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Results for “renewable energy optimization”

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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A scalable planning framework of energy storage systems under frequency dynamics constraints

As the penetration of renewables increases in power systems, the declining system inertia can cause frequency stability issues. Battery energy storage systems (BESSs) respond fast and therefore can relieve the low inertia difficulty but need to be appropriately sized considering the associated cost. This paper presents a novel stochastic optimization model for economically planning BESS capacity while considering the spatial–temporal correlation of wind generation and generator outages under frequency stability constraints, which include the rate-of-change of frequency (RoCoF), frequency nadir (FN), and quasi-steady-state (QSS) frequency. A set of new FN constraints that can be easily linearized is developed. To account for renewable uncertainties, a realistic uncertainty modeling approach, Random Field, is adopted to generate wind generation scenarios by considering both spatial and temporal evolutions of wind speed profiles. The ESS sizing is formulated as a mixed-integer linear programming problem and solved by using a scalable decomposition-and-coordination approach, Surrogate Absolute Value Lagrangian Relaxation (SAVLR). To further improve the scalability and reduce computational burdens, a rolling-horizon-based update is developed and incorporated into SAVLR for providing a practical solution to the long-term planning of very large-scale power systems. Finally, a modified IEEE 118-bus system and the Polish system are used to validate the effectiveness and scalability of the model and solution methodology.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Optimal design of solar-driven electrolytic hydrogen production systems within electricity markets

Hydrogen has the potential to be a key contributor toward a low-carbon economy. Generating hydrogen by electrolysis using renewable energy is one way to support a decarbonized economy; however, its cost is not typically competitive with the carbon-emitting incumbent technology, steam methane reforming. The ability of electrolysis to integrate with electricity markets presents a unique cost reduction opportunity due to the perceived future availability of low and zero-marginal cost renewable energy sources. Additionally, as renewables, and particularly, photovoltaics are installed on the grid, they have a value deflation effect. This work evaluates solar-electrolysis configurations using a mathematical programming framework to maximize system net present value. The framework has been tested with specific weather conditions and financial mechanisms in California. Our findings indicate that a spectrum of potential cost competitive solutions is available for systems that (i) have market configurations resembling hybrid retail/wholesale, resulting in a hydrogen production cost range of US$6.2 kg-1–US$6.6 kg-1, or full wholesale market participation, reducing production cost to US$2.6 kg-1–US$3.1 kg-1, and (ii) achieve projected future cost reductions.

08 HYDROGEN↗
DOE OSTI2025

C4.jl [SWR-25-106]

A proof-of-concept software framework for integrating capacity expansion and production cost optimization with probabilistic resource adequacy assessment and sending feedback signals between the models.

Stephen, Gordon [National Renewable Energy Laborat↗
DOE OSTI2024

Hot Weather Impacts on Electric School Buses

School bus fleets considering electric school buses can use this resource to learn how hot weather impacts them, and learn how to optimize bus performance when temperatures rise.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

An economic assessment of behind-the-meter photovoltaics paired with batteries on the Hawaiian Islands.

Due to natural variability and uncertainty, the ever-increasing penetration of solar generation in Hawaii presents challenges to power grid operators to maintain reliable system operation. Demand response (DR) has the potential to be a cost-effective tool for Hawaii to reach its aggressive renewable energy goals while maintaining the reliability of power grids. The Hawaii Public Utilities Commission has approved the Hawaiian Electric Company's revised portfolio of DR programs. The companies have released a grid services purchase agreement and subscribed an initial tranche of load into their DR programs. This paper presents innovative analytical methods and comprehensive economic assessment for distributed photovoltaics (PV) paired with battery energy storage systems (BESSs) for two new DR programs, including fast frequency response and capacity grid service. Optimal dispatch and sizing methods are proposed for the paired system considering different tariff schedules and PV compensation programs across five islands. It was found that while the best resource configuration and potential economic benefits vary with tariff structure, a BESS paired with PV can be optimally dispatched to generate multiple value streams simultaneously. Compensation from DR programs is an important value stream to help increase the cost-effectiveness of the integrated system.

