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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Grid-Interactive Efficient Buildings Support Federal Facility Decarbonization

The Federal Energy Management Program (FEMP) plays a key role in helping agencies understand and meet energy-related goals, including those surrounding smart buildings technologies and grid-interactive efficient buildings (GEBs), which can support decarbonization. GEB enables demand flexibility that has the potential to reduce electrical infrastructure costs and transform the grid edge, where buildings connect to the power grid.

decarbonization↗
DOE OSTI2024

Total Power Factor Smart Contract with Cyber Grid Guard Using Distributed Ledger Technology for Electrical Utility Grid with Customer-Owned Wind Farm

In modern electrical grids, the numbers of customer-owned distributed energy resources (DERs) have increased, and consequently, so have the numbers of points of common coupling (PCC) between the electrical grid and customer-owned DERs. The disruptive operation of and out-of-tolerance outputs from DERs, especially owned DERs, present a risk to power system operations. A common protective measure is to use relays located at the PCC to isolate poorly behaving or out-of-tolerance DERs from the grid. Ensuring the integrity of the data from these relays at the PCC is vital, and blockchain technology could enhance the security of modern electrical grids by providing an accurate means to translate operational constraints into actions/commands for relays. This study demonstrates an advanced power system application solution using distributed ledger technology (DLT) with smart contracts to manage the relay operation at the PCC. The smart contract defines the allowable total power factor (TPF) of the DER output, and the terms of the smart contract are implemented using DLT with a Cyber Grid Guard (CGG) system for a customer-owned DER (wind farm). This article presents flowcharts for the TPF smart contract implemented by the CGG using DLT. The test scenarios were implemented using a real-time simulator containing a CGG system and relay in-the-loop. The data collected from the CGG system were used to execute the TPF smart contract. The desired TPF limits on the grid-side were between +0.9 and +1.0, and the operation of the breakers in the electrical grid and DER sides was controlled by the relay consistent with the provisions of the smart contract. The events from the real-time simulator, CGG, and relay showed a successful implementation of the TPF smart contract with CGG using DLT, proving the efficacy of this approach in general for implementing electrical grid applications for utilities with connections to customer-owned DERs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Voltage Service Limits Smart Contract Using Distributed Ledger Technology for Electrical Utility Grid with Customer-Owned Generator

Modern electrical grids face growing stability risks from customer-owned generators, especially at points of common couplings (PCCs). Disruptive behavior from power-electronic sources can cause protective relays to isolate problematic generators, making measurement integrity critical. This article presents a distributed ledger technology (DLT) approach that uses smart contracts to evaluate PCC voltage measurements and trigger backup breaker operations. The approach is framed as a verifiable, multi-organization attestation and audit layer, not as a real-time control security mechanism. In the proposed architecture, voltage measurements from a hardware protective relay are anchored on a DLT through the Cyber Grid Guard (CGG) system for attestation by both the grid utility and customer-owned generator. A Voltage Service Limits (VSLs) smart contract evaluates the on-chain measurements against allowable phase-voltage limits derived from the ANSI C84.1 standard. The framework is validated in a hardware-relay-in-the-loop test bed under sustained-undervoltage, sustained-overvoltage, and transient line-to-line fault scenarios. The results show that the VSL smart contract can process these measurements and issue backup breaker actions consistent with the defined service-limit criteria, demonstrating the DLT potential as a verifiable audit layer at the PCC that complements primary protection.

cyber security↗
DOE OSTI2024

Toward Wireless Smart Grid Communications: An Evaluation of Protocol Latencies in an Open-Source 5G Testbed

Fifth-generation networks promise wide availability of wireless communication with inherent security features. The 5G standards also outline access for different applications requiring low latency, machine-to-machine communication, or mobile broadband. These networks can be advantageous to numerous applications that require widespread and diverse communications. One such application is found in smart grids. Smart grid networks, and Operational Technology (OT) networks in general, utilize a variety of communication protocols for low-latency control, data monitoring, and reporting at every level. Transitioning these network communications from wired Wide Area Networks (WANs) to wireless communication through 5G can provide additional benefits to their security and network configurability. However, introducing these wireless capabilities may also result in a degradation of network latency. In this paper, we propose utilizing 5G for smart grid communications, and we evaluate the latency impacts of encapsulating GOOSE, Modbus, and DNP3 for transmission over a 5G network. The OpenAirInterface open-source library is utilized to deploy an in-lab 5G Core Network and gNB for testing with off-the-shelf User Equipment (UE). This creates an effective 5G test platform for experimenting with different OT protocols such as GOOSE. The results are validated by measuring two different Intelligent Electronic Devices’ contact closure times for each network configuration. These tests are also conducted for varying packet sizes in order to isolate different sources of network latency. Our study outlines the latency impact of communication over 5G for time-critical and non-critical applications regarding their transition toward private 5G-based OT network implementations. The conducted experiments illustrate that in the case of GOOSE packets, simple encapsulation may exceed the protocol’s time-critical nature, and, therefore, additional measures must be taken to ensure a viable transition of GOOSE to 5G services. However, non-critical applications are shown to be viable for migration to 5G.

