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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 379 records · Page 21
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DOE OSTI2015

Electricity Market Manipulation: How Behavioral Modeling Can Help Market Design

The question of how to best design electricity markets to integrate variable and uncertain renewable energy resources is becoming increasingly important as more renewable energy is added to electric power systems. Current markets were designed based on a set of assumptions that are not always valid in scenarios of high penetrations of renewables. In a future where renewables might have a larger impact on market mechanisms as well as financial outcomes, there is a need for modeling tools and power system modeling software that can provide policy makers and industry actors with more realistic representations of wholesale markets. One option includes using agent-based modeling frameworks. This paper discusses how key elements of current and future wholesale power markets can be modeled using an agent-based approach and how this approach may become a useful paradigm that researchers can employ when studying and planning for power systems of the future.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Multi-Lab EV Smart Grid Integration Requirements Study. Providing Guidance on Technology Development and Demonstration

The report begins with a discussion of the current state of the energy and transportation systems, followed by a summary of some VGI scenarios and opportunities. The current efforts to create foundational interface standards are detailed, and the requirements for enabling PEVs as a grid resource are presented. Existing technology demonstrations that include vehicle to grid functions are summarized. The report also includes a data-based discussion on the magnitude and variability of PEVs as a grid resource, followed by an overview of existing simulation tools that vi This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. can be used to explore the expansion of VGI to larger grid functions that might offer system and customer value. The document concludes with a summary of the requirements and potential action items that would support greater adoption of VGI.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2013

Impact of Wind and Solar on the Value of Energy Storage

This analysis evaluates how the value of energy storage changes when adding variable generation (VG) renewable energy resources to the grid. A series of VG energy penetration scenarios from 16% to 55% were generated for a utility system in the western United States. This operational value of storage (measured by its ability to reduce system production costs) was estimated in each VG scenario, considering provision of different services and with several sensitivities to fuel price and generation mix. Overall, the results found that the presence of VG increases the value of energy storage by lowering off-peak energy prices more than on-peak prices, leading to a greater opportunity to arbitrage this price difference. However, significant charging from renewables, and consequently a net reduction in carbon emissions, did not occur until VG penetration was in the range of 40%-50%. Increased penetration of VG also increases the potential value of storage when providing reserves, mainly by increasing the amount of reserves required by the system. Despite this increase in value, storage may face challenges in capturing the full benefits it provides. Due to suppression of on-/off-peak price differentials, reserve prices, and incomplete capture of certain system benefits (such as the cost of power plant starts), the revenue obtained by storage in a market setting appears to be substantially less than the net benefit (reduction in production costs) provided to the system. Furthermore, it is unclear how storage will actually incentivize large-scale deployment of renewables needed to substantially increase VG penetration. This demonstrates some of the additional challenges for storage deployed in restructured energy markets.

14 SOLAR ENERGY↗
DOE OSTI2021

The future evolution of energy-water-agriculture interconnectivity across the US

Abstract Energy, water, and agricultural resources across the globe are highly interconnected. This interconnectivity poses science challenges, such as understanding and modeling interconnections, as well as practical challenges, such as efficiently managing interdependent resource systems. Using the US as an example, this study seeks to define and explore how interconnectivity evolves over space and time under a range of influences. Concepts from graph theory and input–output analysis are used to visualize and quantify key intersectoral linkages using two new indices: the ‘Interconnectivity Magnitude Index’ and the ‘Interconnectivity Spread Index’. Using the Global Change Analysis Model (GCAM-USA), we explore the future evolution of these indices under four scenarios that explore a range of forces, including socioeconomic and technological change. Analysis is conducted at both national and state level spatial scales from 2015 to 2100. Results from a Reference scenario show that resource interconnectivity in the US is primarily driven by water use amongst different sectors, while changes in interconnectivity are driven by a decoupling of the water and electricity systems, as power plants become more water-efficient over time. High population and GDP growth results in relatively more decoupling of sectors, as a larger share of water and energy is used outside of interconnected sector feedback loops. Lower socioeconomic growth results in the opposite trend. Transitioning to a low-carbon economy increases interconnectivity because of the expansion of purpose-grown biomass, which strengthens the connections between water and energy. The results highlight that while some regions may experience similar sectoral stress projections, the composition of the intersectoral connectivity leading to that sectoral stress may call for distinctly different multi-sector co-management strategies. The methodology we introduce here can be applied in diverse geographical and sectoral contexts to enable better understanding of where, when, and how coupling or decoupling between sectors could evolve and be better managed.

