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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 181 records · Page 10

Platform for Remote Deployment and Training for Enhanced Building Operation Practices (Building Re-Tuning and On-going Commissioning)

While a building’s energy usage is driven largely by its design and use, building operator behavior has a strong influence on its energy consumption. This project developed and piloted a specific, data-driven coaching methodology to help operators understand how they can adjust operations and/or affect no/low-cost repairs or upgrades to their specific building HVAC systems to reduce energy consumption. Named BuildingCoach, the operational optimization method used is based on the Building Re-tuning approach developed by the Pacific Northwest National Laboratory. A building operations analytics market has matured over the past decade, though its potential to affect energy-saving changes has not been fully realized. Training operators to understand the methods for operational optimization with the explicit approach of using building-system performance data is hypothesized to create a more effective, longer lasting result in building energy efficiency, and this strategy is the fundamental premise of this project. With the support of an Industry Advisory Board, the project succeeded in developing materials and recruiting for and delivering three pilot cohorts. Deliverables included twenty-two self-paced training modules (accessed via a Learning Management System) and a web-based platform that includes access to real-time building system data and a repository for building system documentation. The project set out to have 100 participants from 50 buildings in three pilot cohorts. In the end, there were 28 participants from 17 buildings, i.e., a significant shortfall. The first two pilot cohorts had only two buildings in each, and this was partially due to difficulties in deploying the Building Operator Coaching Solution (“the BOCS”), which is technology that extracts the data from the controls network and presents it as prescribed for coaching. In the third cohort, the project team deployed the BOCS successfully to 13 buildings, the methodology was piloted as intended, and numerous opportunities for optimization were identified. The BuildingCoach business plan charts a path to an economically sustainable effort. However, even with a licensing model captured in the final version of the business plan, the scalability is still limited to keeping less than 1,000 buildings affected by 2033. Even so, there are unexplored paths to greater scalability that are being considered. CUNY BPL is working to perpetuate and grow the use of BuildingCoach. As of this writing, about twenty buildings have either been connected or will be connected with operators coached / to be coached in the NYC municipal portfolio, twelve buildings across four campuses in NY State will use BuildingCoach, a NY upstate county wishes for six or seven buildings to participate with the support of funding from NYSERDA, and others have also expressed interest. In the decades to come, there will be an increasing percentage of large and mid-sized buildings that incorporate automated system optimization (ASO), and the building operators’ role will shift to spend more time on maintenance and monitoring. Meanwhile, programs such as BuildingCoach will play a critical role in optimizing operations. And, regardless of the emergence of ASO, operators will still need to understand how their systems operate so that they can monitor them properly. Within that context, BuildingCoach is an important step towards operators’ understanding of efficient building system operations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Optimal Sizing of Movable Energy Resources for Enhanced Resilience in Distribution Systems: A Techno-Economic Analysis

This article introduces a techno-economic analysis aimed at identifying the optimal total size of movable energy resources (MERs) to enhance the resilience of electric power supply. The core focus of this approach is to determine the total size of MERs required within the distribution network to expedite restoration after extreme events. Leveraging distribution line fragility curves, the proposed methodology generates numerous line outage scenarios, with scenario reduction techniques employed to minimize computational burden. For each reduced multiple line outage scenario, a systematic reconfiguration of the distribution network, represented as a graph, is executed using tie-switches within the system. To evaluate each locational combination of MERs for a specific number of these resources, the expected load curtailment (ELC) is calculated by summing the load curtailment within microgrids formed due to multiple line outages. This process is repeated for all possible locational combinations of MERs to determine minimal ELC for each MER total size. For every MER total size, the minimal ELCs are determined. Finally, a techno-economic analysis is performed using power outage cost and investment cost of MERs to pinpoint an optimal total size of MERs for the distribution system. To demonstrate the effectiveness of the proposed approach, case studies are conducted on the 33-node and the modified IEEE 123-node distribution test systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Sensor Impact Evaluation and Verification Technical Advisory Group Meeting Minutes