42 ENGINEERING↗
DOE OSTI2025

Unsupervised anomaly clustering via offset alignment in multivariate grid sensing data

Modern industries increasingly rely on multi-sensor technologies to acquire complex, high-dimensional data streams, enabling advanced monitoring and control systems. One critical application is online anomaly detection in electrical smart grids, where multivariate and multimodal sensing technologies play a vital role. However, detecting anomalies in such time-series data is challenging due to their inherent temporal dependencies and stochastic behavior. Traditional approaches based on supervised and semi-supervised learning methods depend on labeled datasets, which are often unavailable in real-world scenarios. While unsupervised methods have emerged as promising alternatives, these methods are highly susceptible to noise and outliers commonly present in sensing applications. Furthermore, deep learning-based anomaly detection methods, despite their performance, are often criticized for their black-box nature, limiting their applicability in safety-critical and online environments where interpretability and explainability are paramount. In this work, we propose an unsupervised anomaly clustering method leveraging a cyclic alignment-based offset detection algorithm for multivariate time-series signals. The proposed method is applied to multivariate data collected from vibrational, voltage, and magnetic field sensors deployed in a local grid substation. Our results demonstrate the robustness of the algorithm in accurately clustering various anomalies/events across different sensing modalities. Additionally, we compare the effectiveness of the proposed approach against a simple pattern-based anomaly detection method, which performs well for univariate data but fails to generalize to multivariate and multimodal time-series data.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2025

Smart Contracts for Power Grid Applications Using the Advanced DLT Cyber Grid Guard Testbed

In this study is presented two power system applications with distributed ledger technology (DLT) and smart contracts (SC) that were assessed in a Cyber-Grid-Guard System (CGGS) advanced testbed, with protective relays, power meters, communication devices, DLT devices, synchronized time source, clock displays and real time simulator. This CGGS testbed was set in the Advanced Protection Lab, 252 lab space of the Grid Research Integration and Deployment Center (GRID- C), at Oak Ridge National Laboratory. In power grids, customer-owned distributed energy resources (DERs) are more frequent than in the past, and the numbers of points of interconnection (POI) with customer-owned DERs have increased. Disruptive operation from DERs presents a risk to grid operations, and protective relays located at the POI are used to isolate out-of-tolerance or poorly behaving of DERs. Ensuring the integrity of data from the relays at the POI, and DLT could enhance the security of the power grids. The first application is a SC to define and control the allowable total power factor (TPF) of the DER (wind farm) output, and the terms of the SC are implemented using DLT with a CGGS for a customer-owned DER. The TPF SC was implemented by the CGGS using DLT. The experimental model was performed with a real-time simulator using a CGGS and relay in-the-loop. The data collected from the CGGS were used to execute the TPF SC. The TPF limits were between +0.9 and +1.0, and the breakers’ operation in the POI was controlled by the relay using the SC. The events were collected from the real-time simulator, CGGS, and SEL 700GT relay to validate a successful application of the TPF SC using DLT. The second application is a SC to measure and control the allowable voltage service limits (VSL) by the CGGS using DLT. The tests were performed by using a real-time simulator, CGGS and relay in-the-loop. The data was collected from the CGGS that executed the SC. The main constraints were defined based on ANSI C84.1 service voltage limits, and the operation of the breakers in the POI. The events were collected from the CGGS, and SEL 700GT relay to assess a successful operation of the VSL SC using DLT.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Communication Network Layer State Estimation Measurement Model for a Cyber-Secure Smart Grid

