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Results for “flexibility potential”

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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The Potential to Improve the Value of U.S. Geothermal Electricity Generation Through Flexible Operations

Geothermal power plants have typically been operated as baseload plants. The recent expansion of wind and solar power generation creates a potential opportunity to increase the value of geothermal generation through flexible operations. In recent years, California’s wholesale electricity markets have exhibited frequent, short-term periods of negative pricing, indicating that additional flexibility would be valued in the market. Here, we examine local nodal hourly price records at all geothermal plants located in the western United States. We describe how the frequency and temporal characteristics of negative pricing episodes have changed over recent years. Based on these price series, we calculate the value of multiple strategies of flexible operations. Additionally, we use the estimates of future prices, developed through a capacity-expansion model and a dispatch model, to explore how the value of such flexible operations might change along with further penetration of variable renewable power sources. Based on the historical pricing records, we find that simple curtailment of operations during negative pricing episodes could increase the average energy value by 1–2 dollars per MWh and that allowing for increased production during limited high-priced hours, in addition to curtailment during negative priced hours, could potentially double the increase in value (up to 4 dollars per MWh). The forward-looking simulations indicate reduced values of flexibility from geothermal power despite higher penetrations of variable renewable energy. This result highlights the possibility that increasing flexibility options throughout the system may counteract the influence of increased variable renewable energy deployment.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Coordinated Integration of Renewable Generation and Small Modular Reactors in Puerto Rico – an Initial Study

With a growing interest and awareness to support clean and sustainable sources of energy, several countries have ambitious plans to significantly increase the penetration of renewable energy in the grid. However, the sources of renewable energy, such as solar and wind, are highly intermittent and therefore can pose additional challenges to maintain reliable system operations; one such challenge being flexibility requirement. This paper addresses the concerns of increasing flexibility needs with high renewable penetration, and also study the coordinated integration of nuclear small modular reactor and inverter-based renewable generation sources in a system to achieve high levels of carbon-free and sustainable energy. In this paper, balancing reserve and short-term flexibility requirements were considered for the analysis purpose. Also, methodology was developed to calculate metrics for calculating short-term flexibility requirements in the time-scale of hours. Small modular reactors are considered as potential sources of generation flexibility to complement renewables.

Agrawal, Urmila↗
DOE OSTI2022

Multi-reward Reinforcement Learning Based Bond-Order Potential to Study Strain-Assisted Phase Transitions in Phosphorene

Here, we introduce a multi-reward reinforcement learning (RL) approach to train a flexible bond-order potential (BOP) for 2D phosphorene based on ab initio training data sets. Our approach is based on a continuous action space Monte Carlo tree search algorithm that is general and scalable and presents an efficient multiobjective optimization scheme for high-dimensional materials design problems. As a proof-of-concept, we deploy this scheme to parametrize multiple structural and dynamical properties of 2D phosphorene polymorphs. Our RL-trained BOP model adequately captures the structure, energetics, transformation barriers, equation of state, elastic constants, and phonon dispersions of various 2D P polymorphs. We use this model to probe the impact of temperature and strain rate on the phase transition from black (α-P) to blue phosphorene (β-P) through molecular dynamics simulations. A decrease in critical strain for this phase transition with increase in temperature is observed, and the underlying atomistic mechanisms are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Tri-Level Scheduling Model Considering Residential Demand Flexibility of Aggregated HVACs and EVs Under Distribution LMP

Residential loads, especially heating, ventilation and air conditioners (HVACs) and electric vehicles (EVs), have great potentials to provide demand flexibility which is an attribute of grid-interactive efficient buildings (GEB). Under this new paradigm, first, EV and HVAC aggregator models are developed in this paper to represent the fleet of GEBs, in which the aggregated parameters are obtained based on a new approach of data generation and least squares parameter estimation (DG-LSPE), which can deal with heterogeneous HVACs. Then, a tri-level bidding and dispatching framework is established based on competitive distribution operation with distribution locational marginal price (DLMP). Furthermore, the first two levels form a bilevel model to optimize the aggregators’ payment and to represent the interdependency between load aggregators and the distribution system operator (DSO) using DLMP, and the third level is to dispatch the optimal load aggregation to all residents by the proposed priority list-based demand dispatching algorithm. Finally, case studies on a modified IEEE 33-Bus system illustrate three main technical reasons of payment reduction due to demand flexibility: load shift, DLMP step changes, and power losses. They can be used as general guidelines for better decision-making for future planning and operation of demand response programs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Machine learning-enhanced MPC for demand flexibility in small commercial buildings: An experimental study

