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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Keyless Infrastructure Security Solution (KISS) (Technology Landscape Analysis Report)

The following landscape analysis explores blockchain or distributed ledger technologies application to securing electricity infrastructure. The exploration of how distributed ledger technologies can be used to increase the trustworthiness and integrity of the grid’s edge is imperative to the economic, security and well-being of all modern societies that rely on electricity. Blockchain technology’s ability to secure, track and optimize complex data transactions provides an exciting value proposition to securing and optimizing the U.S. power grid. An energy delivery system (EDS) operating at the grid’s edge requires unprecedented levels of security and trustworthiness to verify integrity of data and manage complex transactive and distributed Energy Resources (DERs) exchanges. Moreover, grid-edge devices lack visibility, control and security to conduct real-time energy transactions with the required security, speed and scale. The U.S. power grid is a complex system of systems that requires secure, reliable and trustworthy EDSs. Grid modernization has increased the speed and size of data sets exchanged on these systems (Gordes and Mylrea 2014). Exasperating the challenge is these systems are increasingly distributed creating new data fidelity and interoperability challenges for grid operators struggling to balance and incorporate DERs. Blockchain technology provides an atomically verifiable cryptographic signature to help increase the trustworthiness of EDSs at the grid’s edge. This feature is especially important at distribution level since EDSs and field devices have increasing operational and security requirements that are often diametrically opposed. For example, as data, speed and analytic requirements increase, security and functionality requirements increase. Further, as the grid’s edge incorporate DERs and transacts in real time, availability is prioritized over the integrity and confidentially of that data. Blockchain presents the prospect of solving some of these complex challenges related to grid security and modernization. However, there are more questions about blockchain applicability than answers. There are number of theoretical blockchain applications to the energy sector, but few energy utilities have actually applied, implemented or even experimented with the underlying distributed ledger technology and consensus algorithms that enable blockchain to exchange data more efficiently and securely.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Sprites and State Channels: Payment Networks that Go Faster than Lightning

Bitcoin, Ethereum and other blockchain-based cryptocurrencies, as deployed today, cannot scale for wide-spread use. A leading approach for cryptocurrency scaling is a smart contract mechanism called a payment channel which enables two mutually distrustful parties to transact efficiently (and only requires a single transaction in the blockchain to set-up). Payment channels can be linked together to form a payment network, such that payments between any two parties can (usually) be routed through the network along a path that connects them. Crucially, both parties can transact without trusting hops along the route. In this paper, we propose a novel variant of payment channels, called Sprites, that reduces the worst-case “collateral cost” that each hop along the route may incur. The benefits of Sprites are two-fold. 1) In Lightning Network, a payment across a path of ℓ channels requires locking up collateral for Θ(ℓΔ) time, where Δ is the time to commit an on-chain transaction. Sprites reduces this cost to Θ(ℓ+Δ). 2) Unlike prior work, Sprites supports partial withdrawals and deposits, during which the channel can continue to operate without interruption. In evaluating Sprites we make several additional contributions. First, our simulation-based security model is the first formalism to model timing guarantees in payment channels. Our construction is also modular, making use of a generic abstraction from folklore, called the “state channel,” which we are the first to formalize. We also provide a simulation framework for payment network protocols, which we use to confirm that the Sprites construction mitigates against throughput-reducing attacks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Machine Learning Approaches for Nuclear Material Accounting Data from Irradiation and Reprocessing

We are currently exploring data analysis methods for their ability to strengthen the synthesis and evaluation of information generated within domestic and international safeguard regimes. Safeguard data typically includes rich heterogenous datasets amenable to advanced data analytics methods, such as machine learning. We have converted transactional data containing both numerical and categorical attributes from a domestic nuclear material control and accountability (NMC&A) system into a low-dimensional numerical structure via linear principal component analysis. This data representation allows for global structure discovery via cluster analysis, which can characterize the typical behavior of each of the primary types of transaction events. Furthermore, the structure of the top principal components captures the “typical behavior” of the data and is thus amenable to anomaly detection through statistical hypothesis testing. We explored this capability by generating erroneous permutations of the data and computing the Q-residual quantity increase associated with the information loss when these data are projected into the low-dimensional principal component analysis space representative of typical transactions.Future work will focus on identifying data transformations (e.g., graph networks) that more closely align with the inherent structure of these transactions to explain more salient information and disambiguate the underlying nuclear process—in this case, irradiation and reprocessing—from artifacts of NMC&A system transactional record keeping data.