Network communication has been proven to be a very important tool and a key factor in the recent development and progress of the power grid operation. It is also considered as the foundation for the smart grid because information and communication are integrated into electricity distribution to achieve reliable and accurate knowledge of the power grid. In previous years, absorbing energy from substations and delivering it to customers was the only type of interaction we knew between utility companies and customers. Presently, the growing connections of small distributed generation units caused by the cost reduction of most of the technologies used in generation and storage of electrical energy, along with the potential benefits of renewable energy have pushed many researchers to look into the improvement of information and communication technologies (ICT) in order to ensure a bidirectional flow of power and data. Moreover, the evolution of information and communication technologies and its applications to smart grid have converted the smart grid into a cyber-physical system where vulnerabilities and additional security challenges such as cyber-threats and cyber-attacks have emerged. Previously, we have demonstrated that using machine learning-based processing on data gathered from communication networks and the power grid was a promising solution for detecting cyber threats by implementing a co-simulation of cyber-security for cross-layer strategy. Since the majority of the challenges observed can only be solved in the network communication layer, we present in this work a physics-based state estimation model of the communication network system towards enhanced cyber-physical security of the smart grid. Information integration with the previously developed machine learning model is developed, providing a enhanced cyber-physical security application for the smart grid. Easy-to-implement model, without hard-to-derive parameters, highlight potential aspects of the model for real-life applications.

Mathieu, Reynold↗
DOE OSTI2024

Optimal Control of SOEC-Based Hydrogen Production Systems for Demand Response Using Deep Reinforcement Learning in Smart Grids

Solid oxide electrolysis cell (SOEC) hydrogen production technology can range in size from small, appliance-size equipment to large-scale, central production facilities that can be tied directly to renewable or non-greenhouse-gas-emitting forms of electricity production, making it an ideal resource for demand response (DR). The SOEC hydrogen production system is a complex integrated system that encompasses fluid dynamics, electrical dynamics, and electrochemical and thermal dynamics, all of which involve non-linearity and non-convexity. Proper control of the SOEC hydrogen production system is crucial to enable its participation in the DR program. Here, to overcome the difficulty of designing an explicit control law for such nonlinear systems with nonconvex optimization features in DR applications, deep reinforcement learning (DRL) is explored to achieve the optimal control of the SOEC system for DR participation. Specifically, a twin delayed deterministic policy gradient (TD3) control framework is applied to achieve optimal response performance during DR events by considering power tracking error and hydrogen production efficiency with a suitable reward function. Two case studies with grid connections for tracking different DR commands were investigated. The first case study involved operating conditions reaching the boundaries, while the second involved operating conditions within the boundaries. The results showed that the proposed DRL-based control for SOEC can track the DR signal in a timely manner while maintaining high energy efficiency.

08 HYDROGEN↗
DOE OSTI2023

Bayesian GAN-Based False Data Injection Attack Detection in Active Distribution Grids With DERs

Advancements in information and communication technologies have revolutionized monitoring and control capabilities within smart grids. However, it also brings new vulnerabilities to data acquisition systems and state estimation functions, which attackers can subtly tamper with the measurement data through compromising the communication network. Moreover, the high penetration of renewable energy sources with the inherited characteristics of uncertainty and variability further complicates the design of effective intrusion detection systems. In this paper, a Bayesian deep learning-based approach is developed to detect cyber attacks and maintain the security of smart grids. Our method specifically addresses the prevalent issue of imbalanced data in real power systems, which arises from the predominance of normal system operations over compromised or attacked states. Employing a novel Bayesian GAN-based technique, our approach successfully discriminates between secure and compromised measurement data, even in scenarios with significant data imbalance. Furthermore, the proposed method accommodates various practical application factors, ensuring accurate intrusion detection despite the presence of measurement noise. The feasibility and effectiveness of the proposed detection mechanism are validated by testing on IEEE 13-node and 123-node test systems. Simulation results and comparisons with literature methods demonstrate the superiority of proposed cybersecurity solutions.

Bayesian GAN↗
DOE OSTI2026

Rolling Root Mean Square Based Multimodal Anomaly Detection for Real Time Monitoring of Smart Grid

Reliable real-time monitoring is valuable for maintaining the operational integrity of modern electrical smart grids. Deployment of heterogeneous sensing technologies in substations has enabled high-resolution, multichannel waveform monitoring, but also introduces challenges for anomaly detection due to noise, baseline drift, and modality-dependent signal characteristics. In this work, we present a computationally efficient unsupervised method for multimodal event detection based on Rolling Root Mean Square based Event Detection (RRMSED). The method is developed using in-house, field deployed sensors collecting data at a utility substation. The sensing system comprises voltage and current sensors, triaxial accelerometers, and magnetometers, collectively capturing electrical, vibrational, and magnetic waveform measurements at high temporal resolution. RRMSED operates by extracting rolling RMS energy features and their first-order temporal differences from consecutive waveform segments for each channel and then applying channel-specific statistical thresholds learned from historical data. A persistence-based exceedance logic is employed to robustly identify transient events while suppressing impulsive noise, and to provide precise temporal localization with high resolution. The framework is designed for continuous server-side operation and can be deployed in real time without requiring complex models. Experiments on simulated waveform data with known ground truth demonstrate low false positive (FP) and false negative (FN) rates. Application to real substation data shows RRMSED to identify events that are not captured by conventional monitoring indicators including fast transient detection algorithm currently deployed in the system. These results indicate that rolling RMS based features provide an effective and practical basis for real-time multimodal event detection in smart-grid substations.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2023

