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At least 1,747 records · Page 97

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DOE OSTI2023

A Novel Resilience-Oriented Cellular Grid Formation Approach for Distribution Systems with Behind-the-Meter Distributed Energy Resources

This paper presents a resilience-oriented cellular grid formation approach to achieve scalable and reconfigurable community microgrid operations for distribution systems with behind-the-meter distributed energy resources. A set of interconnected solar photovoltaics, energy storage systems, and load is termed as a cell, implying a subset of the grid that can operate independently using its own resources. Cells are identified such that each cell inherently has sufficient energy resources to black start and can provide a certain level of backup power for its load under the loss of utility power supply. The proposed cell formation approach builds on a unique self-organizing map-based method (SomRes) to quantify a system's resilience. Using SomRes and a non-dominated sorting-based genetic algorithm (NSGA-II), a fast and efficient cell formation algorithm is developed to identify cells in a distribution system that are resilient against extreme events. The efficacy of the proposed approach is demonstrated on a numerical model of a real distribution feeder in Colorado, United States.

distributed energy resource↗

DOE OSTI2026

From natural language to control signals: a conceptual framework for semantic channel finding in complex experimental infrastructure

Modern experimental platforms such as particle accelerators, fusion devices, telescopes, and industrial process control systems expose tens to hundreds of thousands of control and diagnostic channels, accumulated over decades of hardware evolution. Operators and AI systems alike depend on informal expert knowledge, inconsistent naming conventions, and scattered documentation to locate the signals required for monitoring, troubleshooting, and automated control, creating a persistent bottleneck for reliability, scalability, and emerging language-model-driven interfaces. We formalize semantic channel finding, the task of mapping natural-language intent to concrete control-system signals, as a general problem in complex experimental infrastructure, and introduce a four-paradigm conceptual framework to guide architecture selection based on facility-specific data regimes. The paradigms span (i) direct in-context lookup over small, curated channel dictionaries, (ii) constrained hierarchical navigation through structured trees, (iii) interactive agent exploration using iterative reasoning and tool-based database queries, and (iv) ontology-grounded semantic search that decouples channel meaning from facility-specific naming conventions. We demonstrate the practical feasibility of each paradigm through proof-of-concept implementations at four operational facilities spanning two orders of magnitude in scale: from compact free-electron lasers to large synchrotron light sources, operating under diverse control-system architectures ranging from clean hierarchical naming schemes to legacy environments with decades of heterogeneous conventions. Where evaluated against expert-curated operational queries, these instantiations achieve 90%–97% accuracy, validating the framework’s applicability across real-world deployment scenarios. To accelerate adoption across the broader scientific and industrial control-system community, we release open-source, plug-and-play implementations of all three interactive paradigms-direct lookup, hierarchical navigation, and middle-layer exploration-within the Osprey framework, together with tools for channel database generation, interactive testing, and minimal-configuration deployment. This work establishes semantic channel finding as a foundational capability for human-centric and agentic AI interfaces at large-scale facilities, providing both a systematic framework for architecture design and practical resources to enable adoption without building custom infrastructure from scratch.

channel finding↗

DOE OSTI2022

Model predictive control for demand flexibility: Real-world operation of a commercial building with photovoltaic and battery systems

Hundreds of studies have investigated Model Predictive Control (MPC) for the optimal operation of building energy systems in the past two decades. However, MPC field tests are still uncommon, especially for small- and medium-sized commercial buildings and for buildings integrated with onsite renewables. This paper describes the implementation and the long-term performance evaluation of an MPC controller in a small commercial building equipped with behind-the-meter photovoltaics and electrochemical batteries. MPC controls space conditioning, commercial refrigeration, and the battery system. We tested two types of demand flexibility applications in the field: electricity bill minimization under time-of-use tariffs and responses to grid flexibility events. Results show that the proposed controller achieves 12% of annual electricity cost savings and 34% peak demand reduction against the baseline, while respecting thermal comfort and food safety. The field tests also demonstrate the ability of the MPC controller to provide a multitude of grid services including real-time pricing, demand limiting, load shedding, load shifting, and load tracking, using the same optimization framework.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Experimental investigation of the small-scale prototype of the dew point water recovery system