Small- and medium-sized commercial buildings (SMCBs) represent the majority of U.S. commercial building stock and a significant share of peak electricity demand, yet they often lack centralized building automation systems, representing a significant untapped resource for urban energy management. This infrastructure gap makes advanced control implementation challenging, limiting the potential for widespread demand flexibility. Model Predictive Control (MPC) has shown strong potential for load shifting, peak demand reduction, and cost savings, but its effectiveness is hindered by unmeasured disturbances such as internal heat gains. This paper presents a Hybrid MPC framework that integrates a physics-based gray-box building thermal model, identified using a lumped disturbance (LD) approach, with a machine learning (ML) model for forecasting unmeasured disturbances. The hybrid approach is designed for buildings with multiple individually controlled heat pump and thermostat pairs, common in SMCBs, and aims to optimize coordinated scheduling of multiple heat pumps under dynamic electricity pricing while respecting comfort constraints. The methodology is validated through both simulations of case study buildings and experimental studies at a highly-instrumented test facility. Simulation results show that the Hybrid MPC achieves substantial load shifting and peak demand reduction, approaching the performance of an ideal MPC with perfect disturbance knowledge, and outperforming a conventional MPC without disturbance forecasting. In experiments, the Hybrid MPC reduced daily HVAC energy costs by 8.7%, peak-price time load (load shifting) by 41.7%, and peak demand by 29.2% compared to baseline control, demonstrating comparable benefits to the 11.6% cost savings, 42.9% load shifting, and 23.2% peak reduction of the ideal MPC. These results demonstrate that the proposed hybrid modeling approach can significantly improve MPC performance in real-world SMCB applications without requiring additional disturbance measurements.

Demand Flexibility↗
DOE OSTI2025

HP-FLEX: Field demonstration of the semantics-driven configuration of a Model Predictive Control system to make heat pumps flexible

Model Predictive Control (MPC) has demonstrated significant potential for optimizing building operations and enabling demand flexibility. However, the widespread adoption of MPC is hindered by complex manual configuration and commissioning processes that must be conducted by control experts working alongside building operators. These challenges drive up costs and reduce scalability, particularly when technical human resources and building automation systems are limited, such as in small and medium commercial buildings (SMCBs). This paper demonstrates how semantic standards, specifically ASHRAE 223P, can accelerate the adoption of MPC applications for load flexibility in SMCBs. The authors present a replicable control framework, titled “HP-FLEX” that leverages a building’s semantic model to bootstrap the required data configuration for an MPC controller developed for optimizing heat pump systems as flexible grid resources. The semantic model helps streamline the deployment workflow, particularly for site setup, data/control commissioning, and model setup. This integration enhances portability, transferability, and scalability of the HP-FLEX MPC, which has been developed to support MPC-based supervisory HVAC controllers in SMCBs. Additionally, the paper details the required building and thermostat metadata information to enable the HP-FLEX MPC based on field demonstrations. The new workflow was tested in a small commercial building located in California, U.S., and demonstrated a load shifting performance of 9% based on a dynamic pricing signal that varies by the hour. This work provides a practical pathway for transitioning sophisticated building applications from custom to standardized semantic representations, supporting the broader adoption of advanced control strategies like MPC. The framework also establishes a foundation for evolving metadata requirements as applications mature while maintaining compatibility with industry standards.

