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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Transitioning to Hybrid Power Plants

Hybrid power plants are power generation systems that combine two or more types of electricity generation or storage, creating systems where the optimal dispatch provides more benefits that the sum of the individual components can. These plants exemplify the interconnected challenges of grid modernization. On one hand, rapid increase in deployment and enhanced controllability of renewable energy assets is needed to meet clean energy targets, but on the other, increasing electrification makes it more difficult to manage load and growth in the number of endpoints and stakeholders involved with hybrid plants complicates asset management and cybersecurity. This talk will cover trends in hybrid power plant technologies and deployments, identify future prospects for hybrid systems, and discuss the challenges and research opportunities that exist to transition from the traditional power grids of today to smart, distributed, hybrid power grids we may see in the next few decades.

14 SOLAR ENERGY↗
DOE OSTI2024

Hydrogen Energy Storage System at Borrego Springs Towards an H2 Enabled 100% Renewable Microgrid

San Diego Gas & Electric's Borrego Springs Microgrid is one of the largest microgrids in the USA, serving 2500 residential customers, 300 commercial and industrial customers, and has a peak demand of approximately 14 MW. This microgrid is located at the end of a long transmission line, and is subjected to extreme weather events like storms, wildfires, and flooding, which frequently cause grid outages. The microgrid currently relies on 3.65 MW of diesel-powered generators to provide grid-forming services during these outages, which results in greenhouse gas and criteria pollutant emissions. However, the microgrid has access to approximately 37 MW of installed solar generation and struggles with overgeneration and curtailment. 1.5 MW/4.5 MWh of grid-scale batteries have been installed to capture some the overgeneration and provide resiliency, but a longer-duration low-greenhouse gas emission energy storage solution is needed. In our project, the team will evaluate in the lab and demonstrate in the field a grid-forming fuel cell inverter that can provide grid-forming services while utilizing hydrogen's energy storage scalability. We will first utilize NREL's Renewable Energy Integration and Optimization (REopt) platform to perform analyses on future microgrid scenarios that use hydrogen assets to reduce or eliminate the need for diesel backup generators. We will then evaluate the grid-forming inverter through power hardware-in-the-loop and controller hardware-in-the-loop experiments at ARIES, and de-risk the field deployment and operation of hydrogen assets in the microgrid setting. Finally, this project will demonstrate the operation of the fuel cell inverter and updated microgrid controller by operating in grid-forming mode in the Borrego Springs microgrid.

blackstart with fuel cells↗
DOE OSTI2024

Architecture for Web-Based Visualization of Large-Scale Energy Domains: Preprint

With the growing penetration of inverter-based distributed energy resources and increased loads through electrification, power systems analyses are becoming more important and more complex. Moreover, these analyses increasingly involve the combination of interconnected energy domains with data that are spatially and temporally increasing in scale by orders of magnitude, surpassing the capabilities of many existing analysis and decision-support systems. We present the architectural design, development, and application of a high-resolution web-based visualization environment capable of cross-domain analysis of tens of millions of energy assets, focusing on scalability and performance. Our system supports the exploration, navigation, and analysis of large data from diverse domains such as electrical transmission and distribution systems, mobility and electric vehicle charging networks, communications networks, cyber assets, and other supporting infrastructure. We evaluate this system across multiple use cases, describing the capabilities and limitations of a web-based approach for high-resolution energy system visualizations.

grid modernization↗
DOE OSTI2022

Risk-Informed Operations and Maintenance Decision Making Using Deep Reinforcement Learning

A challenge for operating nuclear power plants is the significant cost of operations and maintenance, at times consuming up to 66% of annual operating costs. This project aims to build a framework for a risk-informed asset-management tool that integrates inspections, repairs, spare-part inventory, supply chain, and business choices to lower overall O&M costs. Our approach uses a combination of data-driven modeling and deep reinforcement learning to create and implement optimal maintenance policies for the existing nuclear fleet, as well as new advanced reactors. The creation of an asset management tool that uses these advanced methods will give operators new capabilities to help reduce the burden of O&M spending in nuclear power plants.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Improving Cyber Situational Understanding

