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This project involved the design, development, and testing of a commercial smart service panel. It includes an innovative AI-driven control method to manage smart functions of the panel and a technical-economic feasibility assessment for enhancing building energy resilience and grid-edge technology integration.
Significant enhancements are being implemented into the GRIDGEN3D, multiple block, structured grid generation software. Automatic, point-to-point, interblock connectivity will be possible through the addition of the domain entity to GRIDBLOCK's block construction process. Also, the unification of GRIDGEN2D and GRIDBLOCK has begun with the addition of edge grid point distribution capability to GRIDBLOCK. The geometric accuracy of surface grids and the ease with which databases may be obtained is being improved by adding support for standard computer-aided design formats (e.g., PATRAN Neutral and IGES files). Finally, volume grid quality was improved through addition of new SOR algorithm features and the new hybrid control function type to GRIDGEN3D.
Load growth at the grid edge is driving increased attention to the distribution system and its ability to enable customer technology adoption in an affordable and timely manner. Key industry stakeholders, including electric utilities and regulators, can benefit from strategies to manage and balance customer needs with infrastructure investments, such as demand flexibility. This report focuses on demand flexibility—the ability to reduce, shift, shed, generate, or modulate loads in response to building and grid needs—to reduce the need for costly grid upgrades by deferring investment needs and increase system reliability by shifting electricity usage during periods of high risk. Specifically, we focus on the emerging characteristics of business models for demand flexibility as a framework to understand how demand flexibility programs generate value. In this report, we focus on demand flexibility program stakeholder ecosystem management strategies, which provide information on value delivery and describe how program implementers leverage partner capabilities and collaborate to deliver customer and grid benefits. This report discusses the role of stakeholder ecosystem management in demand flexibility programs, identifies existing challenges to effective stakeholder management, and describes lessons learned. This report is part of a series that includes reports on value propositions, customer relationship management strategies, and program life cycle.
Load growth at the grid edge is driving increased attention to the distribution system and its ability to enable customer technology adoption in an affordable and timely manner. Key industry stakeholders, including electric utilities and regulators, can benefit from strategies to manage and balance customer needs with infrastructure investments, such as demand flexibility. This report focuses on demand flexibility—the ability to reduce, shift, shed, generate, or modulate loads in response to building and grid needs—to reduce the need for costly grid upgrades by deferring investment needs and increase system reliability by shifting electricity usage during periods of high risk. Specifically, we focus on the emerging characteristics of business models for demand flexibility as a framework to understand how demand flexibility programs generate value. In this report, we focus on demand flexibility program customer relationship management strategies, which provide information on value creation and focus on ensuring customers can navigate programs smoothly. This report discusses the role of customer relationship management strategies in demand flexibility programs, characterizes customer relationship management strategies that can be considered during program design and implementation, identifies existing challenges to customer relationship management strategies, and describes lessons learned. This report is part of a series that includes reports on value propositions, stakeholder ecosystem management, and program life cycle.
Load growth at the grid edge is driving increased attention to the distribution system and its ability to enable customer technology adoption in an affordable and timely manner. Key industry stakeholders, including electric utilities and regulators, can benefit from strategies to manage and balance customer needs with infrastructure investments, such as demand flexibility. This report focuses on demand flexibility—the ability to reduce, shift, shed, generate, or modulate loads in response to building and grid needs—to reduce the need for costly grid upgrades by deferring investment needs and increase system reliability by shifting electricity usage during periods of high risk. Specifically, we focus on the emerging characteristics of business models for demand flexibility as a framework to understand how demand flexibility programs generate value. In this report, we focus on demand flexibility value propositions, which provide information on value creation and describe how programs deliver clear benefits that address customer and grid needs. This report discusses the role of value propositions in demand flexibility programs, provides an overview of value propositions for a range of demand flexibility stakeholders, identifies existing challenges to establishing an effective value proposition, and describes lessons learned. This report is part of a series that includes reports on customer relationship management strategies, stakeholder ecosystem management, and program life cycle.
Load growth at the grid edge is driving increased attention to the distribution system and its ability to enable customer technology adoption in an affordable and timely manner. Key industry stakeholders, including electric utilities and regulators, can benefit from strategies to manage and balance customer needs with infrastructure investments, such as demand flexibility. This report focuses on demand flexibility—the ability to reduce, shift, shed, generate, or modulate loads in response to building and grid needs—to reduce the need for costly grid upgrades by deferring investment needs and increase system reliability by shifting electricity usage during periods of high risk. Specifically, we focus on the emerging characteristics of business models for demand flexibility as a framework to understand how demand flexibility programs generate value. In this report, we focus on the life cycle of demand flexibility programs, which provides information on value creation and describes the various deployment phases program implementers navigate from initial program conceptualization through to program expansion and replication to new customer segments and regions. This report characterizes the key phases of the demand flexibility program life cycle, identifies existing challenges across the program deployment phases, and describes lessons learned. This report is part of a series that includes reports on customer relationship management strategies, stakeholder ecosystem management, and program life cycle.