Paul, Lazlo↗
DOE OSTI2025

HP-FLEX: Field Demonstration of the Semantics-Driven Configuration of a Model Predictive Control System to Make Heat Pumps Flexible

Model Predictive Control (MPC) has demonstrated significant potential for optimizing building operations and enabling demand flexibility. However, the widespread adoption of MPC is hindered by complex manual configuration and commissioning processes that must be conducted by control experts working alongside building operators. These challenges drive up costs and reduce scalability, particularly when technical human resources and building automation systems are limited, such as in small and medium commercial buildings (SMCBs). This paper demonstrates how semantic standards, specifically ASHRAE 223P, can accelerate the adoption of MPC applications for load flexibility in SMCBs. The authors present a replicable control framework, titled “HP-FLEX” that leverages a building’s semantic model to bootstrap the required data configuration for an MPC controller developed for optimizing heat pump systems as flexible grid resources. The semantic model helps streamline the deployment workflow, particularly for site setup, data/control commissioning, and model setup. This integration enhances portability, transferability, and scalability of the HP-FLEX MPC, which has been developed to support MPC-based supervisory HVAC controllers in SMCBs. Additionally, the paper details the required building and thermostat metadata information to enable the HP-FLEX MPC based on field demonstrations. The new workflow was tested in a small commercial building located in California, U.S., and demonstrated a load shifting performance of 9% based on a dynamic pricing signal that varies by the hour. This work provides a practical pathway for transitioning sophisticated building applications from custom to standardized semantic representations, supporting the broader adoption of advanced control strategies like MPC. The framework also establishes a foundation for evolving metadata requirements as applications mature while maintaining compatibility with industry standards.

Paul, Lazlo↗
DOE OSTI2022

Transitioning from File-Based HPC Workflows to Streaming Data Pipelines with openPMD and ADIOS2

This paper aims to create a transition path from file-based IO to streaming-based workflows for scientific applications in an HPC environment. By using the openPMP-api, traditional workflows limited by filesystem bottlenecks can be overcome and flexibly extended for in situ analysis. The openPMD-api is a library for the description of scientific data according to the Open Standard for Particle-Mesh Data (openPMD). Its approach towards recent challenges posed by hardware heterogeneity lies in the decoupling of data description in domain sciences, such as plasma physics simulations, from concrete implementations in hardware and IO. The streaming backend is provided by the ADIOS2 framework, developed at Oak Ridge National Laboratory. This paper surveys two openPMD-based loosely-coupled setups to demonstrate flexible applicability and to evaluate performance. In loose coupling, as opposed to tight coupling, two (or more) applications are executed separately, e.g. in individual MPI contexts, yet cooperate by exchanging data. This way, a streaming-based workflow allows for standalone codes instead of tightly-coupled plugins, using a unified streaming-aware API and leveraging high-speed communication infrastructure available in modern compute clusters for massive data exchange. We determine new challenges in resource allocation and in the need of strategies for a flexible data distribution, demonstrating their influence on efficiency and scaling on the Summit compute system. The presented setups show the potential for a more flexible use of compute resources brought by streaming IO as well as the ability to increase throughput by avoiding filesystem bottlenecks.

Poeschel, Franz↗
DOE OSTI2022

How Distinct Structural Flexibility within SARS-CoV-2 Spike Protein Reveals Potential Therapeutic Targets

The emergence and rapid worldwide spread of the novel coronavirus disease, COVID-19, has prompted concerted efforts to find successful treatments. The causative virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), uses its spike (S) protein to gain entry into host cells. Therefore, the S protein presents a viable target to develop a directed therapy. Here, we deployed an integrated artificial intelligence with all-atom molecular dynamics simulation approach to provide new details of the S protein structure. Based on a comprehensive structural analysis of S proteins from SARS-CoV-2 and previous human coronaviruses, we found that the protomer state of S proteins is structurally flexible. Without the presence of a stabilizing beta sheet from another protomer chain, two regions in the S2 domain and the hinge connecting the S1 and S2 subunits lose their secondary structures. Interestingly, the region in the S2 domain was previously identified as an immunodominant site in the SARS-CoV-1 S protein. We anticipate that the molecular details elucidated here will assist in effective therapeutic development for COVID-19.

Chen, Serena↗
DOE OSTI2023

Highly Flexible Dielectric Films from Solution Processable Covalent Organic Frameworks**

Abstract Covalent organic frameworks (COFs) are known to be a promising class of materials for a wide range of applications, yet their poor solution processability limits their utility in many areas. Here we report a pore engineering method using hydrophilic side chains to improve the processability of hydrazone and β‐ketoenamine‐linked COFs and the production of flexible, crystalline films. Mechanical measurements of the free‐standing COF films of COF‐PEO‐3 (hydrazone‐linked) and TFP‐PEO‐3 (β‐ketoenamine‐linked), revealed a Young's modulus of 391.7 MPa and 1034.7 MPa, respectively. The solubility and excellent mechanical properties enabled the use of these COFs in dielectric devices. Specifically, the TFP‐PEO‐3 film‐based dielectric capacitors display simultaneously high dielectric constant and breakdown strength, resulting in a discharged energy density of 11.22 J cm −3 . This work offers a general approach for producing solution processable COFs and mechanically flexible COF‐based films, which hold great potential for use in energy storage and flexible electronics applications.