Battery energy storage system↗
DOE OSTI2024

The Best of Both Worlds: Combined Thermal and Battery Storage for Widespread Building Decarbonization

To meet 2050 decarbonization targets, widespread building electrification is a critical complement to clean power generation. Behind-the-meter storage (BTMS) (e.g., battery electric energy storage [EES] and thermal energy storage [TES]) integrated with buildings or building end uses to store and supply energy at optimal times can minimize burdens associated with operation, planning, and upgrades to the electrical grid sometimes triggered by building electrification. Such BTMS systems can serve the dual purpose of providing enhanced resilience at the building and grid level, and support the deployment of renewable generation needed for wide-scale decarbonization. While TES can cost-effectively shed and shift thermal loads, it cannot generally backup or shift non-thermal building end uses. EES, by contrast, is more expensive, but applicable to all end uses (i.e., thermal and electrical loads). Combined together, these storage systems can be traded off against one another to perform optimally in meeting demand flexibility, decarbonization goals, and energy resilience of the buildings at a lower total system cost. This paper proposes a framework to define BTMS benefits, provides four illustrative electrification scenarios using TES and EES, and discusses the combined TES/EES benefits with building energy modeling results. The paper also highlights potential barriers to adoption of BTMS and a path forward.

buildings↗
DOE OSTI2020

Estimation of abatement potentials and costs of air pollution emissions in China

Understanding the air pollution emission abatement potential and associated control cost is a prerequisite to design cost ef?cient control policies. In this study, a linear programming algorithm model, International Control Cost Estimate Tool, was updated with cost data for applications of 56 types of end-of-pipe technologies and ?ve types of renewable energy in 10 major sectors namely power generation, industry combustion, cement pro-duction, iron and steel production, other industry processes, domestic combustion, transportation, solvent use, livestock rearing, and fertilizer use. The updated model was implemented to estimate the abatement potential and marginal cost of multiple pollutants in China. The total maximum abatement potentials of sulfur dioxide (SO2), nitrogen oxides (NOx), primary particulate matter (PM2.5), non-volatile organic compounds (NMVOCs), and ammonia (NH3) in China were estimated to be 19.2, 20.8, 9.1, 17.2 and 8.6 Mt, respectively, which accounted for 89.7%, 89.9%, 94.6%, 74.0%, and 80.2% of their total emissions in 2014, respectively. The associated control cost of such reductions was estimated as 92.5, 469.7, 75.7, 449.0, and 361.8 billion CNY in SO2, NOx, primary PM2.5, NMVOCs and NH3, respectively. Shandong, Jiangsu, Henan, Zhejiang, and Guangdong provinces exhibited large abatement potentials for all pollutants. Provincial disparity analysis shows that high GDP regions tend to have higher reduction potential and total abatement costs. End-of-pipe technologies tended be a cost-ef?cient way to control pollution in industries processes (i.e., cement plants, iron and steel plants, lime production, building ceramic production, glass and brick production), whereas such technologies were less cost- effective in fossil fuel-related sectors (i.e., power plants, industry combustion, domestic combustion, and transportation) compared with renewable energy. The abatement potentials and marginal abatement cost curves developed in this study can further be used as a crucial component in an integrated model to design optimized cost-ef?cient control policies.

Zhang, Fenfen↗
DOE OSTI2025

A modeling study of ocean thermal energy conversion resource and potential environmental effects around Kailua-Kona, Hawaii

Ocean Thermal Energy Conversion (OTEC) offers a promising renewable energy solution through a heat exchange process using the temperature difference between warm surface seawater and cold deep seawater. Because accurate resource characterization is critical for the optimal design and implementation of OTEC systems, a high-resolution numerical model is employed to better characterize the OTEC resource at Kona, Hawaii. Our model provides detailed spatial and temporal variability of the thermal gradient, which is essential for assessing the viability and efficiency of OTEC systems. The model results reveal distinct patterns and dynamics not captured by existing observations or models (e.g., lower-resolution information). These findings highlight the importance of using high-resolution models for accurate predictions of thermal gradient variability, ultimately supporting more efficient and sustainable OTEC deployment. Additionally, the study investigates the impacts of mixed water discharge from OTEC plants that can cause shock to organisms living in the surface water and potentially destabilize the water column. Understanding these effects is vital for minimizing any potential negative environmental consequences and ensuring the long-term viability of OTEC operations. Further, our model improves OTEC resource characterization, which can lead to optimal design and deployment of OTEC systems. The analysis of OTEC water discharge impacts can accelerate the development of OTEC technologies, overcoming permitting/consenting challenges. These findings contribute to the broader adoption of high-resolution modeling in ocean energy resource characterization, particularly for OTEC applications.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Hydrogen production in microbial electrolysis cells with biocathodes

Electroautotrophic microbes at biocathodes in microbial electrolysis cells (MECs) can catalyze the hydrogen evolution reaction with low energy demand, facilitating long-term stable performance through specific and renewable biocatalysts. However, MECs have not yet reached commercialization due to a lack of understanding of the optimal microbial strains and reactor configurations for achieving high performance. Here, we critically analyze the criteria for the inocula selection, with a focus on the effect of hydrogenase activity and microbe–electrode interactions. We also evaluate the impact of the reactor design and key parameters, such as membrane type, composition, and electrode surface area on internal resistance, mass transport, and pH imbalances within MECs. Furthermore, this analysis paves the way for advancements that could propel biocathode-assisted MECs toward scalable hydrogen gas production.