Khan, Zarrar (ORCID:0000000281478553)↗
DOE OSTI2024

A data-driven sensor placement approach for detecting voltage violations in distribution systems

Stochastic fluctuations in power injections from distributed energy resources (DERs) combined with load variability can cause constraint violations (e.g., exceeded voltage limits) in electric distribution systems. To monitor grid operations, sensors are placed to measure important quantities such as the voltage magnitudes. Here, in this paper, we consider a sensor placement problem which seeks to identify locations for installing sensors that can capture all possible violations of voltage magnitude limits. We formulate a bilevel optimization problem that minimizes the number of sensors and avoids false sensor alarms in the upper level while ensuring detection of any voltage violations in the lower level. This problem is challenging due to the nonlinearity of the power flow equations and the presence of binary variables. Accordingly, we employ recently developed conservative linear approximations of the power flow equations that overestimate or underestimate the voltage magnitudes. By replacing the nonlinear power flow equations with conservative linear approximations, we can ensure that the resulting sensor locations and thresholds are sufficient to identify any constraint violations. Additionally, we apply various problem reformulations to significantly improve computational tractability while simultaneously ensuring an appropriate placement of sensors. Lastly, we improve the quality of the results via an approximate gradient descent method that adjusts the sensor thresholds. We demonstrate the effectiveness of our proposed method for several test cases, including a system with multiple switching configurations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Renewable hydrogen horizon: Geospatial techno-economic feasibility and life cycle greenhouse gas analysis in the Middle East and North Africa

Renewable hydrogen is receiving increasing attention for its potential as a flexible energy carrier in sectors such as transportation and industry. Specific cost and carbon intensity (CI) of renewable hydrogen production vary largely based on the location, owing to differences in renewable energy resources, as well as the supply chain dynamics. This study maps the techno-economic and life cycle greenhouse gas emissions of renewable hydrogen production in the Middle East and North Africa region, leveraging abundant solar and wind resources. The work investigates the variability in hydrogen costs and CI, optimally sizing proton-exchange membrane (PEM) electrolyzers to account for partial and cyclic loading, and explores standalone versus grid-connected systems. PEM capacity ratios of 52 %–63 % for photovoltaic (PV) systems and 28 %–82 % for wind systems were identified as optimal, with hydrogen production costs ranging from $\$3.8$-$\$4.8$/kg for PV and $2.0-$7.0/kg for wind. CIs span from 1.9 to 3.7 kg CO 2 ,eq /kg H 2 for PV and 0.4–7.7 kg CO 2,eq /kg H 2 for wind systems. The study highlights significant cost and CI reductions achievable with technological advancements and co-product revenue from oxygen and excess electricity sales.