Drescher, Adam↗
DOE OSTI2021

Path-synchronous performance monitoring of interconnection networks based on source code attribution

Examples disclosed herein relate to path-synchronous performance monitoring of an interconnection network based on source code attribution. A processing node in the interconnection network has a profiler module to select a network transaction to be monitored, determine a source code attribution associated with the network transaction to be monitored, and issue a network command to execute the network transaction to be monitored. A logger module creates, in a buffer, a node temporal log associated with the network transaction and the network command. A drainer module periodically captures the node temporal log. The processing node has a network interface controller to receive the network command and mark a packet generated for the network command to be temporally tracked and attributed back to the source code attribution at each hop of the interconnection network traversed by the marked packet.

Chabbi, Milind M.↗
DOE OSTI2021

Visual Modeling for Complex System Valuation: Implementation Guidance

Within the Transactive Systems Program (TSP) at Pacific Northwest National Laboratory (PNNL) the need to incorporate a valuation analysis design early within the research process of transactive energy systems led to development of a valuation methodology. This methodology allows the modeling of economic exchanges within a complex system and supports the evaluation of individual stakeholder economic outcomes in addition to systemwide costs and benefits. The use of visual modeling practices enables the research team to reach common understanding and agreement on the analysis design within the complex system. While this methodology was developed for the valuation of transactive energy systems, it can be applied to any complex system where a granular economic analysis is desired. It allows for the inclusion of equity analyses and ties individual activities and microeconomic outcomes with the systemwide macroeconomic impacts. This document serves as implementation guidance for analysts planning to deploy the methodology within a research study. The appendixes provide specific guidance on how this methodology is deployed within the TSP at PNNL for analysts seeking guidance for deployment within that context.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Impact of Research and Development, Analysis, and Standardization on PV Project Financing Costs

The technical report discusses how R and D efforts focused on removing perceived risk from cash flows to investors have the potential to lower the cost of capital and increase the amount of leverage in a solar project. It also discusses how creating business efficiencies that allow financing transactions to occur more quickly with less effort can reduce the upfront costs associated with arranging financing for a solar project or group of projects. The paper then assesses the impact that these R and D activities might have on the volatility of PV asset cash flows and asset value, as well as the upfront costs of arranging a financial transaction. Finally, we insert these assumptions into financial models to analyze their impacts on the cost of capital for equity and debt investors, project leverage, and upfront financial transaction costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Sequence-to-sequence neural networks for short-term electrical load forecasting in commercial office buildings

The U.S. power grid is transforming to become smarter, cleaner, and more effi- cient. This is leading to the addition of significant distributed variable renew- able generation. Due to the variable nature of renewable generation, the short- and long-term supply-demand imbalances are less predictable, and conventional approaches to mitigating the imbalance will not be efficient or cost-effective. To address this challenge, transactive control technologies have been proposed which balance energy generation and consumption with market activity and in- frastructural limitations. Transactive control requires the ability of individual end-use loads to express flexibility as a function of a transactive signal (e.g., price). Empirical gray- and black-box models have been widely used to express flexibility, and although these approaches are generally easy to construct and simple to use, they do not capture the non-linear behavior that some end-use loads represent . Machine learning approaches have been proposed to address this limitation. Although deep learning approaches for forecasting end-use loads have been explored, certain aspects of the application of deep models to load forecasting are not well understood. These aspects include how much training data is required, and how models should be structured and trained. To that end, this work explores how to approach applying deep recurrent neural networks to short-term electrical load forecasting with a case study of four commercial office buildings. We identify data requirements for training accurate models of whole building electricity use conditioned on outdoor temperature, provide insight into model hyperparameter sensitivity, and demonstrate how readily models can be generalized to unseen buildings.