This report provides the technical advisory group meeting minutes and summary of detailed discussions for future development of sensor impact evaluations and verifications. Methods for sensor configuration/deployment have critical impacts on energy-efficient building control and thermal comfort. However, traditional sensor techniques for building operation and fault detection and diagnostics (FDD) are not optimal in terms of energy efficiency and thermal comfort, and their global effects are not thoroughly investigated. In an effort to address and overcome this limitation, a 3 year project, Sensor Impact Evaluation and Verification, was proposed. The multi-laboratory team—the US Department of Energy’s Oak Ridge National Laboratory (ORNL), Pacific Northwest National Laboratory (PNNL), and the National Renewable Energy Laboratory (NREL)—is conducting early-stage R&D to provide technical supports and guidelines for sensor design in building/HVAC systems to optimize building energy use, FDD, thermal comfort, and grid efficiency. The overall goal of this project is to develop a framework that enables quantitative evaluation of the impact of sensors on building HVAC control, FDD, and consequently, building energy efficiency and thermal comfort.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Mobility Energy Productivity Evaluation of Prediction-Based Vehicle Powertrain Control Combined with Optimal Traffic Management

Transportation vehicle and network system efficiency can be defined in two ways: 1) reduction of travel times across all the vehicles in the system, and 2) reduction in total energy consumed by all the vehicles in the system. The mechanisms to realize these efficiencies are treated as independent (i.e., vehicle and network domains) and, when combined, they have not been adequately studied to date. This research aims to integrate previously developed and published research on Predictive Optimal Energy Management Strategies (POEMS) and Intelligent Traffic Systems (ITS), to address the need for quantifying improvement in system efficiency resulting from simultaneous vehicle and network optimization. POEMS and ITS are partially independent methods which do not require each other to function but whose individual effectiveness may be affected by the presence of the other. In order to evaluate the system level efficiency improvements, the Mobility Energy Productivity (MEP) metric is used. MEP specifically measures the connectedness of a system while accounting for time and energy externalities of modes that provide mobility in a given location. A SUMO model is developed to reflect real traffic patterns in Fort Collins, Colorado and data is collected by a probe SUMO vehicle which is validated against data collected on a real vehicle driving the same routes through the city. Individual vehicle and system level efficiencies are calculated using SUMO outputs for scenarios which integrate POEMS and ITS independently as well as jointly. Results from application of POEMS and ITS show improvement in energy consumption and travel times respectively when compared to the respective baseline scenarios. Our conclusion is that there are promising synergistic benefits to travel time and energy efficiency when POEMS and ITS are combined.

ADVANCED PROPULSION SYSTEMS↗

Market optimization and technoeconomic analysis of hydrogen-electricity coproduction systems

Decarbonization efforts across North America, Europe, and beyond rely on variable renewable energy sources such as wind and solar, as well as alternative fuels, such as hydrogen, to support the sustainable energy transition. These advancements have prompted a need for more flexibility in the electric grid to complement non-dispatchable energy sources and increased demand from electrification. Integrated energy systems are well suited to provide this flexibility, but conventional technoeconomic modeling paradigms neglect the time-varying dynamic nature of the grid and thus undervalue resource flexibility. In this work, we develop a computational optimization framework for dynamic market-based technoeconomic comparison of integrated energy systems that coproduce low-carbon electricity and hydrogen (e.g., solid oxide fuel cells, solid oxide electrolysis) against technologies that only produce electricity (e.g., natural gas combined cycle with carbon capture) or only produce hydrogen. Our framework starts with rigorous physics-based process models, built in the open-source Institute for the Design of Advanced Energy Systems (IDAES) modeling and optimization platform, for six energy process concepts. Using these rigorous models and a workflow to optimally design each technology, the framework is shown to be capable of evaluating new and emerging technologies in varying energy markets under a plethora of future scenarios (i.e., renewables penetration, carbon tax, etc.). Ultimately, our framework finds that solid oxide fuel cell-based coproduction systems achieve positive profits for 85% of the analyzed market scenarios. From these market optimization results, we use multivariate linear regression (R 2 values up to 0.99) to determine which electricity price statistics are most significant to predict the optimized annual profit of each system. The proposed framework provides a powerful tool for directly comparing flexible, multi-product energy process concepts to help discern optimal technology and integration options.