The article presents experimental investigation of the Dew Point Water Recovery (DPWR) system designed for clean water recovery using the dew point cooling phenomenon. The technology is a novel distillation process which allows for highly efficient water recovery. It is a first distillation technology which allows for water distillation through direct contact with atmospheric air. The authors designed, built, and tested an original prototype of a laboratory-scale DPWR system to verify its basic operating principles and perform performance analysis. It was shown that on average 10 × 10 −6 m 3 of clean water could be recovered from each cubic meter of the supply air with an average GOR of 1.9. Although the operation of the DPWR system requires electricity to drive fans, pumps and other associated equipment, the actual driving force of the system is thermal energy. The average thermal energy demand of the DPWR system was 370 kWh/m 3 , and more than half of this energy was based on the thermodynamic potential of the supply air. Based on experimental data, the mathematical model of the DPWR system was verified and it was found that the average relative discrepancy in predicting air temperature did not exceed 8%, and in predicting air humidity: 5%. In conclusion, the conducted experiments confirmed that the proposed DPWR system is feasible and has the potential to fill the technological gap in terms of a low-energy and easily accessible water recovery method.

42 ENGINEERING↗

DOE OSTI2022

Comparing the economic performance of ice storage and batteries for buildings with on-site PV through model predictive control and optimal sizing

Integrating renewable energy and energy storage systems provides a way of operating the electrical grid system more energy efficiently and stably. Thermal storage and batteries are the most common devices for integration. However, it is not clear which integrated storage system performs better in terms of overall economics. Ice storage has low initial and maintenance costs, but there is an efficiency penalty for charging of storage and it can only shift electrical loads associated with building cooling requirements. A battery's round-trip efficiency, on the contrary, is quite consistent and batteries can be used to shift both HVAC and non-HVAC loads. However, batteries have greater initial costs and a shorter life. Finally, this research presents a tool, using model predictive control and optimal sizing, and provides a case study for comparing life-cycle economics of battery and ice storage systems for commercial buildings that have chillers for cooling and an on-site photovoltaic system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Foundational Science to Accelerate Nuclear Energy Innovation [Brochure]

The foundational science gaps inhibiting the advancement of nuclear energy technologies are identified and tackled in five priority research opportunities. These opportunities pave the way to accelerate the development and ultimately the adoption of new nuclear energy systems. They include the fundamental aspects of ion-electron interactions; novel properties of next-generation coolants and solvents; interfacial dynamics, not only in solids, but in other aspects of nuclear reactors; novel operando and in-situ monitoring and sensing; and artificial intelligence to accelerate condensed phases discovery. Building on the foundation established by previous BES workshops, these opportunities encompass recent advances in fundamental knowledge and focus on the experimental and computational methods needed to resolve major technical challenges for nuclear energy technologies. Through developing fundamental scientific insight as well as pushing the frontiers of modeling complex systems and probing the operation of materials and chemical systems in extreme environments, research motivated by the priorities identified here will further develop the promise, potential, and utilization of nuclear energy for a clean energy future. The PROs are as follows: (1) Master complex electronic structures to tailor thermochemical reactivity, transport, and microstructural evolution; (2) Interrogate and direct the physics and chemistry underpinning next-generation coolants and solvents; (3) Elucidate and control the underlying physics and chemistry of interfaces in complex nuclear environments; (4) Bridge multi-fidelity multi-resolution experiments, computational modeling, and data science to control dynamic behavior; and (5) Harness artificial intelligence to design inherently resilient condensed phases.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Artificial Intelligence for Energy Systems Cybersecurity