Skomski, Elliott↗
DOE OSTI2018

Rules of the rooftop: Platform design and price reductions in an online solar photovoltaic marketplace in the United States

Residential solar photovoltaic (PV) customers typically obtain price quotes directly from installers, a process with high consumer search costs that enable price dispersion and can discourage adoption. Recently, however, customers are increasingly shopping for PV using third-party online platforms that make it easier to obtain and compare quotes from multiple installers. While early evidence indicates that these platforms make the PV market more transparent and lower prices, the literature suggests that market outcomes are critically linked to detailed aspects of platform design. In this study, we econometrically analyze a unique dataset of quotes offered in the online PV marketplace EnergySage to investigate how four changes to the platform design have influenced quote prices and transaction prices. We find that the largest reductions in quote prices ($0.11/ W) and transaction prices ($0.15/W) were associated with the introduction of a price guidance information feature for installers. While educating and informing customers has repeatedly emerged as a policy priority for bolstering PV markets, these results suggest that the same should be true on the supply side. We also find that customers are willing to pay higher transaction prices for systems with higher-quality panels, dispelling concerns that online PV marketplaces lead to a 'race to the bottom' phenomenon by emphasizing price at the expense of quality.

14 SOLAR ENERGY↗
DOE OSTI2024

Using Financial Contracts to Facilitate Informal Leases Within a Western United States Water Market Based on Prior Appropriation

The ability to reallocate water to higher-value uses during drought is an increasingly important “soft-path” tool for managing water resources in an uncertain future. In most of the Western United States, state-level water market institutions that enable reallocation also impose substantial transaction costs on market participants related to regulatory approval and litigation. These transaction costs can be prohibitive for many participants in terms of both costs and lengthy approval periods, limiting transfers and reducing allocation efficiency, particularly during drought crises periods. This manuscript describes a mechanism to reduce transaction costs by adapting an existing form of informal leases to facilitate quicker and less expensive transfers among market participants. Instead of navigating the formal approval process to lease a water right, informal leases are financial contracts for conservation that enable more junior holders of existing rights to divert water during drought, thereby allowing the formal transfer approval process to be bypassed. The informal leasing approach is tested in the Upper Colorado River Basin, where drought and institutional barriers to transfers lead to frequent shortages for urban rights holders along Colorado's Front Range. Informal leases are facilitated via option contracts that include adaptive triggers and that define volumes of additional, compensatory, releases designed to mitigate impacts to instream flows and third parties. Results suggest that more rapid reallocation of water via informal leases could have resulted in up to $222 million in additional benefits for urban rights holders during the historical period 1950–2013.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Technology progress and clean vehicle policies on fleet turnover and equity: insights from household vehicle fleet micro-simulations with $\text{ATLAS}$

This paper documents the design and application of ATLAS (Automobile and Technology Lifecycle-Based ASsignment), a comprehensive household vehicle transaction and technology adoption micro-simulator in the San Francisco Bay Area. ATLAS evolves the fleet mix of individual households by simulating the vehicle transaction and choice decisions in response to co-evolving demographics, land use, and vehicle technology simulations. While most existing literature has focused on the aggregate clean vehicle uptake, this paper differentiates distributional effects and decomposes the underlying mechanisms across heterogeneous sub-populations of households. Using scenarios and sensitivity simulations that vary vehicle technology and policy assumptions, we find that Zero Emission Vehicles (ZEVs) penetrate into higher income groups at a faster rate than into lower income groups, which is intuitive and aligns with expectations. Interestingly, the relative income disparity in ZEV ownership shrinks over time across all scenarios, with a ZEV mandate coupled with declining battery cost leading to the greatest reduction in disparity of ZEV ownership by 2050. Federal, state, and local financial incentives influence the redistribution of ZEV uptake across income groups and contribute to narrowing income disparity. Vehicle transaction frequency and new versus used market dynamics are found to be important factors contributing to the income disparity.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Automobile and Technology Lifecycle-Based Assignment (ATLAS) v2.0.12