The rapid digitalization of the electric grid - driven by the integration of inverter-based resources (IBRs), battery energy storage systems (BESS), and advanced grid control platforms - has significantly enhanced grid efficiency, visibility, and flexibility. However, this evolution also introduces new cybersecurity risks, particularly through supply chain dependencies and operational blind spots at the grid edge. To address these challenges, Idaho National Laboratory (INL), through the Department of Energy (DOE) Office of Cybersecurity, Energy Security, and Emergency Response (CESER) Rapid Risk initiative, conducted a series of rapid risk assessment engagements with energy organizations across the United States. Drawing on lessons learned from these engagements, INL developed the following threat hunting guide for asset owners and operators (AOOs) to enhance their cybersecurity visibility within BESS and IBR systems. The guide demonstrates how to use passive network monitoring to baseline device behavior, detect adversarial activity, and investigate anomalies without disrupting operations. By implementing these practices, energy sector stakeholders can improve coordination between cybersecurity and operations teams and strengthen the resilience of distributed energy resources (DERs) within the modern power grid. Prior to implementing any network monitoring, packet capture, or threat hunting activity described in this guide, AOOs are strongly advised to review applicable governance frameworks, legal requirements, and organizational policies. This guide is intended for informational and educational purposes only. It does not replace compliance with any federal, state, or local cybersecurity mandates or industry standards. Implementation of described configurations, technologies, or analytic workflows is performed at the discretion and responsibility of the asset owner and operator.
Grid edge renewable energy resources, such as rooftop solar photovoltaics, closely interact with consumer load profiles. Therefore, forecasting future electricity demand, ideally at the individual household level, is indispensable. In this paper, we present a transfer learning enhanced household load profile forecasting method. First, we tune a long short-term memory forecasting model to perform day-ahead prediction of household electricity load profiles. Then we improve these individualized models using transfer learning, and we use k-means clustering to create optimal source data sets. We find average improvements of 4.38% (largest improvement of 10.71%) when the entire data set was used to train the source model and 2.45% (largest improvement of 11.57%) in the mean absolute error when households were first clustered and used to train separate source models for each cluster. We find that transfer learning with clustered data can effectively boost the forecasting performance of the LSTM models. We use realistic household power measurements for 148 real residential households in Austin, Texas.
This presentation is for a panel in the Grid Edge conference about orchestrated situational awareness for automated restoration and resilience enhancement.
Alfalfa-based testbeds enable building equipment, control products, and workforce development tools to interact with dynamic building simulations representing the desired building, system, weather, and grid configuration. Alfalfa is used to de-risk implementation of load flexibility prior to field deployment, reducing the costs and timelines associated with adoption of decarbonization technology at the grid edge.
A CFD sensitivity analysis is conducted for an aircraft at several conditions, including flow with substantial separation (buffet onset). The sensitivity is studied using two different Navier-Stokes computer codes, three different turbulence models, and two different grid treatments of the wing trailing edge. This effort is a follow-on to an earlier study of CFD variation over a different aircraft in buffet onset conditions. Similar to the earlier study, the turbulence model is found to have the largest effect, with a variation of 3.8% in lift at the buffet onset angle of attack. Drag and moment variation are 2.9% and 23.6%, respectively. The variations due to code and trailing edge cap grid are smaller than that due to turbulence model. Overall, the combined approximate error band in CFD due to code, turbulence model, and trailing edge treatment at the buffet onset angle of attack are: 4% in lift, 3% in drag, and 31% in moment. The CFD results show similar trends to flight test data, but also exhibit a lift curve break not seen in the data.
A patched-grid algorithm for the analysis of complex configurations with an implicit, upwind-biased Navier-Stokes solver is presented. Results from both a spatial-flux and a time-flux conservation approach to patching across zonal boundaries are presented. A generalized coordinate transformation with a biquadratic geometric element is used at the zonal interface in order to treat highly stretched viscous grids and arbitrarily shaped zonal boundaries. Applications are made to the F/A-18 forebody-Ieading-edge extension configuration at subsonic, high-alpha conditions. Computed surface flow patterns compare well with ground-based and flight-test results; the large effect of Reynolds number on the forebody flowfield is shown.
Two special polyhedra present themselves for the definition of B-splines: a simplex S and a box or parallelepiped B, where the edges of S project into an irregular grid, while the edges of B project into the edges of a regular grid. More general splines may be found by forming linear combinations of these B-splines, where the three-dimensional coefficients are called the spline control points. Univariate splines are simplex splines, where s = 1, whereas splines over a regular triangular grid are box splines, where s = 2. Two simple facts render the development of the construction of B-splines: (1) any face of a simplex or a box is again a simplex or box but of lower dimension; and (2) any simplex or box can be easily subdivided into smaller simplices or boxes. The first fact gives a geometric approach to Mansfield-like recursion formulas that express a B-spline in B-splines of lower order, where the coefficients depend on x. By repeated recursion, the B-spline will be expressed as B-splines of order 1; i.e., piecewise constants. In the case of a simplex spline, the second fact gives a so-called insertion algorithm that constructs the new control points if an additional knot is inserted.