Carbon Intensity↗
DOE OSTI2021

Ocean Energy for Ocean Worlds

Energy systems generating power from ocean waves, currents, tides, and thermal and salinity gradients are being developed to power oceanographic instrumentation and autonomous vehicles in Earth’s oceans. Exploration missions enabled or extended with in-situ energy harvesting on Earth may be similar to those on extra-terrestrial ocean worlds including mapping, biogeochemical sampling, acoustic monitoring, search for building blocks of life, and physical oceanographic characterization. With the addition of ocean energy available at the point of observation, data can be collected on larger spatial and temporal scales, enabling more comprehensive missions on other worlds. Power resource characteristics (e.g. current speed, variability) will similarly need to be assessed. Earth and extra-terrestrial ocean missions share vast technical challenges including high expense, need for advanced autonomy, ability to survive in the harshest environments, and limited communications or opportunities for intervention. Marine energy systems may be the only option for in-situ generation in certain scenarios (e.g., under a thick layer of ice) or advantageous over onboard energy storage options that may interfere with the natural environment (e.g., radioisotope thermoelectric generators). This work provides an overview of existing marine energy technology pertaining to ocean observations, highlights emerging research and development and new funding and program initiatives, explores similarities in mission requirements between terrestrial and extra-terrestrial applications, and suggests innovation pathways towards enabling high-value ocean world exploration powered by ocean energy.

Cavagnaro, Robert J.↗
DOE OSTI2021

Efficient loading of reduced data ensembles produced at ORNL SNS/HFIR neutron time-of-flight facilities

We present algorithmic improvements to the loading operations of certain reduced data ensembles produced from neutron scattering experiments at Oak Ridge National Laboratory (ORNL) facilities. Ensembles from multiple measurements are required to cover a wide range of the phase space of a sample material of interest. They are stored using the standard NeXus schema on individual HDF5 files. This makes it a scalability challenge, as the number of experiments stored increases in a single ensemble file. The present work follows up on our previous efforts on data management algorithms, to address identified input output (I/O) bottlenecks in Mantid, an open-source data analysis framework used across several neutron science facilities around the world. We reuse an in-memory binary-tree metadata index that resembles data access patterns, to provide a scalable search and extraction mechanism. In addition, several memory operations are refactored and optimized for the current common use cases, ranging most frequently from 10 to 180, and up to 360 separate measurement configurations. Results from this work show consistent speed ups in wall-clock time on the Mantid LoadMD routine, ranging from 19% to 23% on average, on ORNL production computing systems. The latter depends on the complexity of the targeted instrument-specific data and the system I/O and compute variability for the shared computational resources available to users of ORNL’s Spallation Neutron Source (SNS) and the High Flux Isotope Reactor (HFIR) instruments. Nevertheless, we continue to highlight the need for more research to address reduction challenges as experimental data volumes, user time and processing costs increase.

Godoy, William↗
DOE OSTI2016

Control and Coordination of Frequency Responsive Residential Water Heaters

Demand-side frequency control can complement traditional generator controls to maintain the stability of large electric systems in the face of rising uncertainty and variability associated with renewable energy resources. This paper presents a hierarchical frequency-based load control strategy that uses a supervisor to flexibly adjust control gains that a population of end-use loads respond to in a decentralized manner to help meet the NERC BAL-003-1 frequency response standard at both the area level and interconnection level. The load model is calibrated and used to model populations of frequency-responsive water heaters in a PowerWorld simulation of the U.S. Western Interconnection (WECC). The proposed design is implemented and demonstrated on physical water heaters in a laboratory setting. A significant fraction of the required frequency response in the WECC could be supplied by electric water heaters alone at penetration levels of less than 15%, while contributing to NERC requirements at the interconnection and area levels.

Williams, Tess L.↗
DOE OSTI2011

PV performance modeling workshop summary report.

During the development of a solar photovoltaic (PV) energy project, predicting expected energy production from a system is a key part of understanding system value. System energy production is a function of the system design and location, the mounting configuration, the power conversion system, and the module technology, as well as the solar resource. Even if all other variables are held constant, annual energy yield (kWh/kWp) will vary among module technologies because of differences in response to low-light levels and temperature. A number of PV system performance models have been developed and are in use, but little has been published on validation of these models or the accuracy and uncertainty of their output. With support from the U.S. Department of Energy's Solar Energy Technologies Program, Sandia National Laboratories organized a PV Performance Modeling Workshop in Albuquerque, New Mexico, September 22-23, 2010. The workshop was intended to address the current state of PV system models, develop a path forward for establishing best practices on PV system performance modeling, and set the stage for standardization of testing and validation procedures for models and input parameters. This report summarizes discussions and presentations from the workshop, as well as examines opportunities for collaborative efforts to develop objective comparisons between models and across sites and applications.

