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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Comparing Numerical Models of the TALOS Wave Energy Converter

In this study, we present the development of a numerical model of the TALOS Wave Energy Converter (WEC) in WEC-Sim and compare it with existing numerical in-house models from Lancaster and results from DNV's Sesam code. The objective of this work is to validate the performance of the TALOS WEC using WEC-Sim and compare the results with those obtained from other numerical models. The TALOS WEC is a promising technology for the generation of clean, renewable energy from ocean waves. The development of a reliable numerical model for the TALOS WEC is crucial for its design and optimization. To achieve this, we have implemented the TALOS WEC in WEC-Sim and compared the results to in-house models from Lancaster and to results from DNV's Sesam code. Our results show good agreement with the results from Lancaster and DNV, demonstrating the accuracy and reliability of the numerical model developed in WEC-Sim. This work advances the field of wave energy conversion by providing a verified numerical model of the TALOS WEC, which can be used for further optimization and design studies.

CFD↗
DOE OSTI2024

Adaptive Computing and Multi-Fidelity Strategies for Control, Design and Scale-Up of Renewable Energy Applications

We describe our ongoing research in adaptive computing and multi-fidelity modeling strategies. Our goal is to use a combination of low- and high-fidelity simulation models to enable computationally efficient optimization and uncertainty quantification. We develop optimization formulations that take into account the compute resources currently available, which act as a constraint with regards to the fidelity level simulation we can run while maximizing information gain. These strategies are being implemented into a software framework with a generalized API allowing its application to a broad range of applications, from power grid stability and buildings control to material synthesis and biofuels processing. We will discuss a few examples from these applications that can benefit from this approach, especially when considering challenges arising in scaling up experiments and simulations.

adaptive computing↗
DOE OSTI2024

Incorporating Wind Turbine Choice in High-Resolution Geospatial Supply Curve and Capacity Expansion Models

To achieve national decarbonization goals, U.S. annual deployment of wind energy will need to increase by at least fivefold compared to the recent past. Modeling and analysis frameworks can help inform where and how wind energy deployment might occur and thereby help enable the achievement of decarbonization goals. However, most prior wind energy modeling and analysis studies rely on generalized representations of wind energy technologies. Since wind energy technology advancements are expected to increase the competitiveness of wind energy, it is important to incorporate more detailed representations of turbine technology into wind energy modeling. Here we present a new method that incorporates wind turbine choice into the technology representation of land-based wind energy in long-term planning models. Our method integrates three previously published modeling and analysis capabilities: 1) bottom-up cost modeling to estimate future technology costs, 2) geospatial modeling to represent siting decisions, and 3) power sector modeling to evaluate potential deployment. We refer to this approach as a "customized turbine choice" methodology because it creates a composite turbine scenario by choosing from multiple wind turbine technologies—using site-specific optimized turbine layout and selecting the least-cost technology at each location. We demonstrate the capabilities of this new modeling pipeline by examining how the selection of four different wind turbine configurations might evolve from 2021 through 2040. Our results show that using our customized turbine choice methodology could lead to higher estimates for wind future deployment, which indicates that more simplified modeling might underestimate the role that wind energy could play in meeting decarbonization goals. Future research is needed to further explore the implications of turbine choice and to better inform technology researchers, original equipment manufacturers, and other wind industry stakeholders about the market potential of different wind turbine technologies.

17 WIND ENERGY↗
DOE OSTI2023

New Tool Evaluates the Financial Viability of Pumped Storage Hydropower

The rapidly evolving electricity system with increasing variable renewable energy (VRE) resources provides both opportunities and challenges for the power sector. With the significant ramps and intermittency associated with VRE resources, the requirements and need for additional flexible resources increase. Pumped storage hydropower (PSH) provides flexibility to the electricity grid to replace fossil fuel plants, which are responsible for 25% of U.S. emissions. PSH projects support various aspects of power system operations, including flexibility, ramping capability, energy, ancillary service, black start, and others. The significant potential of hydropower requires understanding the different value drivers to the electricity system specific to the location of a project and then optimizing the plant for the different system values. Thus, determining the value of PSH projects and their many services and contributions to the electricity system can be a challenge for potential developers, system owners, regulators, policy makers, and consultants.

