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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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DOE OSTI2020

An Uncertainty Management Framework for Integrated Gas-Electric Energy Systems

In many parts of the world, electric power systems have seen a significant shift toward generation from renewable energy and natural gas. Because of their ability to flexibly adjust power generation in real time, gas-fired power plants are frequently seen as the perfect partner for variable renewable generation. However, this reliance on gas generation increases interdependence and propagates uncertainty between power grids and gas pipelines and brings coordination and uncertainty management challenges. To address these issues, we propose an uncertainty management framework for uncertain, but bounded gas consumption by gas-fired power plants. The admissible ranges are computed based on a joint optimization problem for the combined gas and electricity networks, which involves chance-constrained scheduling for the electric grid and a novel robust optimization formulation for the natural-gas network. This formulation ensures feasibility of the integrated system with a high probability, while providing a tractable numerical formulation. A key advance with respect to existing methods is that our method is based on a physically accurate, validated model for transient gas pipeline flows. Our case study benchmarks our proposed formulation against methods that ignore how reserve activation impacts the fuel use of gas power plants and only consider predetermined gas consumption. Here, the results demonstrate the importance of considering uncertainty to avoid operating constraint violations and curtailment of gas to the generators.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Flow Forecasting on New England’s Great River Hydro (Abstract)

Great River Hydro (GRH) operates 13 generating stations and 3 storage-only reservoirs along the Upper Connecticut River, draining 6,266 square miles. Managing the reservoirs requires coordination over a couple days. So far seasonal flow-forecast and medium range probabilistic flow forecast during high flow conditions are leading to satisfactory management. Great River Hydro is seeking technical assistance to evaluate potential improvements in inflow forecasting and scheduling accuracy, particularly during short- to medium- duration periods (1-10 days). Improved accuracy is anticipated to enhance the efficiency with which GRH utilizes water, improving GRH’s ability to hit the best priced hours throughout the system and enhancing revenues as a result.

13 HYDRO ENERGY↗

DOE OSTI2020

Combining model-based and model-free methods for stochastic control of distributed energy resources

Modern distribution systems are experiencing a fast transformation with the growing penetration of distributed energy resources (DERs). Along with the economic and environmental benefits of DERs, challenges arise to address the uncertainties caused by their inherent volatility. If properly coordinated, however, DERs have the potential to provide the controllability that grid operators need. Here, we propose a hierarchical control framework that combines the model-based and model-free methods for stochastic DER control in distribution systems. The upper-level scheduler considers a chance-constrained optimal power flow problem (model-based) that schedules DER setpoints to minimize the operational cost and maintain the operating reserve. The lower-level distributed DER controllers absorb real-time disturbances and uncertainties using the extremum seeking control (model-free) to achieve grid objectives. The combination of model-based and model-free methods allows us to take the advantages of both methods to effectively manage the uncertainty in distribution systems. The proposed work is demonstrated on the IEEE 13-node feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Deep Reinforcement Learning Enabled Physical-Model-Free Two-Timescale Voltage Control Method for Active Distribution Systems

Active distribution networks are being challenged by frequent and rapid voltage violations due to renewable energy integration. Conventional model-based voltage control methods rely on accurate parameters of the distribution networks, which are difficult to achieve in practice. This paper proposes a novel physical-model-free two-timescale voltage control framework for active distribution systems. To achieve fast control of PV inverters, the whole network is first partitioned into several subnetworks using voltage-reactive power sensitivity. Then, the scheduling of PV inverters in the multiple sub-networks is formulated as Markov games and solved by a multi-agent soft actor-critic (MASAC) algorithm, where each subnetwork is modeled as an intelligent agent. All agents are trained in a centralized manner to learn a coordinated strategy while being executed based on only local information for fast response. For the slower time-scale control, OLTCs and switched capacitors are coordinated by a single agent-based SAC algorithm using the global information with considering control behaviors of the inverters. Particularly, the two-level agents are trained concurrently with information exchange according to the reward signal calculated from the data-driven surrogate model. Comparative tests with different benchmark methods on IEEE 33-and 123-bus systems and 342-node low voltage distribution system demonstrate that the proposed method can effectively mitigate the fast voltage violations and achieve systematical coordination of different voltage regulation assets without the knowledge of accurate system model.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Robust Scheduling of Networked Microgrids for Economics and Resilience Improvement

