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

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Real Time - Optimal Power Flow Based Distributed Energy Resources Management System (DERMS) (CRADA Number CRD-20-16909 Final Report)

The integration of behind-the-meter distributed energy resources (DERs) into distribution systems brings transformative changes to power systems. This requires operators and planners to find solutions to modernize electric grids and to effectively manage DERs for grid services. NREL developed novel DER management algorithms (referred to as Real-Time Optimal Power Flow, RT-OPF) through U.S. Department of Energy (DOE)-funded efforts, including Advanced Research Projects Agency–Energy (ARPA-E) Network Optimized Distributed Energy Systems (NODES) funding. This cutting-edge control technology aims to modernize distribution systems with large amounts of DER integration, which will help utilities solve issues brought by renewable integration and build resilient and renewable-based electric grids nationwide. Utilidata worked with NREL to investigate the commercialization opportunity of this RT-OPF-based distributed energy resource management system (DERMS). In this project, NREL performs the technology transfer of the RT-OPF to Utilidata to help them fully understand the RT-OPF solution, to identify potential engineering hurdles, and to assess the expected commercial value of various RT-OPF use cases and deployment. The technology transfer work includes two major tasks. First, NREL performs an in-depth knowledge transfer of the entire RT-OPF solution to Utilidata to help them gain an extensive and detailed understanding of the complete RT-OPF solution. In this task, NREL provides exhaustive information (e.g., documentation, code packages, laboratory and field trial data, performance results) while conducting in-depth training sessions to provide a thorough explanation of the entire solution. NREL hosts meetings to present different topics related to the RT-OPF solution, and question-and-answer sessions are included in each meeting to better explain the RT-OPF-related work. Second, the RT-OPF simulations are performed in a laboratory environment. The main objectives of this task are to walk through with Utilidata engineers how to set up a simulation of RT-OPF, identifying each RT-OPF code block/component in operation, learning how these components interact with each other, and eventually running RT-OPF simulations under various system conditions. This task helps Utilidata engineers understand performance limitations and constraints while also quantifying the commercial value of the RT-OPF for different use cases. Based on these two tasks, Utilidata engineers should be able to define and prioritize the next steps of RT-OPF implementation with an eye toward commercial success and scalability of the solution. The next steps are expected to be part of a new project following the conclusion of this project.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Real Time-Optimal Power Flow-Based Distributed Energy Resource Management System (DERMS)

This project aims to promote lab-proven clean energy technology to commercially scalable versions of the technology, integrate the technology with broader systems, provide extended performance data, and validate the manufacturability and reliability of the technology. The lab-proven technology, RT-OPF DERMS, was developed and validated through previous U.S. Department of Energy-funded efforts, including Advanced Research Projects Agency-Energy funding under the Network Optimized Distributed Energy Systems program and Holy-Cross Energy High Impact Project. In the Advanced Research Projects Agency-Energy Network Optimized Distributed Energy Systems project, the RT-OPF DERMS was developed and implemented in multiple hardware platforms, demonstrating its performance and capabilities in the lab and field environments. The technology was also evaluated and matured via a participation in the U.S. Department of Energy I-Corps program, whose goal is to pair teams of researchers with industry mentors for an intensive 2-month training in which the researchers define technology value propositions, conduct customer discovery interviews, and develop viable market pathways for their technologies. These activities indicate the high technology maturity and Technology Readiness Level of the RT-OPF DERMS.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

DERMS-RT (Distributed Energy Resource Management Solution using Real-Time Optimization) [SWR-20-46]

DERMS-RT provides a distributed energy resource management solution using real-time optimization to control different types of distributed energy resources (DERs) — such as rooftop PVs, battery energy storage systems, HVAC loads, electric water heater loads, and EV charging loads, and use these DERs to provide distribution voltage regulation and peak demand management services. The control approach implemented by DERM-RT is developed based on the real-time optimal power flow and distributed control previously developed by NREL researchers. The software codes are designed to provide modular DER management solution that can be easily applied to control heterogeneous DERs in distribution grids in coordination with centralized utility management systems. Also, DERMS-RT provides application programming interface (API) to extract useful grid model information from an open-source power system simulation software, use the information to solve the optimization problem, and apply the optimal set-points to the controlled DERs. In this way, DERMS-RT provides a plug-and-play function for implementing the real-time DER control on different utility system models, and it can be used as a simulation testbed to demonstrate the impact of real-time DER optimization on improving grid operations.