13 HYDRO ENERGY↗
DOE OSTI2022

Distributed Optimization Approaches with Discrete Variables in the Power Distribution Systems

Traditionally, centralized approaches have predominantly been used for the power system operation and control. With increasing penetration of small-scale distributed energy resources (DERs) in the distribution network, especially independently owned renewable resources, distributed algorithms can serve as a potential alternative for improving scalability, resiliency and addressing privacy concerns. However, the complexity of distributed algorithms significantly increases with the integration of the legacy devices, the operation of which depend on discrete control variables. This paper aims to provide a review of the distributed optimization algorithms incorporating discrete control variables for the power distribution system. While the research in this domain is still at its nascence, an extensive comparison of the approaches in the literature for applying quadratic penalty, branch and bound,ordinal optimization and proximal operator to handle discrete variables in the framework of ADMM and dual decomposition have been addressed. Future research direction in this field have been also provided.

Adan, Jannatul↗
DOE OSTI2023

Machine learning-based ethylene and carbon monoxide estimation, real-time optimization, and multivariable feedback control of an experimental electrochemical reactor

Electrochemical reduction of CO 2 gas is a novel CO 2 utilization technique that has the potential to mitigate the global climate crisis caused by anthropogenic CO 2 emissions, and enable the large-scale storage of energy generated from renewable sources in the form of carbon-based chemicals and fuels. However, due to the complexity of the electrochemical reactions, the explicit first-principles models for CO2 reduction are not available yet, and there has been a limited effort to develop process modeling, optimization and control of CO 2 electrochemical reactors. To this end, a rotating cylinder electrode (RCE) reactor has been constructed at UCLA to understand the mass transfer and reaction kinetics effects separately on the productivity. In the RCE reactor, the applied potential strongly influences the reaction energetics and the electrode rotation speed affects the hydrodynamic boundary layer and modifies the film mass transfer coefficient, which involves convective and diffusive transport. Further, the present work aims to develop a multi-input multi-output (MIMO) control scheme for the RCE reactor that integrates techniques from artificial and recurrent neural network modeling, nonlinear optimization, and process controller design. Specifically, production rates of two products from the experimental reactor, ethylene and carbon monoxide, are controlled by manipulating two inputs, applied potential and catalyst rotation speed. Process dynamics and controllability are analyzed, a feedback control strategy is designed and the controllers are tuned accordingly. The experimental electrochemical cell is employed to gather data for process modeling and implement the multivariable control system. Finally, the experimental results are presented which demonstrate excellent closed-loop performance by the control system and regulation of the outputs at three different set-points including an economically-optimal set-point.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Beneficial Integration of Energy Storage and Load Management with Photovoltaic (PV)

In recent years, a number of industry activities have aimed at addressing the integration challenges posed by the variability and uncertainty of higher penetration of renewable generation sources, like solar photovoltaic (PV) – one of the key objectives of the Sustainable and Holistic Integration of Energy Storage and Solar PV (SHINES) program launched by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE). This EPRI led Beneficial Integration of Energy Storge, and Load Management with PV project aimed to design, develop, and demonstrate end-to-end distributed energy resource (DER) integration solution to build on these activities. EPRI led project team designed and implemented a local controller that uses model predictive control (MPC) algorithm to optimally manage DERs on site by planning for a receding horizon while executing the control settings for the first step of its plan. The team has also developed a system controller to interface with the local controller to demonstrate the hierarchical control and how it can leverage DER capabilities to address challenges like over voltage and thermal limit violations which typically limits the DER hosting capacity of distribution feeders. Team has demonstrated how the local controller with optimization algorithm can effectively manage controllable loads like HVAC, water heater, and pool pumps to allow for greater integration of PV with relatively smaller energy storage system requirements. Optimal utilization of the load control can also reduce the depth of discharge of batteries to meet grid export/import limit from behind-the-meter (BTM) DERs. Proper utilization of DER capabilities via local control intelligence, like the one developed and demonstrated in this project can help the industry to address integration challenges of higher penetration of solar PV in economically efficient manner. This can help to accelerate deployment of clean renewable energy systems at lower societal cost.