Biotechnology & Applied Microbiology↗
DOE OSTI2021

Modular SOEC System for Efficient H 2 Production at High Current Density

The overall objective of the project was to demonstrate the potential of Solid Oxide Electrolysis Cell (SOEC) systems to produce hydrogen at a cost of $\$$2.00/kg H 2 or less (excluding delivery, compression, storage, and dispensing). An additional objective of the project was to enhance Solid Oxide Electrolysis Cell (SOEC) stack endurance and impart subsystem robustness for operation on load profiles compatible with intermittent renewable energy sources. The project was designed to achieve the overall objectives through a multi-disciplinary approach that included SOEC stack and materials development, systems development and optimization, techno-economic analyses, and ultimately the demonstration of a > 4 kg H 2 /day SOEC system. The scope of work included research and development efforts to develop an advanced, high temperature, water splitting (HTWS) system with superior performance and durability relative to the current state-of-the-art. In support of the aforementioned goals, the project activities included: 1) Develop and test cell materials with improved endurance at high current densities; 2) Design and fabricate a >4 kg H 2 /day SOEC stack optimized for high efficiency operation in the range of 1 - 2 A/cm 2 ; 3) Perform stack testing under simulated system conditions; 4) Perform systems analysis including configuration and parametric studies, to maximize SOEC system efficiency and minimize H 2 production costs; 5) Develop the conceptual design of a >4 kg H 2 /day thermally self-sustaining SOEC demonstration system including stack module and balance of plant (BoP).

08 HYDROGEN↗
DOE OSTI2020

Mesoscale Modeling to Characterize Eagle Soaring Habitat

Uncovering drivers of risk is crucial to understanding interactions between wildlife and wind turbines, and identifying options for impact minimization. These drivers tie to co-variates linked to behavior and movement patterns that allow us to estimate locations and periods of risk. For volant species, atmospheric flow can have significant influence on flight patterns. For obligate soaring birds, like golden eagles, updraft velocities can inform where eagles are likely to travel, at what altitude, and where conditions are not likely sufficient to sustain soaring flight. This has been an active area of study in recent years, using relatively coarse atmospheric data generally at the 20km x 20km scale or larger. Leveraging a 20-year dataset from the Weather Research and Forecasting Model (WRF) (https://www.mmm.ucar.edu/weather-research-and-forecasting-model), we are quantifying vertical velocities across the continental United States at a 2km x 2km resolution. Wind resource data sets originally were static maps showing the mean annual wind speed over an area. However, for these data sets to be optimally used for various applications they must be high-resolution time series, seamlessly span large geographic contexts, and account for uncertainty in wind speed. The National Renewable Energy Laboratory is producing public available datasets that meet these criteria and will be bias corrected to yield the most accurate wind resource data. This effort is an augment to the current WIND Toolkit which houses a high resolution data set. In the new iteration of the WIND Toolkit, a 20-year dataset will be used to improve the accuracy and estimate uncertainty using ensemble and machine-learning techniques. The resulting product will be the most accurate dataset of its size and at a 2km x 2km spatial and 5-minute temporal resolution. Through this work, a mesoscale vertical velocity layer will be produced by calculating the likelihood of orographic updraft and thermal updraft conditions across the continental United States. Specifically, we will use WRF model output combined with digital elevation maps to predict updrafts and then determine if vertical velocities are sufficient to support Golden Eagle soaring and gliding. Ultimately this data layer will be made available as a GIS layer in the Wind Prospector (maps.nrel.gov/wind-prospector/) tool or a similar framework. Data that will be incorporated include wind speed, direction temperature, relative humidity, barometric pressure, air density, precipitation rate, solar radiation, atmospheric stability, skin temperature, and upward heat flux. These products will advance research on interactions between volant species and wind energy by providing open access to highly resolved data with uncertainty quantification not previously available at this scale.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

RouteE: A Vehicle Energy Consumption Prediction Engine

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL's Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Machine Learning for Advanced Building Construction: Preprint

High-efficiency retrofits can play a key role in reducing carbon emissions associated with buildings if processes can be scaled-up to reduce cost, time, and disruption. Here we demonstrate an artificial intelligence/computer vision (AI/CV)- enabled framework for converting exterior build scans and dimensional data directly into manufacturing and installation specifications for overclad panels. In our workflow point clouds associated with LiDAR-scanned buildings are segmented into a facade feature space, vectorized features are extracted using an iterative random-sampling consensus algorithm, and from this representation an optimal panel design plan satisfying manufacturing constraints is generated. This system and the corresponding construction process is demonstrated on a test facade structure constructed at the National Renewable Energy Laboratory (NREL). We also include a brief summary of a techno-economic study designed to estimate the potential energy and cost impact of this new system.

