastructure, leading to prolonged power outages. Rising temperatures and changing weather patterns can strain the grid, resulting in increased demand for electricity, stressing transmission and distribution systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Domestic Extremism (Executive Summary)

Domestic extremism (DE) has been a growing concern in the U.S. in recent months, as illustrated in multiple bulletins from the Department of Homeland Security (DHS) warning law enforcement partners of the heightened threat. As concerns about these actors grows, it is important that facilities in the U.S. and internationally that protect critical assets, such as sensitive information, hazardous materials, or critical infrastructure, have effective methods in place to secure those assets. DE has challenged security systems through the danger of insider attack and violence, creating a new threat to be countered. In this effort, therefore, we used a literature review and focus group discussions with experts in critical asset security and extremism to understand the nature of the domestic extremist threat, to identify best practices in securing assets, recognize potential gaps in security measures to be corrected, and recommend actions for the Office of Radiological Security (ORS) to address DE with its partners.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2026

PopGNN: Graph Neural Network-Based Flexible Future Population Forecasting Model

Accurate population forecasts is important to plan critical infrastructure and services, from housing and education to healthcare and transport. However, traditional population prediction studies have only employed traditional machine learning models limited to capture complex spatial interdependencies and patterns. Althogh recently computer vision-based framework was introduced with with promising accuracy, it has critical limitations for real-world planning applications: it function only at fixed spatial resolutions, restricting their use in diverse boundaries such as census tracts, neighborhoods, or administrative zones. Therefore, this study suggests a Graph Neural Network (GNN)-based population prediction framework, called PopGNN. This model recorded remarkable performance compared with state-of-the-art models and traditional baseline models in the grid and administrative boundaries. Furthermore, our framework achieved comparable predictive accuracy to a computer vision-based model in both the South Korea and Tennessee case studies. Consequently, this study is valuable in that a single model can provide accurate population forecasts that address diverse planning demands, ranging from granular grid-level estimates for precise service allocation and facility location planning to aggregate administrative-level forecasts for macro-scale regional policy and resource distribution.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Coupling of the Electricity and Transportation Sectors - Part I: Sector Overviews

“Sector coupling” is a concept that addresses potential designs for the future power industry. Traditionally, the energy sectors, i.e., electricity supply, transport, and industry, have functioned largely independently from one another. Thus, the concept of sector coupling means that the electricity sector would become the central pillar of the energy system, supplying the other sectors – transportation and industry – with energy in various ways, a scheme described by the term “Power-to-X”. This report delves into the ambitious goal set by the United States to achieve net-zero emissions by 2050, with a pivotal milestone of making half of all passenger vehicles sold in America zero-emission by 2030. Central to achieving this objective is the electrification of the transportation sector, resulting in complex interactions between this sector and the electric industry. As electric vehicles become more prevalent, a shift in the dependency on electricity for vehicle charging emerges, alongside a reduction in the electricity demand associated with maintaining the fossil fuel supply chain. Both the electricity and fossil fuel have been identified as critical infrastructure sectors, the transportation sector's historical reliance on fossil fuels, and the mature supply chain supporting them are explored. This study emphasizes the existing sectoral coupling between the electric energy and transportation sectors, extending beyond vehicle charging to essential operations at refineries, pipeline facilities, storage, and fuel extraction. Recognizing the critical dependency of fossil fuel supply chain operations on the electric sector, this research underscores the need for a comprehensive view of the evolving dynamics between the transportation and electric sectors. This study has been split into two segments, Part-I provides an overview of the existing fossil fuel supply chain architecture and the electrical sector. We provide an in-depth analysis of the grid-transportation sectoral coupling, its interactions, the vulnerabilities and essential take-aways that would help us to address them and apply towards an electrified transportation sector in the future. We engaged with stakeholders to understand their expectations in enabling an electrified transportation. We accumulate the understandings from the current fossil fuel-based system in developing a risk matrix, by identifying all the threats and vulnerabilities and how would they apply to both the current and future transportation systems in Part-II of this report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Autonomous Tools for Attack Surface Reduction (Final Report)