Artificial intelligence and machine learning systems have the potential to influence the future design and implementation of cybersecurity systems for the power grid. These systems may enhance the overall operation of the power system by leveraging and making sense of massive amounts of data. However, we must also understand how AI/ML will need to be protected from cyber threat actors. We discuss the existing insights the NREL team has developed using AI/ML systems and then present resources including ESIF and the Cyber Energy Emulation Platform that can be used to generate training data and insights. We end by offering suggestions on priority research paths for AI in cybersecurity.

artificial intelligence↗

DOE OSTI2026

Validating a Dynamic PWR Safety and Security Model?

Nuclear power plants (NPPs) are assessed for safety and security using separate models that cannot capture how an attacker's decisions and a plant's response unfold together in real time, leaving regulators and operators without a complete picture of true plant vulnerability. Traditional probabilistic risk assessment (PRA) methods treat adversarial events as fixed initiators with predetermined outcomes, and are structurally incapable of representing the time-dependent interplay between physical security events, safety system response, and operator mitigative actions. At Idaho National Laboratory (INL), I contributed to the development and validation of Modeling and Analysis for Safety and Security using the Dynamic EMRALD Framework (MASS-DEF). Where static PRA relies on event-tree logic that cannot evolve mid-scenario, MASS-DEF couples a time-dependent dynamic PRA tool EMRALD (Event Modeling Risk Assessment using Linked Diagrams) with attack simulation software, allowing attacker behavior, plant system states, and operator actions to interact across time. My work focused on validating a general Pressurized Water Reactor (PWR) model. I traced model logic against PWR plant to identified errors in logic and confirm accuracy. I then built and tested attack scenarios against a general PWR model to verify that the model produced expected outcomes across all logical pathways. I also contributed a section to a related technical paper applying the same EMRALD platform to radiation dose modeling. Results show that MASS-DEF can quantitatively demonstrate that many plants exceed their regulatory security thresholds. This demonstrated margin provides a technically defensible basis for reducing the number of guards without compromising regulatory compliance. Physical security costs represent roughly 10% of annual operating budgets, making such reductions directly meaningful to INL's mission of sustaining existing commercial NPPs. This internship strengthened my understanding of nuclear systems, probabilistic modeling, and technical writing, and has solidified my pursuit of a career at a national laboratory.

98 - NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL↗

DOE OSTI2022

Upgrade and Operation of the ATLAS Radiation Interlock System (ARIS)

ATLAS (the Argonne Tandem Linac Accelerator Sys-tem) is a superconducting heavy ion accelerator which can accelerate nearly all stable, and some unstable, iso-topes between hydrogen and uranium. Prompt radiation fields from gamma and or neutron are typically below 1 rem/hr at 30 cm, but are permitted up to 300 rem/hr at 30 cm. The original ATLAS Radiation Interlock System (ARIS), hereafter referred to as ARIS 1.0 was installed 30 years ago. While it has been a functional critical safe-ty system, its age has exposed the facility to high risk of temporary shutdown due to failure of obsolete compo-nents. Topics discussed will be architecture, hardware improvements, functional improvements, and operation permitting personnel access to areas with low levels of radiation.

Blomberg, B. R.↗

DOE OSTI2021

Screening of alkali-assisted storage conditions to define the operational window of deacetylation within storage systems in the bioenergy supply chain