08 HYDROGEN↗

Revenue Analysis of Stationary and Transportable Battery Storage for Power Systems: A Market Participant Perspective

The power system faces a growing need for increased transmission capacity and reliability with the rising integration of renewable energy resources. To tackle this challenge, Battery Energy Storage Systems (BESSs) prove effective in enhancing grid capacity and relieving transmission congestion. This paper focuses on the PJM market, conducting a thorough revenue analysis to identify and characterize highly profitable nodes for BESS market participants. A comparison between stationary and transportable BESSs reveals that the transportable BESSs can generate higher potential revenue in energy and regulation markets. Based on these findings, we propose an optimal placement algorithm to support market participants in selecting strategic sites for BESS installation, validated with real PJM market data. This paper provides valuable insights for navigating market-based power system participants and promoting the effective integration of BESSs.

25 ENERGY STORAGE↗

Paired Hydro-Battery Hybrid System Operations Using MIQP Based Multi-Objective Optimization

Hybridizing a hydropower plant with a battery energy storage system is often a very expensive decision. So to make the case for feasibility of hybridization the cost benefit analysis must be comprehensive. Most literature found on this subject only uses one out of many different available value streams to carry out a feasibility analysis. To that end, in this study we propose a multi-objective optimization formulation and a mixed-integer quadratic programming optimization engine that considers multiple different value streams and optimizes hydro-battery hybrid system paired operations to maximize revenue generated from energy arbitrage while simultaneously minimizing the total hydro turbine mileage thus reducing turbine starts and stops and improving turbines life all while following all environmental constraints and not violating any limitations either regulatory or preferential (i.e., to support recreational activities like white water rafting, etc.). In this study we also implemented the developed optimization methodology to a real-world case study of Bagnell dam hydropower facility (8 units totaling 240 MW rated power capacity) which is owned and operated by our industry partner Ameren Energy Inc. The case study outcomes shows that by hybridizing the Bagnell dam hydropower facility with a 60 MW x 2-hr battery energy storage system, the annual benefits can be increased over \$6 million while reducing the annual mileage averaged per turbine and number of start/stops by over 98\% and 85\% respectively.

Chalishazar, Vishvas H.↗

Improving Data Center Energy Efficiency Through End-to-End Cooling Modeling and Optimization. Final Report

This report summarizes the development and demonstration of the three features developed for the DOE project “Improving Data Center Energy Efficiency through End-to-End Cooling Modeling and Optimization” (Award No. DE-EE0007688). The development of the three software features, Feature 1: Optimization of Cooling System Operation, Feature 2: Optimization of Airflow Management System, and Feature 3: Simultaneous Optimization of Airflow Management and Cooling Systems, are described first. Feature 1 is implemented in Phase 1 of the project and demonstrates 15% energy savings from cooling system optimization by applying the proposed retrofit solutions. Feature 2 is implemented in Phase 2 and the assessments show cooling energy-saving potential between 19.3% and 54.0%, depending on the energy-saving measures employed. In addition, investigating the energy saving potential for a hypothetical simple data center shows about 6.8% energy savings by optimizing the server load placement. Feature 3 is implemented in Phase 3 and the results show energy savings of 53.4% and 74.6% for the two partner data centers, respectively, compared to the baselines defined in Phase 1. Lastly, the project outcomes including software releases and publications are discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Autonomous Energy Management Software System for Small Commercial Buildings in Support of Decarbonization (Abstract)

The primary goal for the project is to develop and validate an autonomous energy management software (AEMS) system that will continuously optimize small commercial building operations by minimizing energy consumption and cost, providing a solution for maximizing decarbonization benefits from electrification of buildings. The work will leverage vast experience of Pacific Northwest National Laboratory (PNNL) research and development staff who have over two decades of experience in developing and successfully transferring software technologies to the private sector. This solution will be jointly developed with Intellimation LLC who plans to use it to scale their building energy efficiency offering. The project plan will include collaboratively working with Intellimation to package a set of solutions into the AEMS system, validate and demonstrate their capability, and value proposition through field demonstrations. If the deployment of the AEMS system optimizes RTUs’ set points, schedules, setbacks and optimal start and results in energy consumption reduction of 20%, the technical potential savings is approximately 675 trillion Btus of site energy savings and 2,000 trillion Btus of source energy. It will also result in carbon reductions of approximately 2.4 MMTCO2 and contribute to the climate change mitigation plans of many cities and states across the United States. Additional cost savings and emissions reduction are possible from management of peak electricity demand. The primary outcome will be an AEMS system that can be deployed at scale on small commercial buildings to improve operating efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Software Tools for Energy Storage Valuation and Design