14 SOLAR ENERGY↗
DOE OSTI2025

The Origin of Improved Performance in Boron‐Alloyed Silicon Nanoparticle‐Based Anodes for Lithium‐Ion Batteries

Stabilizing the solid electrolyte interphase (SEI) remains a key challenge for silicon‐based lithium‐ion battery anodes. Alloying silicon with secondary elements like boron has emerged as a promising strategy to improve the cycle life of silicon anodes, yet the underlying mechanism remains unclear. To address this knowledge gap, how boron concentration influences battery performance is systematically investigated. These results show a near‐monotonic increase in cycle lifetime with higher boron content, with boron‐rich electrodes significantly outperforming pure silicon. Additionally, silicon‐boron alloy anodes exhibit nearly three times longer calendar life than pure silicon. Through detailed mechanistic analysis, alternative contributing factors are systematically ruled out, and it is proposed that improved passivation arises from a strong permanent dipole at the nanoparticle surface. This dipole, formed by undercoordinated and highly Lewis acidic boron, creates a static, ion‐dense layer that stabilizes the electrochemical interface, reducing parasitic electrolyte decomposition and enhancing long‐term stability. These findings suggest that, within the SEI framework, the electric double layer is an important consideration in surface passivation. This insight provides an underexplored parameter space for optimizing silicon anodes in next‐generation lithium‐ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2025

OPTICHEM (OPTimizer for Industrial CHEMical pathways) [SWR-25-70]

Using alternative feedstocks such as biomass and waste could help the chemical sector address growing challenges from supply chain disruptions. This project aims to identify optimal combinations of chemical production pathways that achieve user-defined priorities within set resource constraints. This project contains two main Python modules that work together to perform multi-objective optimization of feedstock usage and post-optimization analysis. The outputs from both modules (e.g., CSV files, PDF diagrams, HTML reports, and pickle files) are automatically saved in dedicated output folders. The folder names include key parameters such as the optimization metric, target year, and whether 2030 results are fixed.

Ghosh, Tapajyoti [National Renewable Energy Labora↗
DOE OSTI2025

Wind Turbine Rotor Design Using High-Fidelity Aerostructural Optimization

Large wind turbines yield more energy but demand careful aeroelastic blade design. Coupled multiphysics design strategies can reduce wind energy costs by exploiting fluid-structure interactions. This work presents the first high-fidelity aerostructural optimization study of a large wind turbine rotor. We use blade-resolved fluid dynamics and structural solvers in a monolithic gradient-based optimization framework to explore steady-state torque and blade mass tradeoffs. The coupled-adjoint approach computes gradients efficiently, enabling the optimization of over 100 structural and geometric parameters simultaneously. Our optimization study modifies a DTU 10 MW benchmark with a simplified structure and isotropic material properties. The tightly coupled optimizations increase torque by 14% while reducing rotor mass by 9% or reduce blade mass by 27% while maintaining torque. Blade-resolved models provide greater design freedom, enabling 5% higher mass reductions than conventional parameterizations at equal torque. This framework paves the way for more detailed high-fidelity optimization studies to complement conventional design approaches.