Effective cybersecurity operations require the ability to analyze large amounts of information to assess security risks and formulate defensive strategies against adversaries. This has become more complex in recent years as the sprawl and interconnectivity of devices grows through implementation of virtualization, cloud computing, and Internet of Things (IoT). The amount of data and analysis required for effective cybersecurity command and control decisions far exceeds humans’ capacity to perform manually. We characterize the analysis problem as cyber situational understanding. The research presented to improve cyber situational understanding focuses on vulnerability analysis and threat intelligence. Regarding vulnerabilities, entities must analyze and plan work for between thousands and tens of thousands of software vulnerabilities annually. Entities heavily use network firewalls to limit vulnerability exposure. As a result, some of these vulnerabilities permit exposure to adversarial exploitation, whereas others are inaccessible and therefore present negligible risk of exploitation. Distinguishing between high and low risk software vulnerabilities requires a deep understanding of the vulnerability, network firewall protection, and characteristics of the targeted device. This problem is solved by extracting network service features from vulnerability data features using both machine-learning and natural language processing. Then, the network firewall topology is parsed to determine which vulnerabilities are reachable by adversaries. Ultimately, a state-based safety analysis ascertains which vulnerabilities are unsafe. A related vulnerability analysis problem occurs in cybersecurity operations when associating an entity’s hardware and software assets to public vulnerability databases. Assets often reveal hardware and software through installation artifacts and network service identification, and entities store these artifacts in inventory databases. However, software and hardware vendors apply a standard Common Platform Enumeration (CPE) naming convention when publicly reporting vulnerabilities. Associating these two datasets often requires many hours to days of manual inspection. The proposed solution automates the mapping approach of human analysts using fuzzy matching techniques, natural language processing, and, ultimately, machine learning to present a small set of recommendations for mapping the two datasets. The result significantly reduces human analysis time and reduces the occurrence of false positives in vulnerability notifications. Finally, cyber threat intelligence (CTI) requires associating cyber observable artifacts, such as IP addresses, URIs, and file hashes, with cyber threat tactics, techniques, and procedures. Unfortunately, most CTI data is compartmentalized across multiple organizations and cannot be shared due to the legal and reputational risk with cyber threat being associated with the entity. The approach to solving this problem inovlves using a distributed ledger with anonymous token spending and authentication. This allows a consortium of semi-trusted entities to share the workload of curating CTI for a threat sharing community’s cooperative benefit.

Huff, Philip↗
DOE OSTI2025

SURVEILLANCE DETECTION FOR TRANSPORTATION OPERATIONS PERSONNEL TO PREVENT HIJACKING, THEFT, SABOTAGE, AND MALICIOUS SECURITY EVENTS DURING TRANSPORTING NUCLEAR MATERIAL

The secure transportation of high-consequence materials, including nuclear and radiological assets, is a critical global priority in the face of escalating terrorism, security threats, and violent protests targeting these operations. Effective surveillance detection—the ability to identify, assess, and respond to potential threats across a continuum of scenarios—is paramount in addressing these challenges. This paper outlines a phased, multi-tiered training program designed to strengthen the surveillance detection capabilities of organizations responsible for nuclear material transport. The proposed training program adopts a progressive approach, gradually increasing in technical complexity to provide participants with comprehensive knowledge and tools for implementing robust security strategies. It targets a wide spectrum of stakeholders, including competent authorities, regulators, inspectors, shippers, carriers, law enforcement, and emergency response personnel, equipping them to plan, evaluate, and safeguard nuclear material transportation effectively. Each phase of the program emphasizes distinct elements of the surveillance detection continuum and transport security, focusing on critical topics such as threat identification, adversary task timelines, protective methodologies, and attack mitigation strategies. The training framework is anchored in technical exchanges and scenario-driven courses that reflect real-world complexities and challenges. By addressing the surveillance detection continuum comprehensively—from early threat assessment to active countermeasures—the program reinforces global efforts to secure nuclear assets. It aligns with international security objectives and fosters a strong security culture within participating organizations, ensuring personnel are prepared to counter potential threats and maintain the safe, secure movement of these materials. Ultimately, this initiative aims to enhance preparedness, security, and response capabilities, supporting the global mission to safeguard high-consequence materials against evolving threats.