ATLAS is a comprehensive vehicle transaction and technology adoption microsimulator. ATLAS evolves the fleet mix of individual households by simulating the transaction (vehicle addition, disposal, and replacement) and choice (vehicle type, vintage, powertrain, and tenure) decisions in response to the co-evolving demographics, land use, and vehicle technology simulations. Different from the existing vehicle models that are either static or aggregated (e.g. stock model), ATLAS is fully disaggregated and dynamic following a sequential and circumstantial decision-making trajectory. This fine-grained approach not only enhances the realism of the simulation but also provides a nuanced understanding of the dynamics inherent in vehicle fleet evolution. ATLAS outputs are fully compatible with subsequent agent-based transportation modeling system and can enable distributional effect analysis regarding the fleet turnover among heterogeneous populations. ATLAS expands the typical new sale focused vehicle choice modeling to including used vehicle transactions that are of increasing interests to understanding the vehicle adoption behavior among lower income households.

Jin, Ling↗
DOE OSTI2018

Dynamic Household Vehicle Decision Modeling Considering Plug-in Electric Vehicles

Plug-in electric vehicles (PEVs) offer alternatives to traditional vehicles that rely on petroleum-based fuels. While PEV customers can enjoy significant reductions in fuel costs, they incur a larger capital cost than users of traditional vehicles because PEV technology is still maturing. Therefore, consumers adopting PEVs face a trade-off between fuel cost savings with environmental benefits and extra capital cost. It is thus crucial for policy makers and PEV manufacturers to understand people's vehicle decision-making process while considering their socio-economic and travel pattern characteristics as well as the built-environment factors. This paper presents a connected, two-stage, dynamic model of PEV adoption and vehicle-transaction decision-making. Two connected nested logit (NL) models are estimated. The upper level is a two-level NL model to predict choice of vehicle type between four fuel types: gasoline, diesel, hybrid gasoline-electric, and PEV. The lower level is an NL model of vehicle-transaction choice which accommodates four transaction decisions of buy, trade, dispose, and do nothing, while accounting for the log-sum from the vehicle-type choice model, and is estimated using two waves of a panel data set. We find that households with higher levels of income and education are more likely to adopt a PEV. We also found that primarily decision makers take into account the accessibility to charging stations as a critical factor in choosing PEVs.

Nazari, Fatemeh↗
DOE OSTI2019

Economical and Engineering Aspects of Proactive Demand Participation: Centralized versus Bilateral Control Structure

The electric grid of the U.S. is evolving from a system characterized by large and centralized fossil-fueled power plants and passive customers to a network with high penetration of distributed energy resources and proactive customers. This project facilitated the evolution of the U.S. electric power grid by enabling proactive participation of demands in electricity market at the power distribution system level. This project has three major achievements. First, we designed a centralized distribution system operator managed electricity market and developed a three-phase alternating current optimal power flow algorithm to coordinate the operation of large-scale flexible loads and distributed energy resources. Second, we designed a decentralized bilateral coordinate framework for retail electricity market, where each participating customer is capable of making decisions regarding energy production and consumption based on information received from transactive neighbors. Third, the impacts of centralized and decentralized demand-side participation scheme on power system operations were evaluated with both large-scale simulations and hardware-in-the-loop demonstrations. The major activities and specific accomplishments of the technical tasks are summarized in Section II. The detailed design of centralized/hierarchical and decentralized/bilateral retail electricity markets and the comparison between the two markets are described in Section III. The accomplishments and conclusions of the project are presented in Section IV. The major findings of the project falls into three areas, hierarchical distribution system operator managed electricity market, bilateral electricity market and decentralized market coordination, and comparison of centralized and decentralized market coordination mechanisms. In the area of hierarchical distribution system operator managed electricity market design, an integrated wholesale and retail electricity market was shown to be highly effective in extracting the operational flexibility from distributed energy resources to provide services to the wholesale market. A chordal conversion based convex iteration algorithm is not only computationally efficiency but also achieves global optimal solution in coordinating the operations of a large number of heterogeneous distributed energy resources.In the area of decentralized market coordination approach, we showed that bilateral transactions among proactive customers and decentralized transaction clearing mechanism based on simultaneous second-price sealed-bid auction can encourage truthful bidding strategies from the market participations. In addition, the individual customers can rely on a portfolio optimization approach to maximize their returns while minimizing risks. In the area of comparison of centralized and decentralized market coordination mechanisms, we found that the centralized market design results in higher social welfare and lower wholesale market clearing price for electricity. In general, centralized market design favors distributed energy resources and fixed loads. The decentralized market design mostly benefits flexible loads and conventional generators.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Blockchain based Communication Architectures with Applications to Private Security Networks