17 WIND ENERGY↗
DOE OSTI2023

Hydroelectric Power and Hydrogen Production Integration

Hydropower-based hydrogen production could introduce opportunities for new revenue streams for hydropower plants, including from energy storage and regeneration as well as from sale of the hydrogen product to external markets. Hydrogen-based energy storage and regeneration could also help support Idaho Power’s decarbonization goals by decreasing dependence on fossil-based peaking power plants. Additionally, integration of hydrogen production with hydropower generation could help address the issue of low dissolved oxygen river water conditions that commonly accompany hydropower plant operations by utilizing the oxygen byproduct from an electrolytic hydrogen production process as a resource for mitigation of low dissolved oxygen water conditions. Comprehensive techno-economic analysis of the hybrid hydroelectric and hydrogen energy storage system has revealed critical insights into the pathways and considerations for optimizing the economic value and environmental benefits of such systems. Among the three identified pathways of natural gas blending, regeneration, and direct sale of hydrogen, the direct sale of hydrogen emerges as the most profitable, particularly given the current pricing dynamics of hydrogen and electricity. However, as we anticipate a future grid characterized by higher renewable energy penetration, the landscape may evolve, featuring lower average electricity prices, heightened fluctuations, and more significant seasonal variations. Consequently, the attractiveness of electricity regeneration through hydrogen and the benefits of long-duration hydrogen storage are expected to increase substantially in such a dynamic energy scenario. The careful selection of component sizes within the hybrid system proves to be paramount for ensuring cost-effectiveness. Notably, the size of the hydrogen market has emerged as a critical determinant for the optimal size of the electrolyzer. Hydrogen storage should be sized to meet energy shifting requirements. The appropriate size of the fuel cell/microturbine generator hinges on the shape of electricity prices and the available revenue streams derived from participating in grid services. Striking the right balance among these components is essential for maximizing the overall efficiency and profitability of the hydrogen facility. Furthermore, the by-product of electrolysis, namely oxygen, introduces an additional dimension to the system's functionality. The oxygen generated can be effectively utilized for dissolved oxygen (DO) mitigation, particularly with larger electrolyzer sizes capable of satisfying the complete oxygen demand for this purpose. While the economic benefits derived from saved oxygen purchase costs may be relatively modest compared to other revenue streams, the environmental advantages of repurposing oxygen for DO mitigation could help Idaho Power meet their environmental obligations. In addition to the identified factors shaping the viability of hybrid hydrogen production and hydroelectric generation, it is noteworthy that the integration of hydrogen energy storage offers a unique advantage during unusually wet years. In such periods of increased water inflow, the hydrogen storage capacity serves as a valuable supplement to the reservoir. By utilizing hydrogen energy storage as a complementary reservoir, the system gains flexibility in reservoir management when dealing with fluctuations in water availability.

08 HYDROGEN↗
DOE OSTI2023

Incorporation of market signals for the optimal design of post combustion carbon capture systems

Recent studies have shown that fossil generators equipped with post-combustion carbon capture (PCC) systems are needed to reduce the cost of deep decarbonization. Such generators need to be flexible and responsive to grid conditions, particularly in a high variable renewable energy (VRE) environment. In this work, we evaluate the net present value (NPV) of retrofitting an existing natural gas combined cycle (NGCC) unit with a flexible PCC system while incorporating market signals from a high VRE grid. We use our industrial partner’s NGCC configuration as representative of existing NGCC units and Svante’s rapid-temperature swing adsorption (TSA) for PCC. Because of its ability to rapidly startup/shutdown and ramp-up/ramp-down, the chosen capture technology is very attractive for load-following operations. For a given set of market signals, we formulate a two-stage stochastic multi-period optimization problem, under the price-taker assumption, to simultaneously optimize the design of the capture system and operation of the entire plant. Rigorous models for the NGCC unit, PCC system, and compression system are developed using commercial process simulators and validated with either plant or vendor data. For computational tractability, we develop surrogate/reduced-order models for use in the optimization problem. The surrogate model for the NGCC plant is constructed by linearizing the rigorous dynamic model at 75% load, while data-driven nonlinear surrogate models for the capture and compression systems are constructed using simulation data from the rigorous models. The optimization problem, formulated as a mixed integer bilinear program, is implemented in the IDAES® integrated platform and solved to global optimality using Gurobi 9.5. Using this formulation, we determine the profitability of retrofitting an existing NGCC unit with the chosen capture system for multiple regions in the U.S. under two scenarios with different carbon prices. Importantly, the results show that the optimal decision strongly depends on the region and on the carbon price, thereby demonstrating the importance of the inclusion of market signals in the design process.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

SDOM (Storage Deployment Optimization Model) [SWR-21-73]