Zineddin, Dr. Z. [ORNL] (ORCID:0009000848740725)↗
DOE OSTI2025

Capturing Historic Reliability Performance Through Graph Databases: A Model Based System Engineering Approach

With the goal of improving the performance and reliability of high dependable technological systems such as nuclear power plants, advanced monitoring and health management systems are employed to inform system engineers on observed degradation processes and anomalous behaviors of assets and components. This information is captured in the form of large amount of data which can be heterogenous in nature (e.g., numeric, textual). Such large data availability poses challenges when system engineers are required to parse and analyze them in order to track historic reliability performance of assets and components. This paper tackles directly this challenge by providing means to organize data in the form of a graph: a knowledge graph. The presented approach distinguish itself from current knowledge graph-based methods by the fact that model-based system engineering (MBSE) models are used to “put data into context”. In particular, MBSE models are used as skeleton of a knowledge graph; numeric and textual data elements, once processed, are associated to MBSE model elements. Thus, a knowledge graph captures both system architecture (though MBSE models) and health/performance data. Such feature opens the door to new data analytics methods designed to identify causal relations between observed phenomena.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

INTEGRATION OF DATA ANALYTICS WITH SYSTEM HEALTH PROGRAMS

Industry equipment reliability and asset management programs are essential elements that help ensure the safe and economical operation of nuclear power plants. The effectiveness of these programs is addressed in several industry developed and regulatory programs. However, these programs have proven to be labor intensive and expensive. There is an opportunity to significantly enhance the collection, analysis, and use of this information to provide more cost-effective plant operation. Additionally, there is an acute industry need to leverage advanced technology to reduce costs and improve operational effectiveness. The goal of this paper is to provide effective and efficient analytical methods and tools to support risk-informed decisions for the equipment reliability and asset management programs at nuclear power plants. This is accomplished by creating a direct bridge between component health/lifecycle data and decision making (e.g., maintenance scheduling and project prioritization). Here we are supporting typical system engineer decisions regarding maintenance activity scheduling and component ageing management. This is performed in a risk-informed context where herein the term “risk” is broadly constructed to include both plant reliability and economics. This framework combines data analytics tools to analyze equipment reliability data with risk-informed methods designed to support system engineer decisions (e.g., maintenance and replacement schedules, optimal maintenance posture) in a customizable workflow. A challenge is that the structure of this workflow strongly depends on the decision that needs to be made, the type of data available, and the constraints that need to be considered. Current methods are designed to provide specific answers to specific problems; however, these methods might prove to be inadequate even when problem settings slightly change (e.g., different types of requirements, additional dependencies between system reliability and economics). We tackled this challenge by designing framework in a flexible and modular fashion such that the user can assemble and customize his/her own workflow that integrates SSC economic lifecycle models (e.g., maintenance and replacement costs), system reliability models, and optimization methods.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Integrating AI Data Centers with the Power Grid

The rapid expansion of artificial intelligence (AI) has triggered an unprecedented surge in electricity demand, with US data center energy use projected to double or triple 2023 levels by 2028. This exponential growth places strain on grid infrastructure, which can hinder timely construction of desired computing capacity. To bridge this supply-demand gap, utilities and AI developers are increasingly turning to demand flexibility, a strategy that incentivizes shifting or reducing power use during peak periods of grid stress. Data centers are uniquely equipped for flexible operations due to their digital workloads, built-in redundancy, and onsite energy assets. This article outlines four primary mechanisms to enable data center flexibility: computational load flexibility (shifting tasks temporally or geographically), flexible use of core facility infrastructure adjustments, energy storage utilization, and onsite electricity generation. To encourage adoption, utilities are deploying new tariff designs, including voluntary interruptible service riders, mandated flexibility requirements, and streamlined interconnection processes for flexible loads. For the highly capitalized and rapidly growing AI industry, the primary motivators for embracing these strategies are expediting facility interconnection, satisfying emerging regulatory mandates, and mitigating community resistance. While demand flexibility cannot substitute the long-term need for new bulk power generation, it serves as an essential, immediate solution for enabling near-term deployment. By transforming data centers from grid stressors into stabilizing assets, flexible operations can ensure reliable grid integration, ease market pressures, and support a resilient power system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Equipment Self-Assessment Guide Checklist