Ding, Fei↗
DOE OSTI2021

A Circularity Assessment for Silicon Solar Panels Based on Dynamic Material Flow Analysis

This paper examines the impacts of design, operational, and end-of-life (EOL) waste pathways’ parameters on material circularity in silicon solar photovoltaic (PV) modules. Dynamic material flow analysis (DMFA) quantifies time-series material flows through systems’ life cycle stages to identify hotspots of waste generation, estimate resource needs in the future, and guide sustainable material management. We introduce a DMFA framework based on U.S. electricity demand for the period 2000-2100 to assess stocks and flows of bulk PV materials (i.e., solar glass and aluminum frames). We apply the model to a range of scenarios to understand how material demands depend on selected PV-related parameters, different material circularity strategies, and recent module design trends (e.g., bifacials, large-format-high power modules). Our results enable advanced planning for future materials needs and provide insight into potential opportunities to minimize material waste.

circular economy↗
DOE OSTI2021

Peak Load Management in Distribution Systems Using Legacy Utility Equipment and Distributed Energy Resources: Preprint

The ability to perform peak load management in distribution systems has several benefits for utilities, including reduced demand charges and improved reliability, efficiency, and utilization of the network infrastructure. This paper demonstrates the coordinated operation of an advanced distribution management system (ADMS) and a distributed energy resource management system (DERMS) to achieve peak load management using a realistic laboratory test bed. A commercial ADMS reduces the peak demand by reducing system voltages using a dynamic voltage regulation (DVR) application. A prototype DERMS—based on real-time optimal power flow—controls distributed battery energy storage systems to further reduce the feeder power. Results from the experiments conducted using a model of a real distribution feeder show that the coordinated operation of the ADMS and DERMS is effective in accomplishing peak load management.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

Peak Load Management in Distribution Systems Using Legacy Utility Equipment and Distributed Energy Resources

The ability to perform peak load management in distribution systems has several benefits for utilities, including reduced demand charges and improved reliability, efficiency, and utilization of the network infrastructure. This paper demonstrates the coordinated operation of an advanced distribution management system (ADMS) and a distributed energy resource management system (DERMS) to achieve peak load management using a realistic laboratory test bed. A commercial ADMS reduces the peak demand by reducing system voltages using a dynamic voltage regulation (DVR) application. A prototype DERMS-based on real-time optimal power flow-controls distributed battery energy storage systems to further reduce the feeder power. Results from the experiments conducted using a model of a real distribution feeder show that the coordinated operation of the ADMS and DERMS is effective in accomplishing peak load management.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Comparison of Deterministic and Statistical Models for Water Quality Compliance Forecasting in the San Joaquin River Basin, California

Model selection for water quality forecasting depends on many factors including analyst expertise and cost, stakeholder involvement and expected performance. Water quality forecasting in arid river basins is especially challenging given the importance of protecting beneficial uses in these environments and the livelihood of agricultural communities. In the agriculture-dominated San Joaquin River Basin of California, real-time salinity management (RTSM) is a state-sanctioned program that helps to maximize allowable salt export while protecting existing basin beneficial uses of water supply. The RTSM strategy supplants the federal total maximum daily load (TMDL) approach that could impose fines associated with exceedances of monthly and annual salt load allocations of up to $1 million per year based on average year hydrology and salt load export limits. The essential components of the current program include the establishment of telemetered sensor networks, a web-based information system for sharing data, a basin-scale salt load assimilative capacity forecasting model and institutional entities tasked with performing weekly forecasts of river salt assimilative capacity and scheduling west-side drainage export of salt loads. Web-based information portals have been developed to share model input data and salt assimilative capacity forecasts together with increasing stakeholder awareness and involvement in water quality resource management activities in the river basin. Two modeling approaches have been developed simultaneously. The first relies on a statistical analysis of the relationship between flow and salt concentration at three compliance monitoring sites and the use of these regression relationships for forecasting. The second salt load forecasting approach is a customized application of the Watershed Analysis Risk Management Framework (WARMF), a watershed water quality simulation model that has been configured to estimate daily river salt assimilative capacity and to provide decision support for real-time salinity management at the watershed level. Analysis of the results from both model-based forecasting approaches over a period of five years shows that the regression-based forecasting model, run daily Monday to Friday each week, provided marginally better performance. However, the regression-based forecasting model assumes the same general relationship between flow and salinity which breaks down during extreme weather events such as droughts when water allocation cutbacks among stakeholders are not evenly distributed across the basin. A recent test case shows the utility of both models in dealing with an exceedance event at one compliance monitoring site recently introduced in 2020.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Streaming Readout and Data-Stream Processing With ERSAP