This poster presents a hierarchical control framework for a virtual power plant that leverages behind-the-meter resources for grid services while maintaining customer privacy during setpoint disaggregation. Unlike many existing approaches, the virtual power plant model uses a hierarchical control strategy and an iterative approach to determine the optimal set point dis-aggregation without direct load control while maintaining system-level power flow and voltage constraints. The proposed approach is numerically validated on a synthetic distribution feeder in San Francisco, demonstrating the ability of the framework to provide privacy-preserving virtual power plant services.
The U.S. is facing a significant increase in energy demand, driven by AI advancements, the rapid expansion of data centers, manufacturing and industrial growth, and the electrification of transportation and buildings. Buildings alone account for approximately 75% of U.S. electricity consumption and 40% of total energy use. To address these challenges, NLR leverages its state-of-the-art research facilities, advanced energy modeling, hardware-in-the-loop emulation, and real-world demonstrations to provide data-driven insights that de-risk emerging energy solutions, increase efficiency and demand flexibility, optimize grid controls, and identify vulnerabilities to enhance energy security. This presentation will highlight our research ecosystem and its role in supporting a more reliable, affordable, and adaptive energy infrastructure in the face of accelerating demand.
The electric system is rapidly evolving and growing, raising new questions about how to define and value grid services delivered by a diverse set of resources, including customer-owned assets. This report advances a service–parameter–value framework that links what a grid service does to why it matters, and how its benefits are evidenced. We build upon prior efforts (Bender et al. 2021) and extend them to include value categories (reliability, resilience, cost & access, and security) with primary and secondary tiers, service-specific value streams that explain how benefits materialize, and metrics that make valuation traceable and comparable. We apply the framework to the six core grid services defined in (Kolln et al. 2023): Frequency Response, Regulation, Reserves, Energy, Voltage Management, and Blackstart and demonstrate it on three use cases to highlight the adaptability of these value streams. Further, we outline how the framework integrates with existing tools such as asset optimization, hosting capacity and cost-benefit analysis, and propose a path to portable qualifications and consistent stacking rules for use in tariffs and filings. Finally, we identify future pathways for research and application research needs to support standardization and decision-ready comparisons across services, assets, and architectures.
A new blocking technique for complex three-dimensional configurations is described. This new technique is based upon the concept of an abstraction, or squared-up representation, of the configuration and the associated grid. By allowing the user to describe blocking requirements in natural terms (such as 'wrap a grid around this leading edge' or 'make all grid lines emanating from this wall orthogonal to it'), users can quickly generate complex grids around complex configurations, while still maintaining a high level of control where desired. An added advantage of the abstraction concept is that once a blocking is defined for a class of configurations, it can be automatically applied to other configurations of the same class, making the new technique particularly well suited for the parametric variations which typically occur during design processes. Grids have been generated for a variety of real-world, two- and three-dimensional configurations. In all cases, the time required to generate the grid, given just an electronic form of the configuration, was at most a few days. Hence with this new technique, the generation of a block-structured grid is only slightly more expensive than the generation of an unstructured grid for the same configuration.
The Nuclear Electric Xenon Ion System (NEXIS) ion thruster was developed for potential outer planet robotic missions under NASA's Prometheus program. This engine was designed to operate at power levels ranging from 16 to over 20 kWe at specific impulses of 6000 to 7500 s for burn times of up to 10 years, satisfying the requirements of nuclear electric propulsion systems such as that on the proposed Prometheus 1 mission to explore the icy moons of Jupiter. State-of-the-art performance and life assessment tools were used to design the thruster. Following the successful performance validation of a Laboratory Model thruster, Development Model hardware was fabricated and subjected to vibration and wear testing. The results of a 2000-hour wear test are reported herein. Thruster performance achieved the target requirements and was steady for the duration of the test. Ion optics performance was similarly stable. Discharge loss increases of 6 eV/ion were observed in the first 500 hours of the test and were attributed to primary electron energy decreases due to cathode insert conditioning. Relatively high recycle rates were observed and were identified to be high-voltage-to-ground arcs in the back of the thruster caused by wire insulation outgassing and electron penetration through the plasma screen. Field emission of electrons between the accelerator and screen grids was observed and attributed to evolution of field emitter sites at accelerator grid aperture edges caused by ion bombardment. Preliminary modeling and analysis indicates that the NEXIS engine can meet mission performance requirements over the required lifetime. Finally, successful validation of the NEXIS design methodology, design tools, and technologies with the results of the wear test and companion performance and vibration tests presents significant applicability of the NEXIS development effort to missions of near-term as well as long-term interest for NASA.