The benefits of networked microgrids in terms of economics and resilience are investigated and validated in this work. Considering the stochastic unintentional islanding conditions and conventional forecast errors of both renewable generation and loads, a two-stage adaptive robust optimization is proposed to minimize the total operating cost of networked microgrids in the worst scenario of the modeled uncertainties. By coordinating the dispatch of distributed energy resources (DERs) and responsive demand among networked microgrids, the total operating cost is minimized, which includes the start-up and shut-down cost of distributed generators (DGs), the operation and maintenance (O&M) cost of DGs, the cost of buying/selling power from/to the utility grid, the degradation cost of energy storage systems (ESSs), and the cost associated with load shedding. The proposed optimization is solved with the column and constraint generation (C&CG) algorithm. The results of case studies demonstrate the advantages of networked microgrids over independent microgrids in terms of reducing total operating cost and improving the resilience of power supply.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Synthesis of Correct Digital Controller Models from Specifications by Model Transformation (21-0320)

The design of high consequence controllers (in weapons systems, autonomy, etc.) that do what they are supposed to do is a significant challenge. Testing simply does not come close to meeting the requirements for assurance. Today circuit designers at Sandia (and elsewhere) typically capture the core behavior of their components using state models in tools such as STATEFLOW. They then check that their models meet certain requirements (e.g. “The system bus must not deadlock” or “both traffic lights at an intersection must not be green at the same time”) using tools called model checkers. If the model checker returns “yes” then the property is guaranteed to be satisfied by the model. However, there are several drawbacks to this industry practice: (1) there is a lot of detail to get right, this is particularly challenging when there are multiple components requiring complex coordination (2) any errors returned by the model checker have to be traced back through the design and fixed, necessitating rework, (3) there are severe scalability problems with this approach, particularly when dealing with concurrency. All this places high demands on the designers who now face not only an accelerated schedule but also controllers of increasing complexity. This report describes a new and fundamentally different approach to the construction of safety-critical digital controllers. Instead of directly constructing a complete model and then trying to verify it, the designer can start with an initial abstract (think “sketch”) model plus the requirements, from which a correct concrete model is automatically synthesized. There is no need for post-hoc verification of required functional properties. Having tool to carry this out will significantly impact the nation’s ability to ensure the safety of high-consequence digital systems. The approach has been implemented in a prototype tool, along with a suite of examples, including ones that reflect actual problems faced by designers. Our approach operates on a variant of Statecharts developed at Sandia called Qspecs. Statecharts are a widely used formalism for developing concurrent reactive systems, supporting scalability through allowing state models containing composite states, which are the serial or parallel composition of substates which can themselves contain statecharts. Statecharts enable an incremental style of development, in which states are progressively refined to incorporate greater detail in an incremental model of software development. Our approach formulates a set of constraints from the structure of the models and the requirements and propagates these constraints to a fixpoint. The solution to the constraints is an inductive invariant along with guards on the transitions. We also show how our approach extends to implementation refinement, decomposition, composition, and elaboration. We currently handle safety requirements written in LTL (Linear Temporal Logic)

42 ENGINEERING↗

DOE OSTI2026

Multi-Agent Control Planes for Quantum Networks: A Scalable Architecture for Autonomous Quantum Internet Management

Quantum networks are expected to enable distributed quantum computing, secure communication, and global entanglement distribution. However, operating such networks presents significant challenges, including stochastic quantum processes, fragile entanglement resources, dynamic topology, and cross-layer control requirements. Current quantum network control architectures largely rely on centralized or hierarchical controllers inspired by classical software-defined networking (SDN). While effective for small testbeds, these approaches face scalability, latency, and reliability limitations as quantum networks grow. This paper proposes a multi-agent control plane architecture for quantum networks. In this design, intelligent software agents operate at quantum nodes, repeaters, and orchestration layers, collectively managing entanglement generation, routing, purification, and scheduling. The distributed intelligence of the agent system allows the network to adapt dynamically to quantum hardware variability and environmental noise. We argue that multi-agent systems provide significant advantages over centralized control approaches, including scalability, resilience, local autonomy, and real-time adaptation. The paper discusses architectural design principles, agent coordination mechanisms, and research challenges in deploying multi-agent control planes for the emerging quantum Internet.

Alnajjar, Anees [ORNL] (ORCID:0000000237101601)↗

DOE OSTI2026

Shaping the FutureWorkforce: Challenges and Lessons Learned in HPC Education from National Labs and Computing Centers

Workforce training at national laboratories and computing centers is essential and typically falls into two categories: foundational training for newcomers and advanced training for experienced users. Foundational topics—such as version control, build systems, and basic HPC usage—are largely transferable across institutions, while cluster-specific training varies due to differences in hardware, job schedulers, and local workflows. Training on emerging technologies is split between hardware-specific content and broadly applicable programming paradigms. Here, to reduce redundancy and increase impact, national labs, computing centers, and vendors are collaborating through initiatives like the HPC Training Working Group to share best practices, co-develop materials, and broaden outreach. These coordinated efforts aim to make HPC training more accessible, scalable, and consistent across the community.