Architecture for Quantum-in-the Loop Real-Time Simulations for Designing Resilient Smart Grids

With the power grid growing more complex every day with the inclusion of new sensors and regulatory approvals that enable end-users and small local developers to participate in the grid, it is becoming challenging for conventional smart grid simulation, emulation, and testing technologies to keep up. In this work, we propose that quantum-encoded real-time simulations can be helpful under the new paradigm and operational circumstances to solve optimization problems for power grids. By leveraging the principles of quantum mechanics, the proposed quantum-in-loop (QIL) framework will enable better and faster optimization solutions based on real-world, real-time data streams, facilitating the real-time planning and operations of electrical grids that rely on millions of distributed sensors and controllers. Furthermore, QIL will allow researchers and engineers to assist utilities in designing, developing, and de-risking algorithms to optimize power grid operation and resilience by considering inputs from millions of grid-connected devices. QIL framework is being developed to have a self-limiting triage mechanism, which will help engineers and practitioners identify fundamental physical limits on quantum processors, revealing what quantum algorithms can and cannot do in utility-specific use cases and must continue to count on classical high-performance computing infrastructure.

digital real-time simulation↗
DOE OSTI2023

A Systematic Study to Determine 5G Baseline Performance for Scientific Computing

The fifth-generation (5G) cellular networks envisions achieving higher data rates, improved connectivity, reduced latency, and better quality of service (QoS) than the fourthgeneration (4G) cellular networks. Such improved performance can be utilized to address the challenges in applications such as electricity generation in power systems. The traditional power grids responsible for electricity generation suffer from drawbacks such as life-threatening blackout crises, and energy storage proliferation as they are not robust to extreme climatic conditions. A recent study proposed the idea of extending the capabilities of advanced wireless technologies such as the current 5G to develop a robust, energy-efficient, and secure smart grids. However there are two main challenges associated with the integration of power systems and wireless technologies. First, it is imperative to understand the architecture and the enabling technologies of 5G to ensure that the performance requirements of the smart grids are met. Second, an end-to-end testbed is required to determine if the performance requirements are met by estimating the 5G characteristics such as latency, and throughput. Our proposed alleviates the aforementioned concerns in the following manner. To begin with, a systematic study of the 5G architecture including both the StandAlone (SA) and Non-Standalone (NSA) operations is presented. Furthermore, a detailed survey of the possible 5G enabling technologies is elicited. In addition to these, an end-toend testbed that can estimate the 5G characteristics is explained in detail with appropriate preliminary results.

5G, 5G Communication↗
DOE OSTI2022

Device-Centric Ransomware Detection using Machine Learning-Based Memory Forensics for Smart Inverters

Ransomware attacks are the fastest-growing form of cyberattacks worldwide. Recently, ransomware attacks have targeted industrial control systems (ICSs), including power grids. Lessons learned from recent incidents in ICSs show that ransomware groups can deliver ransomware into not only the organization’s control servers, but also the operational technology (OT) devices such as smart inverters and smart grid devices. This paper proposes a machine learning (ML)- based memory forensics method enabling the detection of ransomware binaries stored in the memory of a commercial smart inverter. Device firmware binary files are extracted from a Serial Peripheral Interface (SPI) flash memory, and samples of both benign and ransomware binaries are generated by a binary manipulation method and a real-world ransomware encryption, separately. A deep transfer learning (DTL) method is used to retrain a convolutional neural network (CNN)-based ransomware detection algorithm using the generated samples. The experimental result validates that the proposed ML-based memory forensics method can accurately detect ransomware files.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Morphodynamics of dendrite growth in alumina based all solid-state sodium metal batteries

All solid-state batteries (ASSBs) with ceramic electrolytes and alkali metal anodes are a potential future energy storage technology for vehicle electrification and smart grids. However, uncontrollable dendrite growth toward ultimate short circuiting in solid electrolytes (SEs) has become a serious concern in the design of long-cycle, safe ASSBs, and the underlying mechanism has remained unclear. Here through multiscale imaging and morphodynamic tracking we show that Na dendrites grow in β''-Al 2 O 3 SEs through an alternating sequence of Na deposition and crack propagation. Atomic-scale imaging evidenced that electrochemical cycling causes massive delamination cracking along the Na + conduction planes, accompanied by the closure of neighboring conduction channels. In situ SEM observations revealed a dynamic interplay between Na deposition and crack propagation: Na deposition accumulates mechanical stress that induces cracking; cracking releases the local stress, which promotes further Na deposition. Thus, Na deposition and cracking alternatingly proceed until short circuits take place. A multiscale phase-field model is developed to recapitulate the morphodynamics of Na dendrite growth, predicting the tree-like fractal morphology of the growing dendrites. Importantly, our findings suggest that decoupling between Na deposition and cracking represents an important route to mitigate uncontrollable dendrite growth in ASSBs.