Senarathna, Milinda C.↗
DOE OSTI2023

Exploring structural transitions at grain boundaries in Nb using a generalized embedded atom interatomic potential

The advancement in experimental techniques, like the atom probe tomography and high resolution electron microscopy, is fueling interest in studying structural transformations of grain boundaries in metal and alloys to uncover correlations between mechanical properties and solute or impurity segregation to grain boundaries. Atomistic modeling is an important tool that can pinpoint the intricate dynamics of grain boundary phase transitions, but the lack of accurate interatomic potentials needed to simulate the complex dynamics of grain boundary structural transitions and identify different metastable phases has been the bottleneck. To this end, we use niobium as a model body centered cubic (BCC) metal and develop an interatomic potential to study grain boundary phase transitions. The potential for Nb is based on a generalization of the embedded atomic method potential and has sufficient flexibility to learn complex energy landscapes using a small set of training structures. We systematically test and validate the using data from ab initio density functional theory calculations and experiments. Using this potential, we calculate energies of multiple symmetric-tilt grain boundaries spanning a wide range of misorientation angles. Additionally, we explore different metastable structures of the Σ 27(552) [$1\overline{1}0$] grain boundary and use molecular dynamic simulations to study the coexistence of metastable phases and grain boundary transitions at finite temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Cybersecurity Considerations and Research Pathways for Grid-Interactive Efficient Buildings

Federal facilities serve critical missions and functions that require safe, reliable, and efficient operations. Digitization of several facility operations has increased the cost-effectiveness of energy usage and optimization of energy system performance. As the building controls landscape shifts to become more connected and smarter, building operators now face unique opportunities and challenges to adopt smart enabled devices that can lower energy usage while also optimizing building system performance. The grid-interactive efficient buildings (GEB) initiative aims to make buildings cleaner and more flexible through these smart devices. Smart enabled devices allow greater connectivity and control through remote operations and provide crucial data for analytics and increased efficiency. GEBs enable demand flexibility that has the potential to reduce electrical costs and transform the grid edge where buildings connect to power grids. This operation of interconnected systems, if not designed with cybersecurity practices, causes security gaps and introduces potential attack paths by adversarial and non-adversarial entities leading to disruption of operations.

building controls↗
DOE OSTI2022

Flexible Siting Criteria and Staff Minimization for Micro-Reactors

The economic potential of micro-reactors is vast and underestimated. Commonly-emphasized applications include niche markets such as remote communities, mines and military bases. However, micro-reactors could be used as flexible energy generators also for larger markets, such as mobile and containerized agriculture and manufacturing facilities, district heating, micro-grids for data centers, sea ports, airports and hospitals. The implication is that micro-reactors may have to be deployed also in non-remote locations. Successful implementation of micro-reactors needs a navigable and predictable licensing process, technology-appropriate siting restrictions, risk-informed emergency and safety requirements, and practical operating and maintenance requirements. The primary goal of this project was to develop siting criteria that are tailored to micro-reactors deployable in densely-populated areas, e.g., urban environments. To achieve that goal, we compared the characteristics of the MIT research reactor (MITR) with those of leading micro-reactor concepts (e.g., eVinci, USNC, Aurora), and evaluated whether and how the MITR design basis (e.g., inherent safety features, engineered safety systems, source term, emergency planning and emergency operating procedures) and associated regulations may be applicable to these new micro-reactors as well. What makes MITR a unique analogue in this context is its small power rating (6 MWt) and physical size, mode of operations (24/7 with a somewhat more commercial flavor than typical university reactors), and especially its urban location. Of course significant differences exist, such as mission (power production vs. research) and the reactor design itself. Leveraging the MITR experience, this project was able to generate criteria that will allow micro-reactors to realize their full economic potential as flexible heat and electricity generators for a diverse portfolio of applications in non-remote locations. As such, the outcome of this project might encourage investment in and use of micro-reactors. A second goal of the project was to conceptualize a model of operations for micro-reactors that would minimize the staffing requirements, and thus reduce the cost of electricity and heat generated by these systems. Here too our approach was to systematically review the MITR experience and requirements, as well as survey the innovations in autonomous control technologies and monitoring (e.g., advanced sensors, drones, robotics, AI) that would permit a dramatic reduction in staffing at future micro-reactor installations. The scope of work was expanded after the start date to include also an evaluation of micro-reactor security, using the so-called consequence-based analysis, and the development of a methodology to perform dynamic risk assessment for micro-reactors, using system theory and modeling and simulation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Grid-Interactive Efficient Buildings Support Federal Facility Decarbonization