Existing communication protocols in high consequence security networks are highly centralized. While this naively makes the controls easier to physically secure, external actors require fewer resources to disrupt the system because there are fewer points in the system can be destroyed or interrupted without the entire system failing. We present a solution to this problem using a proof-of-work-based blockchain implementation built on MultiChain. We construct a test-bed network containing two types of data input: visual imagers and microwave sensor information. These data types are ubiquitous in perimeter intrusion detection security systems and allow a realistic representation of a real-world network architecture. The cameras in this system use an object detection algorithm to nd important targets in the scene. The raw data from the camera and the outputs from the detection algorithm are then placed in a transaction on the distributed ledger. Similarly, microwave data is used to detect relevant events and are placed in a transaction. These transactions are then bundled into blocks and broadcast to the rest of the network using the Bitcoin-based MultiChain protocol. We develop five tests to examine the security metrics of our network. We performed the five security metric test using different sized networks from 7 to 39 nodes to determine how the metrics scale with respect to size. We nd that when compared to a centralized architecture our implementation provides a resiliency increase that is expected from a blockchain-based protocol without slowing the system so much that a human operator would notice. Furthermore, our approach is able to detect tampering in real time. Based on these results, we theorize that security networks in general could use a blockchain-based approach in a meaningful way.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Modeling Value Flows in Utility Rate Structures

As the increased adoption of distributed energy resources continues to challenge flat utility rate structures, time-varying rates and more dynamic mechanisms like transactive energy systems can better leverage customer-sited distributed energy resources to provide grid services. However, adopting new utility policies can be a timely process and requires a high level of transparency into the energy system. A wide range of stakeholders must understand who may be affected by policy changes and how. This work employs the valuation methodology developed under Pacific Northwest National Laboratory’s Transactive Systems Program to outline the functional differences in value flow under a series of conventional rate structures and a transactive energy system. The resulting value model illustrates the nuances that arise and highlights future avenues of work that will be necessary as utilities across the country continue to develop new rate structures and market mechanisms.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Forecasting Commercial Building Electricity Consumption, Zone Airflow and Zone Temperature: Update - Development of a Generalized Machine Learning Approach

The U.S. power grid is being transformed to make it smarter, more efficient, and cleaner. This transformation is leading to the addition of a significant of energy generated by distributed, variable, and renewable resources. Because of the variable nature of renewable generation, the short- and long-term supply and demand imbalances are less predictable, and conventional approaches to mitigating the imbalances will be less efficient or cost effective. To address this challenge and to support the mission and the vision of the U.S. Department of Energy’s (DOE’s) Office of Energy Efficiency and Renewable Energy (EERE) Building Technologies Office has developed a Grid-Interactive Efficient Building Strategy. The strategy focuses on simultaneously improving building energy efficiency and supporting reliability and resilience of the electric grid more efficiently and at a lower cost. In addition, EERE and DOE’s Office of Electricity created an initiative led by DOE and supported by the national laboratories under the Grid Modernization Lab Consortium structure to enhance grid modernization. The work reported in this document is part of the first set of projects funded under the initiative to design, develop, and validate scalable transactive control technologies for the commercial buildings sector. Transactive controls requires the ability of individual end-use loads to express flexibility as a function of a transactive signal (e.g., price). Empirical grey- and black-box models have been widely used to express flexibility. Although this approach is generally easy to construct and simple to use, it does not capture non-linear behavior that some end-use loads represent. Therefore, Pacific Northwest National Laboratory (PNNL) with support from Western Washington University conducted this research to explore the use of deep machine learning (ML) techniques. The work reported in this document is limited to forecasting whole building electricity consumption, the zone airflow and the zone temperature predictions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1995