The electric power grid is a complex critical infrastructure that forms the lifeline of modern society, and its secure and reliable operation is of paramount importance to national security and economic wellbeing. However, recent findings documented in authoritative sources indicate the threat of cyber-based attacks growing in numbers and sophistication. However, securing the grid against stealthy cyberattacks is a challenging task due to legacy nature of the infrastructure coupled with dynamic nature of threat landscape and ever-growing sophistication of the adversaries. Additionally, the grid’s attack surface continues to grow with the increased dependence on digital communications and control that now extends to each consumer through smart meters and distributed energy resources. Unfortunately, this expansive surface increases the grid’s vulnerability and further exposes critical control systems in both substations and control centers. To respond to this emerging need, we had successfully assembled an interdisciplinary team with academic- industry partnership to successfully conduct research, development, evaluation, demonstration, and commercialization of attack surface reduction tools, whose goal was to significantly reduce the cyber attack surface in the North American power grid. Our proposed project was a synergistic collaborative effort leveraging the synergistic expertise of the team members across power systems, cyber security and CPS security, testbeds, field deployments and demonstration, and successful commercialization. The following are the specific tasks that have been successfully completed two phases (2016-2020). Phase I: Task 1: Developed and implemented a robust Project Management and Data Management Plan, coupled with a well thought out Risk Mitigation Plan. Task 2.1: Developed a comprehensive framework that continually assesses and autonomously reduces the attack surface for the power grid control environment spanning across substations, control center and the SCADA network to significantly reduce the risks of cyber attacks. Task 2.2: Developed attack surface analysis techniques, metrics, and tools that assess the attack surface at multiple levels including the control center, substations, and the SCADA network. Task 2.3: Developed attack surface reduction techniques and tools that dynamically reduce attack surface and hence increase attacker’s cost without interfering in the critical functions of the system. Task 2.4: Prototyped, implemented, and quantitatively evaluated/validated the techniques and tools on a realistic industrial CPS security testbed environment by leveraging the unique resources of the team. Task 3: Developed Commercialization plan to transition the developed tools into power system industry stakeholders for a broader adoption by leveraging the expertise of our industrial members. Phase II: Task 4: Successfully completed field demonstration, verification, and evaluation of the effectiveness of the attack surface analysis and reduction techniques on a realistic utility testbed environment. This also involved the development of realistic scenarios, sound metrics, data sets, evaluation criteria, and documentation. Technology integration & Field demonstration: The project had significantly advanced the state-of-the-art research and practice in improving the cybersecurity of our nation’s power grid infrastructure against cyber threats. In particular, the proposed, designed, and deployed attack surface analysis and reduction algorithms and tools have contributed to significantly reducing the exposure and risk of the devices, substations, and the integrated SCADA/EMS/ DMS grid environment to cyber threat. Strong demonstration and evaluation techniques have verified the feasibility of the developed techniques on realistic cyber-physical testbeds and utility partner's real grid environment, and collaborative research and evaluation of attack surface reduction techniques (for wide-are monitoring and control) within a vendor (GE) EMS platform. The Attack Host Analyzer (AHA) tool that was developed through this project was made available through GitHub.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

TASTI-GRID: a holistic view of Florida's grid resilience opportunities

In December of 2025, the American Society of Civil Engineers updated Florida’s infrastructure grade to a ”C+” – reflecting overall investments in recent years. With the recommendation to further strengthen the electric grid and establish consistent building standards, extreme weather, aging infrastructure, and population in-migration are still compounding Florida’s grid vulnerabilities. Key challenges related to hurricane and other tropical & marine weather events are not unique to Florida. However, the state’s topography, location, demographics, and variation among rural and urban centers for tourism and industry, demonstrate the need for unique approaches to advancing critical infrastructure resilience, particularly for large concentrations of elderly residents or in areas of historic underinvestment. Florida’s grid asset advancements are credited with a reduction in average power outage durations. Yet, improvements since 2021 have set the stage for future resilience activities – as modernization efforts have greatly improved data collection – enabling better understanding of vulnerabilities and trends across counties and localities (particularly in Central Florida).