building retrofits↗
DOE OSTI2023

Machine Learning for Advanced Building Construction

High-efficiency retrofits can play a key role in reducing carbon emissions associated with buildings if processes can be scaled-up to reduce cost, time, and disruption. Here we demonstrate an artificial intelligence/computer vision (AI/CV)-enabled framework for converting exterior build scans and dimensional data directly into manufacturing and installation specifications for overclad panels. In our workflow point clouds associated with LiDAR-scanned buildings are segmented into a facade feature space, vectorized features are extracted using an iterative random-sampling consensus algorithm, and from this representation an optimal panel design plan satisfying manufacturing constraints is generated. This system and the corresponding construction process is demonstrated on a test facade structure constructed at the National Renewable Energy Laboratory (NREL). We also include a brief summary of a techno-economic study designed to estimate the potential energy and cost impact of this new system.

build scans↗
DOE OSTI2025

Advancing Water Power Technology With State-of-the-Art Facilities

At the National Renewable Energy Laboratory's (NREL's) water research facilities, our experts have access to a suite of capabilities needed to develop the next great water power innovation and optimize existing ones. The lab's facilities cover five phases of the validation life cycle to ensure marine energy technologies can survive harsh open-water environments. From prototype fabrication to grid integration at all scales, NREL offers end-to-end marine energy device design and validation capabilities.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Optimization of Conditions for Photoluminescence-Based Sensing of Critical Minerals: Strategies and Outlook

Economically critical minerals and metals are vital to America’s security, with application areas in energy, advanced technologies, and defense systems. The growing implementation of renewable energy sources such as electric vehicles is expected to significantly increase demand for these minerals, while the global supply is monopolistic, with most of the production occurring in a few countries outside of the US. To mitigate potential supply chain vulnerabilities, the domestic production of economically critical metals has become a bipartisan priority of the US government, and unconventional sources such as coal and its utilization byproducts such as fly ash and acid mine drainage are being actively explored as an alternative method for meeting domestic demand. Slow and expensive characterization methods for critical metals present a significant pain point for domestic production, which has led to the exploration of alternative low-cost, portable platforms capable of detecting target metals during resource prospecting and downstream process monitoring. The National Energy Technology Laboratory has developed a portable fiber optic-based luminescence spectrometer that utilizes a metal-organic framework (MOF) material to detect and distinguish parts-per-billion levels of the visible-emitting rare earth elements terbium, dysprosium, samarium, and europium, which are all economically critical metals. Although preliminary results with this system are promising, a crucial barrier to practical deployment is the ability to operate in low pH, high ionic strength environments, as acidic conditions and the presence of other metal ions can significantly reduce luminescence signal. Here, current strategies related to sample treatment, material design, and sensor configuration are discussed in the context of improving sensor performance in application-relevant conditions. Additionally, future opportunities, such as the optimization of the sensing binding environment through computational methods and specific metals to be removed for treatment will be discussed, presenting a forward-looking view for innovation in luminescent sensing of critical metals.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Ionomer Optimization for Water Uptake and Swelling in Anion Exchange Membrane Electrolyzer: Hydrogen Evolution Electrode

Green hydrogen produced through anion exchange membrane water electrolysis is a promising, low-cost chemical storage solution for intermittent renewable energy sources. Low-temperature electrolysis using anion exchange membranes (AEM) combines the benefits of established water electrolysis technologies based on alkaline electrolysis and proton exchange membrane electrolysis. The anion conductive ionomers (ACI) used in the AEM electrolyzer (AEMEL) electrodes has been investigated. The ACI serves two primary purposes: (i) facilitate hydroxide conduction between the catalyst and bulk electrolyte and (ii) bind the catalyst to the porous transport layer and membrane. High ion exchange capacity (IEC) ACIs are desired, however, high IEC can cause excessive water uptake (WU) and detrimental ACI swelling. Proper water management is a key factor in obtaining maximum performance in AEM-based devices. In this study, a series of poly(norbornene)-based ACIs were synthesized and deployed in hydrogen evolving AEMEL cathode electrodes. A balance between ionic conductivity, WU and ionomer swelling was achieved in the ACI by varying the IEC and degree of polymer cross-linking. It was found that higher IEC ACIs with light crosslinking are preferred in the HER electrode. Furthermore, such a configuration fine-tuned the WU and ionomer swelling to achieve optimum cell performance and reduce cell operating voltages.

08 HYDROGEN↗