The Federal Energy Management Program (FEMP) plays a key role in helping agencies understand and meet energy-related goals, including those surrounding smart buildings technologies and grid-interactive efficient buildings (GEBs), which can support decarbonization. GEB enables demand flexibility that has the potential to reduce electrical infrastructure costs and transform the grid edge, where buildings connect to the power grid.

decarbonization↗
DOE OSTI2025

Cracking in polymer substrates for flexible electronic devices and its mitigation

Mechanical reliability plays a critical role in determining the durability of flexible electronic devices because of the significant mechanical stresses they experience during manufacturing and operation. Many such devices are built on sheets comprising stiff transparent-conducting oxide (TCO) electrode films on compliant polymer substrates, and it is generally assumed that the high-toughness polymer substrates do not crack. Contrary to this assumption, here we show extensive cracking in the polymer substrates during bending of a variety of TCO/polymer sheets, and a device example — flexible perovskite solar cells. Such substrate cracking, which compromises the overall mechanical integrity of the entire device, is driven by the amplified stress-intensity factor caused by the elastic mismatch at the film/substrate interface. To mitigate this substrate cracking, an interlayer-engineering approach is designed and experimentally demonstrated. This approach is potentially applicable to myriad flexible electronic devices, with stiff films on compliant substrates, for improving their durability and reliability.

42 ENGINEERING↗
DOE OSTI2023

Highly Flexible Dielectric Films from Solution Processable Covalent Organic Frameworks**

Covalent organic frameworks (COFs) are known to be a promising class of materials for a wide range of applications, yet their poor solution processability limits their utility in many areas. Here we report a pore engineering method using hydrophilic side chains to improve the processability of hydrazone and β-ketoenamine-linked COFs and the production of flexible, crystalline films. Further, mechanical measurements of the free-standing COF films of COF-PEO-3 (hydrazone-linked) and TFP-PEO-3 (β-ketoenamine-linked), revealed a Young's modulus of 391.7 MPa and 1034.7 MPa, respectively. The solubility and excellent mechanical properties enabled the use of these COFs in dielectric devices. Specifically, the TFP-PEO-3 film-based dielectric capacitors display simultaneously high dielectric constant and breakdown strength, resulting in a discharged energy density of 11.22 J cm -3 . This work offers a general approach for producing solution processable COFs and mechanically flexible COF-based films, which hold great potential for use in energy storage and flexible electronics applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Residential Demand Flexibility: Modeling Occupant Behavior using Sociodemographic Predictors

Demand flexibility (DF) has the potential to increase the saturation of renewables in the grid and reduce operating costs for both utilities and customers. However, less than 8% of U.S. residential electric customers are enrolled in DF programs. A major research gap on this topic is an uneven understanding of behavioral drivers of electricity use and DF program participation at the household level. In this study, we employ machine learning models to predict residential occupant behavior in activities relevant to DF. We model occupants' extensive decisions (i.e., choice of action) and intensive behaviors (i.e., amount of time spent) during peak and off-peak time periods using the publicly available American Time Use Survey, which includes activities data for approximately 200,000 respondents. In our machine learning models, predictions for both extensive and intensive behavior fell within a +/-20% error margin at the aggregate level. We identify 13 key sociodemographic predictors of DF-related intensive behavior using LASSO inference and beta coefficient ranking. However, these top predictors differ by activity, suggesting potential scope for differential user targeting for DF events and technologies during program design. This work also contributes to understanding when and who might adopt these DF technologies based on their daily routine activities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