SDOM is designed to accurately represent the operation of energy storage across different timescales, including long-duration and seasonal applications, and the spatiotemporal diversity and complementarity among VRE sources. SDOM uses an hourly temporal resolution, a fine spatial resolution for VRE sources, and a 1-year optimization window. SDOM assumes that all builds of VRE are accompanied by sufficient additional transmission capacity to allow full utilization of these additional resources. Nuclear, hydropower, and other renewable generation (e.g., biomass and geothermal energy sources) are fixed based on operational data (time series) for a given year; thus, SDOM minimizes total system cost using conventional generators as balancing units and using VRE and storage technologies to achieve a user-defined carbon-free or renewable energy target. The total system cost includes capital costs, fixed operation-and-maintenance (FO&M) costs, variable operation-and-maintenance (VO&M) costs, and fuel cost for power generation and storage technologies.

Guerra Fernandez, Omar Jose↗
DOE OSTI2024

Exploring Multidimensional Spatial-Temporal Hydropower Operational Flexibilities by Modeling and Optimizing Water-Constrained Cascading Hydroelectric Systems

Because of unique characteristics such as clean and cost-competitive electricity as well as fast-ramping and storage abilities, the power industry continues to evolve its operation strategies for cascading hydroelectric (CHE) systems for providing enhanced values to the grid, especially under the deeper renewable resource integration. However, existing operation practices of CHEs predate the integration of renewables, which could prohibit the effective utilization of their inherent flexibilities in delivering maximum financial benefits and providing valuable grid services to the power system and electricity market operations. Indeed, modeling and optimizing these resource-limited while flexible CHE assets with uncertainties and imperfect information across multiple spatial-temporal dimensions present significant challenges. To facilitate CHE facility operators in effectively coordinating water usage and hydropower plant operations across multiple timescales, this project aims to fill the existing gaps by developing a suite of accurate water inflow (WI) forecast models as well as enhanced CHE modeling and optimization approaches with proper consideration of their unique characteristics, which would help explore their multidimensional spatial-temporal operational flexibility potentials. The developed approaches could better align reservoir operation strategies with variability and uncertainty of future water availability. They can also promote more effective utilization of multidimensional spatial-temporal hydropower operational flexibility potentials by designing long-term evacuation plans of reservoirs and short-term operation of CHEs, along with their coordination with other types of renewables. The project leverages various resources to facilitate the research and development activities, including actual characteristics data of CHE systems and a library of current and future cases of Portland General Electric (PGE). These realistic data enable the project team to study how to maximize the value of CHEs under current and future portfolios and evaluate opportunities to improve operation practices.

13 HYDRO ENERGY↗
DOE OSTI2025

Robustness: The Missing Ingredient in Generation Scheduling

This article highlights robustness as an essential factor to cope with the ever-increasing levels of uncertainty in generation scheduling under significant renewable energy penetration, as is the case in Brazil and Spain. To that end, robust generation scheduling is framed within the different optimization-based approaches that are available for uncertainty handling. In addition, the suitability of robust optimization to accommodate practical security criteria in generation scheduling is also emphasized. Interestingly, this article points out the existence of an effective algorithm allowing the discovery of critical or so-called umbrella scenarios, which paves the way for the implementation of robust generation scheduling in industry practice.