HPC↗

DOE OSTI2022

Arbitrage and Capacity Firming in Coordination with Day-Ahead Bidding of a Hybrid PV Plant

A hybrid PV plant (HPP) combines a photovoltaic (PV) plant with a battery energy storage system (BESS), which is considered a promising step towards the future of renewable power plants by the U.S. Department of Energy. When the renewable penetration reaches a significant level, a hybrid PV plant can bid in as a controllable thermal plant in the future electricity market. In this study, a bidding and BESS scheduling model is proposed for the HPP. The robust optimization (RO) technique has been utilized to identify the worst-case scenario of uncertainties during the bidding process. To address the overly conservative issue of the single-stage RO, we have decoupled the BESS schedule for arbitrage and PV capacity firming by a two-stage RO formulation. By comparing the output of single-stage RO and two-stage RO, the two-stage RO bids and schedules in a more aggressive manner, which increases the income of HPP. Also, the penalty of under-generation is considered in our model so that the day-ahead bidding decision and arbitrage schedules can be adjusted based on the potential UNDER-GENERATION penalty. Because the proposed model is non-convex and contains multi-stages, the Column-and-Constraint Generation (C&CG) algorithm is applied to the model as the solution. The proposed model has shown better economic performance compared to a state-of-art single-stage bidding method in case studies.

BESS scheduling↗

DOE OSTI2022

Arbitrage and Capacity Firming in Coordination with Day-Ahead Bidding of a Hybrid PV Plant: Preprint

BESS scheduling↗

DOE OSTI2024

Real-Time Gear-Shift Optimization for an Autonomous Wheel Loader

Off-road vehicles, such as wheel loaders, consume a significant amount of fuel during the transportation of materials. The gear-shifting process is crucial in fuel savings for transportation, and therefore, optimization of gearshifts is important for vehicle control. For an autonomous off-road vehicle, there is potential for more fuel savings by proper coordination of gearshift optimization and optimization of other control inputs. This brief proposes a new method for integrating gear-shifting into transportation optimization for an autonomous wheel loader to minimize fuel consumption. Furthermore, tests conducted on short loading cycles show that this method can save around 10%–20% of fuel on average compared with a conventional gearshift-scheduling method.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Implementation and Operation of Power Flow Control Solutions for Transmission Systems

Power-flow controls (PFCs) technologies provide a suite of alternatives to more-efficiently direct the flow of power on the grid, improving flexibility and enabling the grid to be more responsive and resilient. PFCs can be used in many cases to mitigate overloads and consequently defer the installation of grid-expansion projects. They can also be suitable options in situations where new transmission lines cannot be built because of environmental constraints, land use, or other types of restrictions. Other uses include mitigation of loop or unintended flows, forcing of contractual or scheduled flows, reduction of congestion, and mitigation solutions during large maintenance or construction projects. While many studies have demonstrated the potential economic benefits of PFC for different types of applications, the implementation implications of using multiple PFCs for system operation and reliability have not been analyzed in detail. The lack of information and practical experience on these aspects hinders adoption of this technology. One use case would be that multiple PFCs are required to coordinate congestion management. In such cases, multiple PFCs are deployed in a meshed power system to control power flows across a wide area. The challenges are associated with PFC interaction—namely, the self and mutual power-flow sensitivity of each PFC, which must be accounted for in the calculation of the respective operating setpoints. This report analyzes implementation and operation that must be considered in evaluating transmission solutions using PFCs. The report first analyzes attributes with which PFC-based solutions should comply, based on the objective and characteristics of each particular use case. It then describes different approaches for coordinated control of multiple PFC devices for different applications, including options to avoid adverse interactions and reduce risks due to inappropriate coordination. Control and operation are illustrated through case studies performed on both generic and actual power system models. This report provides insights for transmission planners and operation engineers about the implementation of PFC-based transmission solutions; consequently, it will help them make informed decisions about the adoption of the technology.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Redesign of the Timeline Generator at Fermilab using a web-based Flutter application, GraphQL API and an IOC

The control system at Fermilab is undergoing an evolution with a shift towards web-based applications with connections to the EPICS infrastructure. The Timeline Generator (TLG) is an application that serves to coordinate events across the lab using different timing links. These links include the Tevatron clock (TCLK), a 10 MHz serial link with events encoded at 20Hz and Machine Data (MDAT), a communication link with states encoded at 720Hz. This paper covers the redesign of the major components of the TLG. This includes a web-based Flutter application for building timelines. A placement service is in use that has a GraphQL interface and uses a timeline input to compute a schedule of events and states. The Flutter application sends this computed schedule to the TLG IOC via a GraphQL interface to the Data Pool Manager (DPM). The TLG IOC runs on an Arria FPGA, the Accelerator Clock Generator (ACLK-GEN), which is responsible for writing the events and states on to the different timing links.