With the exponential growth in the volume and complexity of data generated at high-energy physics and nuclear physics research facilities, there is an imperative demand for innovative strategies to process this data in real or near-real-time. Given the surge in the requirement for high-performance computing, it becomes pivotal to reassess the adaptability of current data processing architectures in integrating new technologies and managing streaming data. This paper introduces the ERSAP framework, a modern solution that synergizes flow-based programming with the reactive actor model, paving the way for distributed, reactive, and high performance in data stream processing applications. Additionally, we unveil a novel algorithm focused on time-based clustering and event identification in data streams. The efficacy of this approach is further exemplified through the data-stream processing outcomes obtained from the recent beam tests of the EIC prototype calorimeter at DESY.

Vardan, Gyurjyan↗
DOE OSTI2026

A GPU-based compressible combustion solver for applications exhibiting disparate space and time scales

High-speed chemically active flows pose significant computational challenges due to their disparate space and time scales, with stiff chemistry often dominating simulation time. While modern scientific computing programs achieve exascale performance by leveraging graphics processing units (GPUs), existing GPU-based compressible combustion solvers face critical limitations in memory management, load balancing, and handling the highly localized nature of chemical reactions. To this end, we present a high-performance compressible reacting flow solver built on the AMReX framework and optimized for multi-GPU settings. Here, our approach addresses three GPU performance bottlenecks: memory access patterns through column-major storage optimization, computational workload variability via a bulk-sparse integration strategy for chemical kinetics, and multi-GPU load distribution for adaptive mesh refinement applications. The solver adapts existing matrix-based chemical kinetics formulations to multi-grid contexts. Using representative combustion applications, including 2D and 3D detonations and a 3D jet-in-crossflow configuration, we demonstrate 1.4–5× performance improvements over initial implementations on an in-house cluster of NVIDIA H100 GPUs, and near-ideal weak scaling on the Frontier supercomputer (Oak Ridge Leadership Computing Facility) with up to 1024 AMD Instinct MI250X GPUs. Roofline analysis reveals substantial improvements in arithmetic intensity for both convection (∼ 10 ×) and chemistry (∼ 4 ×) routines, confirming efficient utilization of GPU memory bandwidth and computational resources.

42 ENGINEERING↗
DOE OSTI2021

Phasor-Based Control for Scalable Solar PV Integration

This project demonstrated that solar PV can be recruited to stabilize the grid, smooth out disturbances, manage power flows, and assist circuit switching operations. It developed a radically new, layered control framework for Distributed Energy Resources (DER) to act in response to real-time, measured conditions on their local distribution circuit, rather than waiting for a price signal to indicate preferred behavior. By enabling resources to act as good citizens on the electric grid, Phasor-Based Control (PBC) facilitates arbitrarily high solar penetration levels.

14 SOLAR ENERGY↗
DOE OSTI2021

Phasor-Based Control for Scalable Solar PV Integration (Final Technical Report)

This project demonstrated that solar PV can be recruited to stabilize the grid, smooth out disturbances, manage power flows, and assist circuit switching operations. It developed a radically new, layered control framework for Distributed Energy Resources (DER) to act in response to real-time, measured conditions on their local distribution circuit, rather than waiting for a price signal to indicate preferred behavior. By enabling resources to act as good citizens on the electric grid, Phasor-Based Control (PBC) facilitates arbitrarily high solar penetration levels.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Environmental Flow Requirements from FERC Licenses Across the US