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Domestic Extremism: Countering the Threat Posed to Critical Assets

Domestic extremism has been a growing concern in the United States in recent months, as illustrated in multiple bulletins from the Department of Homeland Security (DHS) warning law enforcement partners of the heightened threat. As concerns about these actors grows, it is important that facilities in the U.S. and internationally that protect critical assets, such as sensitive information, hazardous materials, or critical infrastructure, have effective methods in place to secure those assets. DE has challenged security systems through the threat of insider attack and violence, creating a new threat to be countered in the Office of Radiological Security’s radiological source security mission. In this effort, we used a literature review and focus group discussions with experts in critical asset security and extremism to understand the nature of the domestic extremist threat, to identify best practices in securing assets, recognize potential gaps in security measures to be corrected, and recommend actions and next steps. Twenty-two subject matter experts participated in a series of five focus group sessions. Questions focused on definitions of domestic extremism, potential changes in the threat, best practices in securing facilities, assets, and personnel, and any perceived gaps. Upon completion of the focus groups, notes were analyzed thematically to identify any recurring patterns in the results. In addition, a review of academic, industry, and government literature was conducted to understand the threat, describe the process of radicalization to extremism, and to identify empirically informed practices in prevention and response. Results of this project demonstrated that further work is needed to define domestic extremism in law, regulation, and policy, to help the U.S. develop a consistent response to the threat within organizations. This is especially important, as SMEs emphasized the need for early intervention in prevention efforts, noting that organizations need clear guidance on when and how to intervene. In addition, the need for social media monitoring was discussed, although challenges remain to do so with appropriate respect for privacy and civil liberties concerns.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2023

A Transductive Graph Neural Network learning for Grid Resilience Analysis

Power grids are critical infrastructures that require robust resilience analysis to ensure reliable and uninterrupted electricity supply. Traditional simulation-based methods for grid resilience analysis suffer from computational complexity and limited ability to capture the full spectrum of potential disruptions. This paper presents a novel approach to enhance grid resilience by leveraging transductive graph neural network (GNN) learning to identify critical nodes and links. By leveraging the graph structure and system features, GNNs effectively learn resilience metrics and accurately identify critical nodes based on actual grid operational behavior. The efficacy of the proposed approach is demonstrated through case studies on node criticality scoring and critical node/line identification in cascading outage scenarios. The results highlight the advantages of learning-based methods over traditional simulation-based approaches and their potential to revolutionize grid resilience analysis. The contributions of this paper include a graph-based scalable approach for fast cascading analysis, an inductive formulation for training GNN models, and a transfer learning-based approach to scale the model to largescale power systems.

grid resilience, graph neural networks, transducti↗

DOE OSTI2020

Autonomous Tools for Attack Surface Reduction

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Cyber Resilience and Social Equity: Twin Pillars of a Sustainable Energy Future

This paper examines the intersection of security and accessibility within energy systems amidst the rise of grid modernization and digitization, especially considering the regulatory changes and the imperatives of inclusive energy strategies. It addresses the dual need for secure, resilient infrastructure and a commitment to mitigate energy poverty while maintaining equitable access to energy. Amid escalating cybersecurity and physical threats, the paper advocates for sustainable energy delivery systems that ensure robust defenses without compromising the goals of reducing energy poverty and ensuring energy security. This paper identifies the pressing need for Cyber-Informed Engineering (CIE) and Secure-by-Design (SbD) principles, highlighting how these strategies can protect critical infrastructure and democratize access to secure energy, particularly for disadvantaged communities. The analysis underscores the challenges presented by the expansion of attack surfaces, interoperability requirements, and grid-edge analytics, offering innovative solutions that leverage advanced technologies and data-driven insights. Furthermore, this paper addresses the workforce development gap, emphasizing the necessity for public-private partnerships and vendor engagement in creating a skilled cybersecurity workforce. This paper has a dual focus on both the technological aspect of cybersecurity and the social dimension of equity within the context of sustainable energy development. It suggests a comprehensive examination of how these two critical elements interact and support the overarching goal of a sustainable energy future.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