Carmichael, Linden [Fermilab]↗

DOE OSTI2024

Connected Thermostat Alternatives for Room Air Conditioners and Minisplit Heat Pumps

The availability of smart, connected thermostats has improved climate control, energy efficiency, and grid demand-response programs for central HVAC systems. However, a significant gap exists in addressing integrated control systems for point-source heating and cooling systems such as window air-conditioners (window ACs) and mini-split heat pumps (MSHPs). This report examines the emerging market of third-party connected thermostats tailored for these systems, focusing on their effectiveness, reliability, and potential barriers to adoption.This study evaluates several commercially available products designed for room ACs and MSHPs through a series of laboratory tests. While these infrared-based (IR-based) thermostats offer remote temperature control and scheduling via mobile apps, our findings reveal that none are seamless, with reliability of basic functions being a critical factor. Promising features include integration of indoor air quality metrics and time-of-use pricing, but the latter are not yet available in the U.S. Barriers to broad user acceptance include non-seamless setup processes, challenges in thermostat placement, and unclear product differentiation. There is a pressing need for research and development in enabling MSHPs and central thermostats to coordinate, enhancing energy savings and comfort in retrofit applications. This study underscores the importance of further innovation in connected thermostat technology to address the diverse needs of single-zone HVAC systems and promote efficient energy management in households.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Evidence of Completion of Milestone 3: Experimental Testbed

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Redesign of the Timeline Generator at Fermilab using a web-based Flutter Application, GraphQL API and an IOC

Redesign of the Timeline Generator at Fermilab using a web-based Flutter application, GraphQL API and an IOC ABSTRACT = The control system at Fermilab is undergoing an evolution with a shift towards web-based applications with connections to the EPICS infrastructure. The Timeline Generator (TLG) is an application that serves to coordinate events across the lab using different timing links. These links include the Tevatron clock (TCLK), a 10 MHz serial link with events encoded at 20Hz and Ma-chine Data (MDAT), a communication link with states encoded at 720Hz. This paper covers the redesign of the major components of the TLG. This includes a web-based Flutter application for building timelines. A placement service is in use that has a GraphQL interface and uses a timeline input to compute a schedule of events and states. The Flutter application sends this computed schedule to the TLG IOC via a GraphQL interface to the Data Pool Manager (DPM). The TLG IOC runs on an Arria FPGA, the Accelerator Clock Generator (ACLK-GEN), which is responsible for writing the events and states on to the different timing links.

Carmichael, Linden [Fermilab]↗

DOE OSTI2025

Evidence of Completion of Milestone 3: Experimental Testbed

Construction and deployment of the experimental testbed encountered scheduling delays, and completion was subsequently further delayed by curtailment of LANL operations due to the COVID-19 pandemic. Between March and October, LANL was in a state of “Limited Operations” such that only certain “mission-critical” work was performed with approximately 25% on-site staffing. In spite of this, much of the lab space preparation was executed and the five initial model house” units were constructed and installed during this period. Still, the pace of construction was impacted by difficulty in coordinating personnel and minimizing contact between workers. Currently, LANL is in the mode of “Normal Operations with Maximum Telework,” which continues to limit the availability of on-site personnel. Nevertheless, Milestone 3 is now complete. This document describes the experiment as it is deployed and provides status for each task for this milestone.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Exascale workflow applications and middleware: An ExaWorks retrospective

Exascale computers offer transformative capabilities to combine data-driven and learning-based approaches with traditional simulation applications to accelerate scientific discovery and insight. However, these software combinations and integrations are difficult to achieve due to the challenges of coordinating and deploying heterogeneous software components on diverse and massive platforms. Here, we present the ExaWorks project, which addresses many of these challenges. We developed a workflow Software Development Toolkit (SDK), a curated collection of workflow technologies that can be composed and interoperated through a common interface, engineered following current best practices, and specifically designed to work on HPC platforms. ExaWorks also developed PSI/J, a job management abstraction API, to simplify the construction of portable software components and applications that can be used over various HPC schedulers. The PSI/J API is a minimal interface for submitting and monitoring jobs and their execution state across multiple and commonly used HPC schedulers. We also describe several leading and innovative workflow examples of ExaWorks tools used on DOE leadership platforms. Furthermore, we discuss how our project is working with the workflow community, large computing facilities, and HPC platform vendors to address the requirements of workflows sustainably at the exascale.

97 MATHEMATICS AND COMPUTING↗