14 SOLAR ENERGY↗
DOE OSTI2012

Capacity Value of PV and Wind Generation in the NV Energy System

Calculation of photovoltaic (PV) and wind power capacity values is important for estimating additional load that can be served by new PV or wind installations in the electrical power system. It also is the basis for assigning capacity credit payments in systems with markets. Because of variability in solar and wind resources, PV and wind generation contribute to power system resource adequacy differently from conventional generation. Many different approaches to calculating PV and wind generation capacity values have been used by utilities and transmission operators. Using the NV Energy system as a study case, this report applies peak-period capacity factor (PPCF) and effective load carrying capability (ELCC) methods to calculate capacity values for renewable energy sources. We show the connection between the PPCF and ELCC methods in the process of deriving a simplified approach that approximates the ELCC method. This simplified approach does not require generation fleet data and provides the theoretical basis for a quick check on capacity value results of PV and wind generation. The diminishing return of capacity benefit as renewable generation increases is conveniently explained using the simplified capacity value approach.

capacity value↗
DOE OSTI2016

Sensor network based solar forecasting using a local vector autoregressive ridge framework

The significant improvements and falling costs of photovoltaic (PV) technology make solar energy a promising resource, yet the cloud induced variability of surface solar irradiance inhibits its effective use in grid-tied PV generation. Short-term irradiance forecasting, especially on the minute scale, is critically important for grid system stability and auxiliary power source management. Compared to the trending sky imaging devices, irradiance sensors are inexpensive and easy to deploy but related forecasting methods have not been well researched. The prominent challenge of applying classic time series models on a network of irradiance sensors is to address their varying spatio-temporal correlations due to local changes in cloud conditions. We propose a local vector autoregressive framework with ridge regularization to forecast irradiance without explicitly determining the wind field or cloud movement. By using local training data, our learned forecast model is adaptive to local cloud conditions and by using regularization, we overcome the risk of overfitting from the limited training data. Our systematic experimental results showed an average of 19.7% RMSE and 20.2% MAE improvement over the benchmark Persistent Model for 1-5 minute forecasts on a comprehensive 25-day dataset.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Bookending the Opportunity to Lower Wind’s LCOE by Reducing the Uncertainty Surrounding Annual Energy Production

Reducing the performance risk surrounding a wind project can potentially lead to a lower weighted-average cost of capital (WACC), and hence a lower levelized cost of energy (LCOE), through an advantageous shift in capital structure, and possibly also a reduction in the cost of capital. Specifically, a reduction in performance risk will move the 1-year P99 annual energy production (AEP) estimate closer to the P50 AEP estimate, which in turn reduces the minimum debt service coverage ratio (DSCR) required by lenders, thereby allowing the project to be financed with a greater proportion of low-cost debt. In addition, a reduction in performance risk might also reduce the cost of one or more of the three sources of capital that are commonly used to finance wind projects: sponsor or cash equity, tax equity, and/or debt. Preliminary internal LBNL analysis of the maximum possible LCOE reduction attainable from reducing the performance risk of a wind project found a potentially significant opportunity for LCOE reduction of ~$10/MWh, by reducing the P50 DSCR to its theoretical minimum value of 1.0 (Bolinger 2015b, 2014) and by reducing the cost of sponsor equity and debt by one-third to one-half each (Bolinger 2015a, 2015b). However, with FY17 funding from the U.S. Department of Energy’s Atmosphere to Electrons (A2e) Performance Risk, Uncertainty, and Finance (PRUF) initiative, LBNL has been revisiting this “bookending” exercise in more depth, and now believes that its earlier preliminary assessment of the LCOE reduction opportunity was overstated. This reassessment is based on two new-found understandings: (1) Due to ever-present and largely irreducible inter-annual variability (IAV) in the wind resource, the minimum required DSCR cannot possibly fall to 1.0 (on a P50 basis), and (2) A reduction in AEP uncertainty will not necessarily lead to a reduction in the cost of capital, meaning that a shift in capital structure is perhaps the best that can be expected (perhaps along with a modest decline in the cost of cash equity as new investors enter the market).