The use of ion mobility spectrometry and gas chromatography/mass spectrometry for the detection of illicit drugs on clandestine records

Illicit drug distribution has over the past decade grown tremendously from simple 'drug pushing' where drugs were distributed from poorly organized individuals to today's well organized and well financed drug cartels. This change to a more 'corporate-like' atmosphere has resulted in a greater use of record keeping to monitor the profits generated. The use of record keeping by drug distributors is not restricted to high level drug smugglers but is used at all levels within the distribution network. Dealers at all levels including street dealers are generally 'fronted', given on consignment quantities of drugs that they in turn sell to customers, thereby requiring the need for records to keep track of drug sales versus liabilities. These records because of their illicit nature are often encrypted to hide the fact that they are indeed records of drug transactions. The creation of a handwritten notation concerning a drug transaction is normally brought on because of a purchase or sale. In a sale, this is commonly accomplished through a consignment, or the designation of a quantity to a customer to whom that amount has been 'fronted'. Because this activity generates a debt, it follows that an accounting for payments made, as well as new transactions completed, is only logical. One of the most common means of representing these is through an 'accounting flow', in which payments are subtracted from a running balance while new sales are added to it. The examination of illicit drug records has been the key to the prosecution of numerous federal, state, and local drug cases for a number of years. The Document Section of the FBI Laboratory, through its Racketeering Records Analysis Unit (RRAU), has been involved in such analytical efforts since 1983. Detailed analytical research brought about an evolution in the systematic approach utilized in the RRAU since that time. The close proximity of the drugs to the records often results in trace drug evidence being transferred to the records. The detection of trace drug residue on surfaces by ion mobility spectrometry (IMS) is well documented in literature. The following procedure will deal primarily with the newer techniques of trace drug analysis and drug record analysis developed by the Chemistry/Toxicology Unit of the FBI Laboratory since the more traditional techniques of latent finger print analysis and document analysis are well known.

Donnelly, Brian↗
NASA NTRS2001

Flight Deck Data Link Displays: An Evaluation of Textual and Graphical Implementations

In Experiment 1, 16 pilots participated in a part-task simulation study that evaluated pilot data link communication for short and long message types and for two textual formats. No differences were found between the two textual formats when evaluating data link transaction times and pilot performance on a secondary task. Pilots initiated flight changes more quickly with the T-Scan format, where location of clearance information roughly corresponded to the cockpit instrument layout. Longer messages were less problematic than two short messages sent in close succession as pilots required more verbal clarification for closely spaced messages. 24 pilots participated in a second experiment that evaluated pilot communication performance for textual data link, two implementations of graphical data link, and a combined graphical and textual information modality. The two modalities incorporating text resulted in significantly faster transaction times and better performance on the secondary task than the two graphical-only implementations. The interval between messages was also more systematically varied in Experiment 2, and a short interval between messages significantly increased the access time for the second message. This delay in access was long enough to increase significantly the total transaction time of the second message, and this effect was exaggerated for the graphical-only implementations. Time to view the message before acknowledgement and time to initiate flight changes were not affected by the interval manipulation, This suggests that pilots adopt a sequential message handling strategy, and presenting messages closely in succession may present operational problems in a data link Air Traffic Control (ATC) environment. The results of this study also indicate that the perceived importance of message content is currently a crucial element in pilot data link communication.

McGann, Alison↗