Abstract Purpose of Review As the application space for energy storage systems (ESS) grows, it is crucial to valuate the technical and economic benefits of ESS deployments. Since there are many analytical tools in this space, this paper provides a review of these tools to help the audience find the proper tools for their energy storage analyses. Recent Findings There are many software tools for valuating ESS. These tools can be classified into two groups: (1) power system simulation and planning tools for analyzing the technical contributions of ESSs, and (2) techno-economic analysis tools for valuating the economic benefits of ESS deployment and specifying the optimal design of energy systems that include ESSs. While many of the tools, developed by the national laboratories, are free to use, the commercial tools are also of great importance in meeting the customers’ specific needs. Summary This paper provides a review of software tools for ESS valuation and design. A review of analysis tools for evaluating the technical impacts of energy storage deployments is also provided, as well as a discussion of development trends for valuation and design tools.

Nguyen, Tu A. (ORCID:0000000181148063)↗

Evaluation of Topology Optimization to Achieve Energy Savings at the Urban District Level: Preprint

Advanced district thermal energy systems have the potential to achieve significant energy savings and facilitate the integration of renewable thermal resources and waste heat, contributing to reductions in carbon emissions. Such systems, also known as fifth generation district heating and cooling (5GDHC) systems, circulate water at temperatures close to ambient, and leverage electrically-driven water-source heat pumps located at connected buildings to further temper the water. However, barriers exist to the adoption of 5GDHC systems, including the factorial growth in potential network configurations as a function of the number of considered buildings. Topology optimization, which seeks to answer the questions, “Which is the best subset of buildings, if any, to connect to a district thermal energy system, and by what network should they be connected, to minimize life cycle cost?” can accelerate the adoption of 5GDHC systems. This study is part of an effort to develop a topology optimization framework for district thermal energy systems. In this study, a heuristic for one aspect of the topology optimization problem---the use of the minimal spanning tree network to connect a given set of buildings at the least life cycle cost---is validated.

28 EE - Advanced Manufacturing Office (EE-5A)↗

Revisiting Theoretical Limits for One Degree-of-Freedom Wave Energy Converters

This work revisits the theoretical limits of one degree-of-freedom wave energy converters (WECs). This work considers the floating sphere used in the OES Task 10 WEC modeling and verification effort for analysis. Analytical equations are derived to determine bounds on displacement amplitude, time-averaged power (TAP), and power-take-off (PTO) force. A unique result found shows that the TAP absorbed by a WEC can be defined solely by the inertial properties and radiation hydrodynamic coefficients. In addition, a unique expression for the PTO force was derived that provides lower and upper bounds when resistive control is used to maximize power generation. For complex conjugate control, this same expression only provides a lower bound, as there is theoretically no upper bound. These bounds assist in comparing the performance of the floating sphere if it were to extract energy using surge or heave motion. The analysis shows because of differences in hydrodynamic coefficients for each oscillating mode, there are different frequency ranges that provide better power capture efficiency. The influence of a motion constraint on TAP while utilizing a nonideal power take-off is examined and found to reduce the losses associated with bidirectional energy flow. The expression to calculate TAP with a nonideal PTO is modified by the electrical conversion efficiency and the ratio of the PTO spring and damping coefficients. Furthermore, the PTO spring and damping coefficients were separated in the expression, allowing for limits to be set on the PTO coefficients to ensure net power generation.