Environmental flow requirements included in Federal Energy Regulatory Commission (FERC) hydropower licenses are important for balancing natural properties and benefits of river ecosystems (e.g., healthy species, recreation, water supply, flood control) supporting hydropower production. In some cases, environmental flow requirements may limit operational flexibility given current operational schemes and make a hydropower plant less able to provide power to the electric grid on demand. Hydropower plants may gain some flexibility as hydropower scheduling time periods are made to be more responsive to the short-term needs of an energy grid increasingly reliant on intermittent renewables. However, many flow requirements focus on the daily, monthly, or seasonal flow fluctuations which matches the time scale of most paradigms linking flow alterations to the health of river ecosystems. This dataset seeks to provide a greater understanding of how flexibility in environmental requirements can be leveraged to create positive outcomes for both the power system and the environment. It contains information on environmental flow requirements from the Protection, Mitigation, and Enhancement section of 50 randomly selected FERC licenses: 25 issued from 1998-2013 that were also included in the ORNL Mitigation Database (Schramm et al. 2015) and 25 issued from 2014-present. The information on environmental flow requirements was extracted from the PM&E section of 50 randomly selected FERC licenses: 25 issued from 1998-2013 that were also included in the ORNL Mitigation Database (Schramm et al. 2015) and 25 issued from 2014-present. The flow requirements were then categorized into flow augmentation categories based on whether the license stated a specific water management purpose for the given requirement called augmentation categories (i.e., fisheries or habitat, recreation or boating, industry, and general or unspecified; Table B). Requirements were also grouped into flow type categories (e.g., minimum flow rate, maximum flow rate, ramping rate). Additional information related to flow requirements such as the augmentation time-period and whether the flow rate was continuous (i.e., condition must be present at all-times) or instantaneous (i.e., condition present at a point in time) was also extracted from the licenses. Some licenses had specific flow requirements based on whether the project was in a wet, dry, or normal water year. If that information was presented in the license, it was also included in the data set. The location within the project was noted, hereafter, zone, in the dataset for flow requirements relating to specific areas of hydropower projects (Dam, Powerhouse, Bypass Reach). Maximum discharge capacities of hydropower facilities were also extracted from both the Existing Hydropower Assets (EHA) data set and the National Inventory of Dams (NID) databases. Each facility was coded with project identification codes from the EHA dataset to facilitate cross-referencing between datasets.

13 HYDRO ENERGY↗
DOE OSTI2022

Performance Evaluation of Distributed Energy Resource Management Algorithm in Large Distribution Networks

This paper presents performance evaluation of hierarchical optimization and control for distributed energy resource management system (DERMS) in large distribution networks via an advanced hardware-in-the-loop (HIL) platform. The HIL platform provides realistic testing in a laboratory envi-ronment, including the accurate modeling of a full-scale dis-tribution system of 11,000 nodes, the DERMS software con-troller, and 90 power hardware photovoltaics (PVs) and bat-tery inverters. The applied DERMS algorithm is designed based on a real-time optimal power flow algorithm and im-plemented with acceleration design that performs fast dis-patch of simulated PVs and real physical hardware DER devices every 4 seconds.

distributed energy management system (DERMS)↗
DOE OSTI2023

Scalable graphene sensor array for real-time toxins monitoring in flowing water

Abstract Risk management for drinking water often requires continuous monitoring of various toxins in flowing water. While they can be readily integrated with existing water infrastructure, two-dimensional (2D) electronic sensors often suffer from device-to-device variations due to the lack of an effective strategy for identifying faulty devices from preselected uniform devices based on electronic properties alone, resulting in sensor inaccuracy and thus slowing down their real-world applications. Here, we report the combination of wet transfer, impedance and noise measurements, and machine learning to facilitate the scalable nanofabrication of graphene-based field-effect transistor (GFET) sensor arrays and the efficient identification of faulty devices. Our sensors were able to perform real-time detection of heavy-metal ions (lead and mercury) and E. coli bacteria simultaneously in flowing tap water. This study offers a reliable quality control protocol to increase the potential of electronic sensors for monitoring pollutants in flowing water.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Hydrologic Regionalization under Data Scarcity: Implications for Streamflow Prediction