This Equipment Self-Assessment Checklist is designed for asset owners and operators (AOOs) responsible for the deployment, operation, maintenance, or cybersecurity oversight of grid systems and digital energy technologies. It provides a structured inspection checklist for evaluating the security, integrity, and operational trustworthiness of equipment across substations, generation sites, distributed energy resources (DERs), and control environments.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
DOE OSTI2023

Data Privacy for the Grid: Toward a Data Privacy Standard for Inverter-Based and Distributed Energy Resources

The traditional approach to planning the distribution grid has focused on reliability in the context of gradual and reasonably predictable load growth. Forecasts of load growth, combined with asset management practices, were used by system planners to identify upgrades to the system to maintain or improve reliability. The decisions, typically based within load flow analysis tools, included considerations about contingency scenarios and corporate forecasts (i.e., top-down predictions at a summary level of what would happen in a particular area that could impact load growth and behavior). As a result, today, this traditional approach no longer fits all purposes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Low-cost embedded optical sensing systems for distribution transformer monitoring

There is a mounting need for low-cost monitoring with online sensing technologies to maintain grid reliability and uptime. Here we introduce an innovative low-cost, embedded optical sensing technology initially focused on transformers that was developed and demonstrated at a major electric utility, Con Edison. A version of it that can be retrofitted onto existing transformers in the field was also developed. Two new 500 kVA distribution network transformers were built with embedded fiber-optic (FO) sensors and qualified per industry standards. Vibration, temperature, and corrosion were key parameters monitored. The first transformer with embedded FO sensors was installed at a Con Edison facility and monitored at our team’s office. The second one was installed in an urban street-side underground location with online data processing/feature extraction algorithms and monitored through a wireless router. Additionally, an older transformer was also retrofitted. Data analysis was done on these transformers showing promising correlations with their corresponding loading cycles. Furthermore, key events such as the transformer primary-side energizing, and other events were detected. In general, the technology was demonstrated over 6 months across the 3 transformers instrumented with promising results. Thus, it has the potential to enable predictive maintenance for transformers and other grid assets.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Hydrogen Based Energy Storage System for Integration with Dispatchable Power Generator (Phase I Feasibility Study)

This project examined the feasibility of integrating hydrogen generation, storage, and use as a means to decarbonize campus activities while retaining the ability to utilize the existing natural gas fired combined heat and power system installed at the campus of the University of California @ Irvine. Analysis of specific potential sites for the integrated system identified a location adjacent to the existing central plant which resulted in minimization of interconnections. A strategy based on use of commercial electrolyzers and gas storage was identified. Primary technology advancements are required for the gas turbine to accommodate higher levels of hydrogen and the integrated controls. The project indicated challenges for adopting the proposed strategy with the present rates and constraints. The availability of a relatively low-cost biogas resource by the campus already decarbonizes the gas turbine to some extent. In the absence of this resource, procurement of electricity directly from large scale renewable operations could facilitate lower electricity costs. Additional solar resources on campus could also help in this regard. The gas turbine cannot be operated below 50% capacity due to air permit constraints. Using the otherwise curtailed gas turbine operation to generate hydrogen via electrolysis by consuming natural gas is not highly efficient and therefore leads to relatively high costs of electricity returned. Several scenarios demonstrate potential for effective decarbonization, yet most involve lower and lower capacity factor for the legacy gas turbine which is not a good use of the asset. A small gas turbine output with higher efficiency operation would help. As would ability to export electricity to the grid. Certainly current rate structures and operational scenarios are less attractive than other possible future structures which should be pushed for in the future.