16 TIDAL AND WAVE POWER↗

Dispatch Optimization Variable Engine

The Dispatch Optimization Variable Engine (DOVE) is software tool written in python, developed at Idaho National Laboratory (INL) that provides an easily accessible application-programming-interface (API) to performing resource dispatch optimization analysis for integrated energy system (IES) configurations. DOVE is an integral part of the Framework for Optimization of Resources and Economics (FORCE) software suite and is leveraged by codes such as the Holistic Energy Resource Optimization Network (HERON) and the Optimization of Real-Time Capacity Allocation (ORCA). The philosophy behind DOVE is to provide a modular software solution to IES planning and operation by utilizing state-of-the-art algorithms and machine learning. The goal is to accurately capture the dispatching behavior of a complex energy system given varying time-dependent signals for demand and commodity pricing.

McDowell, DylanJ. [Idaho National Laboratory (INL)↗

Multi-timescale operations of nuclear-renewable hybrid energy systems for reserve and thermal product provision

In this paper, an optimal operation strategy of a nuclear-renewable hybrid energy system (N-R HES), in conjunction with a district heating network, is developed within a comprehensive multi-timescale electricity market framework. The grid-connected N-R HES is simulated to explore the capabilities and benefits of N-R HES of providing energy products, different reserve products, and thermal products. An N-R HES optimization and control strategy is formulated to exploit the benefits from the hybrid energy system in terms of both energy and ancillary services. A case study is performed on the customized NREL-118 bus test system with high renewable penetrations, based on a multi-timescale (i.e., three-cycle) production cost model. Both day-ahead and real-time market clearing prices are determined from the market model simulation. In conclusion, the results show that the N-R HES can contribute to the reserve requirements and also meet the thermal load, thereby increasing the economic efficiency of N-R HES (with increased revenue ranging from 1.55% to 35.25% at certain cases) compared to the baseline case where reserve and thermal power exports are not optimized.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

A Topology Optimization Framework to Facilitate Adoption of Advanced District Thermal Energy Systems

Advanced district thermal energy systems, which circulate water at temperatures near ambient conditions, and facilitate the utilization of waste heat and renewable thermal sources, can lower the carbon-intensity of urban districts, advancing the U.N. Sustainable Development Goals. Optimization of the network topology — the selection of the best subset of buildings and the best network to connect them, to minimize life cycle cost — can increase adoption of these system in appropriate applications. The potential "solution space" of the topology optimization problem grows factorially with the number of buildings in the district, motivating the consideration of a design heuristic. In this study, a heuristic for the network selection was evaluated with an exhaustive search, for a prototypical four-building district. For the prototypical district considered, the heuristic was effective in selecting an optimal network topology. Additionally, it was found that, in this case, the selection of the subset of buildings was more influential on the life cycle cost than the selection of the network topology. This work is part of a larger effort to develop a topology optimization framework for district thermal energy systems, which is anticipated to address barriers to adoption of ambient-temperature systems.

28 EE - Advanced Manufacturing Office (EE-5A)↗

Assessing the Performance and Impact of PV Technologies on Storage in Hybrid Renewable Systems

Traditional monofacial photovoltaic (mPV) systems are commonly adopted and well-documented because of their lower upfront costs in comparison to bifacial photovoltaic (bPV) systems. This study investigates how PV technologies impact energy storage in grid-scale hybrid renewable systems, focusing on optimizing and assessing the performance of mPV and bPV technologies integrated with pumped storage hydropower. Using Ludington City, Michigan as a case study and analyzing real-world data such as solar irradiance, ambient temperature, and utility-scale load profiles, the research highlights the operational and economic benefits of bPV systems. The results reveal that bPV systems can pump approximately 10.38% more water annually to the upper reservoir while achieving a lower levelized cost of energy ($0.0578/kWh for bPV vs. $0.0672/kWh for mPV). This study underscores the outstanding potential of bPV systems in enhancing energy storage and management strategies, contributing to a more sustainable and resilient renewable energy future.