Continuous streamflow prediction is crucial in many applications of water resources planning and management. However, streamflow prediction is challenging, particularly in data-scarce regions. Here, we demonstrate an approach to regionalize the flow duration curve for predicting daily streamflow in the data-scare region of the central Himalayas. We developed a regression-based model to estimate streamflow at various segments of a flow duration curve by incorporating basin characteristics and climate variables. This study analyzes the sensitivities of proximity and characteristics between the donor (gauged) and receptor (ungauged) basins for time-series streamflow prediction. Our results show that regionalization techniques perform better in low to medium flows over high flows. Our findings are significant in the central Himalayan regional context to inform operational and management decisions in water sector projects like hydropower plants, which generally rely on low-to-medium streamflow information. Although the quantitative results are region-specific, the approach and insights are generalizable to the Himalayan region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Blockchain Empowered Provenance Framework for Sensor Identity Management and Data Flow Security in Fossil-based Power Plants

The overall goals of this project are divided into 3 important phases. First, a peer-to-peer SCADA network is to be established where identity profile of sensors from fossil-power plants will be stored in an immutable manner in order to verify data flow integrity in real time. Second, it is aimed to develop a Blockchain-based provenance platform to audit equipment operations along with data and process integrity violations. In this phase, various operational rules will be incorporated in the ledger on behalf of the operating sensor so its abnormal use can be easily detected. In addition, it is aimed to achieve provenance when the rate of transactions is exceedingly high, thus enabling to design a scalable provenance scheme that can work in real time when number of data collecting points or sensor units increase in fossil-based power plants. Finally, a working testbed will be developed to integrate the Blockchain platform with sensor-based fossil power plant comprising of Remote Terminal Units (RTU) and SCADA devices. Using multiple Raspberry-PIs mimicking the role of RTUs that are interfaced with multiple sensors devices will be deployed to serve as a critical component of fossil power plant and the performance metrics such as throughput, latency, bootstrapping time, etc., will be evaluated on the testbed.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Connected Traffic Signal Coordination Optimization Framework through Network-Wide Adaptive Linear Quadratic Regulator–Based Control Strategy

Traffic congestion in metropolitan areas causes several significant challenges, such as longer travel times, decreased productivity, increased fuel consumption and vehicle emissions, and even severe injuries during crashes. Traffic signal control is a management approach to reduce traffic congestion and allocate the appropriate right of way for safety and mobility efficiency, both in temporal and spatial domains. Here, this study proposes a network-wide adaptive signal control coordination optimization framework based on the linear quadratic regulator algorithm. The traffic flow conditions driven by signal control inputs are formulated based on their network-wide state-space representation. After modeling traffic control regulation constraints, an adaptive linear quadratic regulator algorithm is designed to maximize the network-wide total throughput under the current conditions. Optimal signal control split time durations for multiple intersections in the network are derived by solving the algebraic Riccati equation. Furthermore, the recursive least square parameter estimation method is employed to quantify dynamic traffic condition changes. To verify the effectiveness of this proposed signal control framework, both simulation and real-world experimental tests are conducted for multiple intersections in downtown Chattanooga, Tennessee, United States. In preparation for real-world experimental tests, pipelines for real-time data processing implementation and historical traffic flow data analysis are conducted. The test results demonstrate that the proposed control framework achieves a decrease in travel time by up to 19.4%, total time spent (TTS) by up to 11.9%, and relative queue balance (RQB) by up to 15.6%. The research findings indicate that the proposed signal control framework can be generalized to handle large scale signal control optimization network-wide.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Intelligent Partitioning based Fully Parallel AC Security-Constrained Optimal Power Flow

Today’s power grid is becoming more diverse and integrated with high-level distributed energy resources and smart control technologies that is creating a new set of grid management challenges in terms of large-scale, nonlinear, and non-convex problem modeling, complex and time-consuming computation, as well as difficult uncertainty handling. This project focused on solving a challenging multi-period security-constrained generation scheduling problem, which is of great importance for maximizing the social welfare of real-time dispatch, day-ahead market, as well as weekly planning of power systems. Our developed software explored parallel optimization algorithms for complex and realistic power system models, and develop fast, efficient, and robust grid optimization solutions on the high-performance computing platform that will enable increased grid economics, flexibility, resilience, as well as energy security in the United States.

24 POWER TRANSMISSION AND DISTRIBUTION↗