25 ENERGY STORAGE↗
DOE OSTI2025

Development and Validation of Smart Building Technology Modules for Academic and Professional Education (Final Technical Report)

Smart building technologies can improve building energy efficiency and resilience, reduce carbon emissions, and provide load flexibility to the grid. However, in both college curricula and building professionals’ continuing education, there is a lack of systematic instruction on smart building technologies. Slipstream, partnering with Texas A&M University (TAMU), the Society of Building Science Educators (SBSE), and the National Institute of Building Sciences (NIBS), developed a semester-long smart building curriculum for college students and 16 training videos for building professionals and the general public. The education and training cover the drivers and benefits of smart building technologies, key building energy systems, the latest sensor technologies and IoT devices, and focus on topics related to smart building controls (i.e., energy management information systems, smart building control platforms, cybersecurity, grid-interactive-efficient buildings [GEBs], smart building control methods, and occupant-centric control). The smart building curriculum for college students was taught at TAMU in the Spring semester of 2024 as part of the validation process. Student feedback was collected and summarized in a validation report by TAMU. The curriculum material was also reviewed by SBSE faculty who are interested in teaching smart building technology-related courses. Suggestions on revisions and better adoption of the materials by other faculty across the architectural, engineering, and construction (AEC) domains were compiled in a distinct validation report by SBSE. The SBSE validation report was used to create structured subsets of the curriculum material for adoption at different levels in different sub-disciplines. These subsets are categorized and offered on the SBSE website (https://www.sbse.org/courses/Smart-Building-Technologies). The 16 training videos for building professionals and the general public were previewed by 17 industry experts, and feedback and suggested changes were incorporated into the final version of these videos. The videos are organized into a smart building technology training course and published on the Whole Building Design Guide website (https://www.wbdg.org/ce/doe/bto/sbtt), which is hosted by the National Institute of Building Sciences (NIBS). Project team members created marketing materials to promote the awareness of these free, publicly available education and training resources. Outreach and marketing activities included creating short promotional videos, building project webpages, making project announcements on social media, conducting an email campaign, and directly reaching out to faculties and building professionals. This report describes the project approach, provides outlines of the training materials, along with links to resources, and identifies lessons learned in creating the content. We also suggest ways to scale the instruction of smart building concepts to empower the workforce to accelerate the adoption of smart building technologies in the real world.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

K-anonymity applied to the energy grid of things distributed energy resource management system

Smart grid infrastructure relies on information exchange between multiple actors in order to ensure system reliability. These actors include but are not limited to smart loads, grid control, and energy management technologies. As information exchange between these actors is susceptible to cyber-attacks, security and privacy issues are indispensable to ensure a reliable and stable grid. This position paper proposes a privacypreserving, trust-augmented secure scheme for a smart grid implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Privacy-preserving Information Security for the Energy Grid of Things

Smart grid infrastructure relies on information exchange between multiple actors in order to ensure system reliability. These actors include but are not limited to smart loads, grid control, and energy management technologies. Further, as information exchange between these actors is susceptible to cyber-attacks, security and privacy issues are indispensable to ensure a reliable and stable grid. This position paper proposes a privacy-preserving, trust-augmented secure scheme for a smart grid implementation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Distributed Energy Resource Management Systems: Preserving Customer Privacy through K-Anonymity

The smart grid represents the next generation of electricity distribution systems that utilizes recent technological innovations. It uses digital communication between its components and entities to attain more automation, self-sufficiency, and reliability. One of the many concerns in smart grid digital communication discussions is the possibility of violating customers’ privacy. Violating customers’ privacy imposes a significant barrier as smart grid desirable attributes are tightly tied to customers’ participation. Employing privacy models can address concerns regarding information privacy in smart grid digital communication. In this work, we provide an approach to utilizing K-anonymity to ensure data within the system excludes Personally Identifiable Information. Results suggest that a dynamically generated generalization hierarchy minimizes information loss incurred by the anonymization process.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