13 HYDRO ENERGY↗

A Novel Hollow Fiber Membrane Reactor for High Purity H 2 Generation from Thermal Catalytic NH 3 Decomposition (Final Report)

Ammonia (NH 3 ), as a promising carbon neutral liquid fuel (CNLF) and an effective H 2 source, can be synthesized from air and water (N 2 extracted from air and H 2 from water) using renewable energy sources. To produce H 2 as an intermediate, it is essential to develop effective and economic NH 3 decomposition technologies. We have been developing an innovative hollow fiber membrane reactor for high purity H 2 generation from thermal catalytic NH 3 decomposition. The objective of the proposed research is to fabricate a compact and robust prototype with high energy efficiency (>80%) to deliver high purity (>99%) H2 at high rate per volume (>0.15 g H 2 /h/cm 3 ) from NH3 decomposition at low temperature (<450 °C) and high conversion (>99%). Our overall technical approach is to effectively combine an active catalyst for NH 3 decomposition with a selective H 2 membrane separation process in a novel, compact modular system to generate high purity H 2 and simultaneously achieve high NH 3 conversion and optimize the entire system energy efficiency. This novel integrated membrane reactor design, together with low-cost, highly active Ru-based catalyst, makes it most appropriate for the project objective. The proposed applied research and development work, therefore, represents an innovative and transformational solution to H 2 generation from thermal catalytic NH 3 decomposition. During the course of our research we i) synthesized and characterized ruthenium(Ru)-based catalysts, evaluated their catalytic performance for NH 3 decomposition and optimized the catalyst composition and performance based on machine learning, ii) prepared SAPO-34, MFI membranes on α-alumina hollow fibers and measured their performance for H 2 /N 2 , H 2 /N 2 /NH 3 separation and evaluated carbon molecular sieve (CMS) and Pd/Ag membranes for H 2 /N 2 , H 2 /N 2 /NH 3 separation at different conditions, iii) designed and evaluated several types of membrane reactors for H 2 production from NH 3 decomposition, and iv) evaluated the H 2 delivery cost base on catalysts development, membrane development and membrane reactor. For 3,1,12 RuYK catalyst, H 2 productivity of 6.35 mmol/min/gcat with NH 3 conversion of 95~98% was achieved at 5 bar and 400 °C; for SAPO-34 membrane, the best membrane showed H 2 permeance of 7.56×10 -7 mol/(m 2 ∙s∙Pa) and H 2 /N 2 selectivity of 23.1 at 20 °C; For CMS and Pd/Ag membranes, both showed superior stability for 71%H 2 /24%N 2 /5%NH 3 mixture separation at 450 °C and 7 bar for over 120 h; for the membrane reactor, high H 2 purity of >99.99% with NH 3 conversion of >99.4% were achieved in Pd/Ag membrane reactor at 450 °C, and the NH 3 concentration was always below 10 ppb in H 2 product. The H 2 generation rate was calculated to be as high as 0.47 g/h/cm 3 and the energy efficiency was calculated to be 88%. The membrane reactor showed superior stability for over 370 h and high H 2 purity, H 2 production rate and NH 3 conversion. Based on calculation, the H 2 delivery cost can be as low as $3.66/kg.

08 HYDROGEN↗

A Neural Optimizer With Decision-Focused Learning for Optimal Energy Storage Operation

Here, this article introduces a neural optimizer-based framework for optimizing battery energy storage system (BESS) control for grid services, including demand charge and energy cost reduction. By leveraging decision-focused learning (DFL), the proposed framework ensures seamless integration and adaptation, significantly enhancing control performance. A patch time-series transformer is employed for peak load forecasting, incorporating aleatoric uncertainty quantification to account for forecasting uncertainties within the decision-making process. The framework utilizes a solver-in-the-loop approach to generate optimal BESS actions, which are then used to train the neural optimizer-based agent. By co-optimizing both BESS operational modes and output power within the NN, the system achieves improved performance and robustness. After initial training, the forecasting and control models are jointly fine-tuned to account for forecasting errors, further improving decision precision and efficiency through DFL. Case studies are performed to validate the performance of the framework using multiple real-world datasets, demonstrating superior performance in monthly peak load forecasting compared to state-of-the-art models. In addition, the results are compared against existing decision-making approaches. The results demonstrate a reduction in monthly peak forecasting error by approximately 15% across various performance measures and achieve an optimization gap for BESS operation that is about three times smaller compared to existing methods.

Kim, Hyeonjin [Pacific Northwest National Laborato↗