Street, Alexandre↗
DOE OSTI2024

Final Technical Report

The Department of Energy is interested in technologies that support the sustainable production of fuels, chemicals, and other bioproducts from plant biomass, to offset the nation’s reliance on fossil resources. The plant cell wall of energy crops provides the largest reservoir of raw materials for bioproducts. However, the widespread use of plant cell walls is hampered by their complexity and resistance to breakdown. To improve the productivity and cost-effectiveness of using energy crops to generate bioproducts, the fundamental problem of deconstructing plant cell walls must be addressed. This project developed and evaluated an innovative genetic modification technology to produce strategically designed enzymes that specifically accumulate in the plant cell wall. The resulting enzyme-engineered energy crops are expected to grow normally under natural conditions but break down more quickly and easily under high temperature during the production of biobased products. As such, this plant cell wall targeting enzyme engineering effort will reduce the cost of plant cell wall deconstruction and ultimately improve the economics of bioproducts. The overall objective of this project is to develop and evaluate the in-planta enzyme engineering technology to reduce lignocellulose deconstruction cost. The concept was first validated using tobacco plant, a model plant system that is typically used in lab testing for initial concept validation. Then the enzyme optimization was validated using switchgrass, the energy crop to be used to produce bioproducts. There are three specific objectives in this Phase I project: (1) validate the enzyme optimization concept using tobacco plant, a model plant system. (2) validate the enzyme optimization concept using switchgrass. (3) techno-economic analysis (TEA) for further scale-up application. By the end of this project, in-planta enzyme engineering was validated in both tobacco and switchgrass plants, with improved enzyme activity and saccharification efficiency. The in-planta enzyme engineering in Tabacco didn’t have a significant impact on plant growth and development. Transgenic tobacco plants with in-planta cellulose degrading enzymes showed higher biomass digestibility than wild type. Gene construction and transformation in switchgrass was much longer than expected, which delayed the research progress. Besides, in-planta engineering of lignin degrading enzyme is more challenging than cellulose degrading enzyme, in terms of expression detection. Expression of lignin degrading enzyme and cellulose degrading enzyme improved biomass yield and saccharification efficiency of switchgrass, respectively. It is promising to express both genes in switchgrass for optimized overall performance. According to the results of TEA, switchgrass biomass production cost is mainly attributed to by fertility and harvesting. Biomass production profit can increase up to 10-fold depending on biomass price. The PHA production profit is also sensitive to the biomass price. The proposed technology could potentially reduce the biomass deconstruction cost from 33% to 9% of PHA revenue, making the biomass-based PHA competitive to petroleum-based polymers even in case of relatively high biomass price of biomass. Therefore, cultivation of the genetically engineered self-deconstruction switchgrass for Polyhydroxyalkanoate (PHA) production could benefit switchgrass grower and PHA producer with attractive profits for both sectors. This new enzyme optimization approach will be beneficial for bioindustries that use energy crops as feedstocks. It will improve the economic viability of converting energy crops to renewable products that support a sustainable society and helps address the Nation’s long-term strategic needs for renewable products and reduction of reliance on fossil resources.

42 ENGINEERING↗
DOE OSTI2021

Modeling a Lead-Cooled Fast Reactor with Thermal Energy Storage using Optimal Dispatch and SAM

Increased contributions from wind and solar energy have helped set the United States on an attainable pathway towards carbon-free energy production. Though renewable energy is pivotal for this goal, saturating the grid with these variable energy sources has its challenges. Resources for wind and solar vary from factors beyond human control, hence the power output from these energy generators doesn’t necessarily match power demand for a specific region at a given moment in time. Solar photovoltaic production has a drastic mismatch since the peaks for power demand and power production are often out of phase: production peaks with available irradiance while demand peaks during the morning and evening hours. This “duck curve” in energy demand, the difference between high and low demand, grows as the grid becomes more saturated with renewable energy providers.

Soto, Gabriel J.↗
DOE OSTI2022

Demonstrating solarpilot ’s Python Application Programmable Interface Through Heliostat Optimal Aimpoint Strategy Use Case

solarpilot is a software package that generates solar field layouts and characterizes the optical performance of concentrating solar power (CSP) tower systems. solarpilot was developed by the National Renewable Energy Laboratory (NREL) as a stand-alone desktop application but has also been incorporated into NREL’s System Advisor Model (SAM) in a simplified format. Prior means for user interaction with solarpilot have included the application’s graphical interface, the SAM routines with limited configurability, and through a built-in scripting language called “LK.” This article presents a new, full-featured, python-based application programmable interface (API) for solarpilot, which we hereafter refer to as CoPylot. CoPylot enables python users to perform detailed CSP tower analysis utilizing either the Hermite expansion technique (analytical) or the SolTrace ray-tracing engine. CoPylot’s enables CSP researchers to perform analysis that was previously not possible through solarpilot’s existing interfaces. This article discusses the capabilities of CoPylot and presents a use case wherein we populate a model that obtains optimal solar field aiming strategies.

14 SOLAR ENERGY↗