Overcoming biomass heterogeneity and associated recalcitrance to thermal, chemical, and enzymatic depolymerization is a necessary but challenging aspect of valorizing lignocellulosic biomass to fuels and chemicals. Here, this study explores how this recalcitrance can be reduced during the supply chain unit operation of storage, which is required for seasonally harvested agricultural residues to maintain constant throughput at a biorefinery. In this work, partial alkali pretreatment was performed just prior to corn stover entering storage, which had the benefit of providing a high pH environment entering storage such that soluble sugars were preserved and saponified acetyl groups in hemicellulose. This work investigated a range of viable conditions where saponification and preservation occur simultaneously by varying the moisture content (40% and 60%) and concentrations of sodium hydroxide (low and high) during aerobic and anaerobic storage. Anaerobic conditions preserved overall dry matter below 5% in the three scenarios evaluated, and the highest alkali loading solubilized up to 15% lignin, 18% xylan, and 50% of acetate, meanwhile doubling the extractable components. Scanning electron microscopy images highlighted potential physical impacts including cell-wall disruption near vascular bundles, pitting within parenchyma cells, and cell-wall distortion. Techno-economic assessment indicated that this storage approach and associated logistics system is economically competitive with a conventional approach using low-moisture bales.

09 BIOMASS FUELS↗

DOE OSTI2025

Digital Assurance Checklist for Homeowners and Installers

This document provides a comprehensive Digital Assurance Checklist for securing behind-the-meter energy assets, focusing on both installers and homeowners. As distributed energy resources (DERs) such as solar PV and battery storage become integral to residential energy systems, cybersecurity emerges as a critical component of reliability and safety. The guide outlines actionable steps for installers during pre-installation, commissioning, and post-installation phases, emphasizing practices like network segmentation, credential management, firmware validation, and homeowner education. For homeowners, the document introduces a tiered approach to cyber hygiene—from essential measures like strong Wi-Fi credentials and automatic updates to advanced strategies such as network segmentation, DNS filtering, and intrusion detection. By adopting these practices, stakeholders can mitigate cyber risks, safeguard energy infrastructure, and ensure resilient, secure operation of DER systems. Additional resources and references to industry standards are included to support implementation.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2026

Digital Assurance Checklist for Homeowners and Installers

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

Implementing a micro-operation cache with compaction

Systems, apparatuses, and methods for compacting multiple groups of micro-operations into individual cache lines of a micro-operation cache are disclosed. A processor includes at least a decode unit and a micro-operation cache. When a new group of micro-operations is decoded and ready to be written to the micro-operation cache, the micro-operation cache determines which set is targeted by the new group of micro-operations. If there is a way in this set that can store the new group without evicting any existing group already stored in the way, then the new group is stored into the way with the existing group(s) of micro-operations. Metadata is then updated to indicate that the new group of micro-operations has been written to the way. Additionally, the micro-operation cache manages eviction and replacement policy at the granularity of micro-operation groups rather than at the granularity of cache lines.

Kotra, Jagadish B.↗

DOE OSTI2020

Chapter 7: Predictive Analytics in Future Power Systems: A Panorama and State-of-the-Art of Deep Learning Applications

The challenges surrounding the optimal operation of power systems are growing in various dimensions, due in part to increasingly distributed energy resources and a progression towards large-scale transportation electrification. Currently, the increasing uncertainties associated with both renewable energy generation and demand are largely being managed by increasing operational reserves - potentially at the cost of suboptimal economic conditions - in order to maintain the reliability of the system. This chapter looks at the big picture role of forecasting in power systems from generation to consumption and provides a comprehensive review of traditional approaches for forecasting generation and load in various contexts. This chapter then takes a deep dive into the state-of-the-art machine learning and deep learning approaches for power systems forecasting. Furthermore, a case study of multi-time-horizon solar irradiance forecasting using deep learning is discussed in detail. Smart grids form the backbone of the future interdependent networks. For addressing the challenges associated with the operations of smart grid, development and wide adoption of machine learning and deep learning algorithms capable of producing better forecasting accuracies is urgently needed. Along with exploring the implementation and benefits of these approaches, this chapter also considers the strengths and limitations of deep learning algorithms for power systems forecasting applications. This chapter, thus, provides a panoramic view of state-of-the-art of predictive analytics in power systems in the context of future smart grid operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

A Framework for Closed-Loop Optimization of an Automated Mechanical Serial-Sectioning System via Run-to-Run Control as Applied to a Robo-Met.3D