03 NATURAL GAS↗
DOE OSTI2022

Draft Methodology for Cybersecurity Analysis for Adoption of Wireless Technology in Nuclear Power Plants

The report is delivered as DOE-NE Cybersecurity Program Milestone M2CT-22IN1104014-Industry cybersecurity guidance adoption status. The milestone is specifically to document the process and the status of the efforts towards developing guidance in support of industry’s adoption of wireless communication technologies. To transform the operation of nuclear power plants, including domestic light-water reactors, advanced reactors, microreactors, and fission battery, for grid and non-grid applications, secure and resilient wireless capabilities are required. In addition, to realize new operational concepts such as autonomous operation and remote monitoring, advanced sensor, and instrumentation, wireless technology is essential. However, industry implementation is low, and there is no technical basis for how to understand and address potential risks for wireless communications for critical plant functions. A methodology with a technical basis for implementing secure wireless communication is crucial. This wireless adoption methodology is intended to assist a licensee in identifying an appropriate technological approach for securing wireless communications in nuclear power plant. However, this methodology does not provide guidance for addressing wireless design criteria or for addressing all the cybersecurity commitments in licensees’ cybersecurity plans (CSPs) including addressing security impact analysis that a licensee must perform before an update to a Critical Digital Asset (CDA). This methodology guides a licensee through a process of evaluating a proposed wireless implementation and drafting a plant change notification for an example use case. The example is a generic/nonproprietary version of the first test case of the methodology, which, if successfully implemented, is part of the consideration for protecting other functions’ use of wireless in a cybersecure manner.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Connecting Minds: AI Use Cases to Bridge Power Systems and Large Language Models for Practical Applications

Recent advances in artificial intelligence (AI) and development of large language models (LLMs) present the opportunity to develop a new generation of power systems applications. In contrast with early power system AI applications based on structured numerical data, LLMs offer unique capabilities to perform logical reasoning using text documents, unstructured data, and application programming interface (API) calls to computational software. This paper seeks to bridge the knowledge gap between power systems engineers and LLM developers through a crosscutting explanation of use cases, characteristics, requirements, practical considerations from the perspectives of both LLM capabilities and industry needs. Specific focus is given to applications that can be realistically deployed by electric utilities. After introducing the architecture of LLMs and unique challenges of the power systems domain, this paper proposes twenty representative LLM applications grouped into categories of 1) power system operations, 2) asset management, 3) system planning and analytics, and 4) energy management and protection systems. Five use cases are presented within each category with descriptions of the motivation, objectives, approaches, example inputs / outputs, and benefits of each use case.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

CIE Analysis Process for Engineered Systems

"CIE Analysis Process for Engineered Systems" outlines a comprehensive methodology for integrating Cyber-Informed Engineering (CIE) principles into both new and existing engineered systems. Sponsored by the U.S. Department of Energy’s Office of Cybersecurity, Energy Security, and Emergency Response (DOE CESER), the process aims to achieve cyber-informed decisions by producing functional security requirements for new systems and retrofitting existing systems to mitigate digital risks. The document details a step-by-step approach, including mission and function definition, digital asset awareness, consequence analysis, and mitigation analysis. It emphasizes the importance of documenting mechanical, electrical, programmable, and network components to protect system functions and provides examples and considerations for each step. The ultimate goal is to ensure that engineered systems remain resilient against cyber threats, maintaining safety, performance, and reliability.

42 - ENGINEERING↗
DOE OSTI2021

Case Study: The Tower Companies' Commercial Office Solar PV

This case study describes how the Tower Companies selected and successfully executed onsite solar photovoltaic installations at its commercial office properties and highlights the resulting increase in asset value.

solar, solar PV, photovoltaic, onsite solar, comme↗
DOE OSTI2022

Simulating Impacts of Extreme Events on Grids with High Penetrations of Wind Power Resources

As extreme weather events become more frequent and intense, the demand for connecting grid operation and infrastructure planning with extreme event models will increase as well. We present a methodology for creating damage contingencies and scenarios for electric transmission grids during a hurricane strike. Included is an example case study: Hurricane Dolly damaging a synthetic 2000 bus test system during its landing in Southern Texas. Using WIND Toolkit meteorological data in conjunction with fragility curves for various electric grid elements, we generate stochastic damage scenarios that can be used for short- and long-term planning problems including emergency asset management. We perform statistical analysis of damages and quantify topological effects on example synthetic grid. Also, we investigate loss-of-load events during two-day economic dispatch experiment. Finally, we point out various shortcomings of our method and suggest how it can be improved.

data-driven forecasting↗