Optimization of automated data collection is gaining increased interest for the purposes of enabling closed-loop self-correcting systems that inherently maximize operational efficiencies and reduce waste. Many data collection systems have several variables which influence data accuracy or consistency and which can require frequent user interaction to be monitored and maintained. Operating upon a Robo-MET.3D™ automated mechanical serial-sectioning system, a run-to-run control algorithm has been developed to accelerate data collection and reduce data inconsistency. Here, using historical data amassed over a decade of experiments, a linear regression model of the deterministic system dynamics is created and used to employ a run-to-run control algorithm that optimizes selected system inputs to reduce operator intervention and increase efficacy while reducing variance of system output.

42 ENGINEERING↗

DOE OSTI2024

Weighted Composition Operators for Learning Nonlinear Dynamics

Operator theoretic methods in dynamical system have been dominated by the use of Koopman operators and their continuous time counterparts, such as Koopman Generators and Liouville Operators. The advantage gained from their use primarily stems from the ability to extract subspaces and eigenfunctions within a space of observables that are invariant with respect to the Koopman operator over that space. When this occurs, a dynamic mode decomposition of the systems state provides a linear model for the dynamical system. Not all Koopman operators have eigenfunctions that may be exploited in this manner. However, the framework can still be leveraged for approximations using other operators. In this setting, we present a different operator for the study of dynamical systems, the weighted composition operator. These operators are compact for a wide range of dynamics and spaces, and through their interactions with occupation kernels and vector valued kernels, they admit an estimation of the underlying dynamics. Here, this manuscript presents a new algorithm for the data driven study of dynamical systems from data, and also provides two numerical experiments where convergence is achieved as a proof of concept.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-Shot Transfer the Dynamic Response of Networked Systems

This article develops a deep graph operator network (DeepGraphONet) framework that learns to approximate the dynamics of a complex system (e.g., the power grid or traffic) with an underlying subgraph structure. Here, we build our DeepGraphONet by fusing the ability of graph neural networks to exploit spatially correlated graph information and deep operator networks to approximate the solution operator of dynamical systems. The resulting DeepGraphONet can then predict the dynamics within a given short/medium-term time horizon by observing a finite history of the graph state information. Furthermore, we design our DeepGraphONet to be resolution independent. That is, we do not require the finite history to be collected at the exact/same resolution. In addition, to disseminate the results from a trained DeepGraphONet, we design a zero-shot learning strategy that enables using it on a different subgraph. Finally, empirical results on the transient stability prediction problem of power grids and traffic flow forecasting problem of a vehicular system illustrate the effectiveness of the proposed DeepGraphONet.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Optimal Planning and Operation of Multi-Frequency HVac Transmission Systems

Low-frequency high-voltage alternating-current (LF-HVac) transmission scheme has been recently proposed as an alternative solution to conventional 50/60-Hz HVac and high-voltage direct-current (HVdc) schemes for bulk power transfer. This paper proposes an optimal planning and operation for loss minimization in a multi-frequency HVac transmission system. In such a system, conventional HVac and LF-HVac grids are interconnected using back-to-back (BTB) converters. The dependence of system MW losses on converter dispatch as well as the operating voltage and frequency in the LF-HVac is discussed and compared with that of HVdc transmission. Based on the results of the loss analysis, multi-objective optimization formulations for both planning and operation stages are proposed. The planning phase decides a suitable voltage level for the LF-HVac grid, while the operation phase determines the optimal operating frequency and power dispatch of BTB converters, generators, and shunt capacitors. A solution approach that effectively handles the variations of transmission line parameters with the rated voltage and operating frequency in the LF-HVac grid is proposed. The proposed solutions of the planning and operation stages are evaluated using a multi-frequency HVac system. The results show a significant loss reduction and improved voltage regulation during a 24-hour simulation.

Nguyen, Quan H.↗