17 WIND ENERGY↗
DOE OSTI2017

Subalpine and alpine species range shifts with climate change: temperature and soil moisture manipulations to test species and population responses (Final Report)

The Alpine Treeline Warming Experiment (ATWE) was established in 2008-2009 to experimentally test how climate change would affect species ranges at and beyond the cold edge limits of forest growth. One key finding was that warming can hinder upslope tree migration by increasing seedling water stress and reducing seedling survival. We found evidence for more conservative traits in seedlings from high elevation tree populations, which may further slow tree expansion into the current alpine. In the alpine, warming may only increase productivity if summer rain increases, as dominant alpine life forms were negatively affected by heating unless water was added. The ATWE experimental design and focus on dynamic vegetation were novel, and the setting at the mountain treeline transition was important to understanding effects of climate variability and change on water resources, carbon cycling and biodiversity. We ended climate manipulations and removed associated infrastructure from the field in 2016 following 8 years of data collection. To date, 5 MS theses and 18 peer-reviewed papers have been published or accepted (including one that yielded the cover image in Global Change Biology, left), and 1 PhD thesis and at least 5 papers are in review or active development. Numerous past seasonal and long-term field staff have gone on to graduate school, and subsequently to research positions as university faculty or agency scientists. PI Kueppers continues collecting data on soil microclimate and seedling survival for long-lived tree species to fill a critical gap in knowledge regarding the seedling-to-sapling transition across species and elevations. Primary project data is housed on the project’s virtual server, organized according to topic. Published data and model code is available either through the project website or via other permanent archives. Our results are influencing understanding and modeling of climate and genetic constraints on dynamic vegetation change across the landscape, particularly in the mountains of the Western U.S.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Methods for Translating ReEDS Solutions to Production Cost Modeling Tools

Capacity expansion modeling tools are increasingly being utilized to investigate a wide range of potential future scenarios, particularly with high penetrations of variable and energy-constrained resources that may be operated differently than current dispatch paradigms. While capacity expansion models are particularly adept at making investment decisions for future years, they must make compromises in operational aspects to maintain computational tractability. However, it is of high interest to determine if such future systems would be able to maintain reliability during a variety of grid conditions, and to identify any potential operational challenges for these systems at finer temporal resolution than is typically captured in CEMs. As such, there is value in having an automated tool that can convert many CEM investment pathways into inputs for a more detailed production cost model, in this case the PLEXOS commercial software package. This paper describes the methodology used to make that translation for the NREL-developed ReEDS model, making use of two internally developed tools - PIDG and beetle - along with a set of processing scripts. We describe the current assumptions, data sets, and important operational characteristics of these translations along with an example of the extended analyses that may be done through this connection.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

A Review of Quantum Computing Technologies in Power System Optimization

As modern power grids increasingly integrate variable renewable generation, distributed energy resources, and energy storage systems, classical optimization techniques are facing unprecedented challenges. This review examines the emerging application of quantum computing to overcome these challenges in power system optimization, including optimal power flow (OPF), unit commitment (UC), economic dispatch (ED), and intelligent switching and topology optimization (IS-TO). Recent research has introduced various quantum methodologies—such as gate-based, annealing-based, variational algorithms, and quantum-inspired algorithms—to address the combinatorial complexity inherent in grid reconfiguration and energy management. The review summaries the quantum algorithms, quantum devices and the power system test cases, highlighting hybrid quantum–classical strategies that leverage the complementary strengths of both paradigms. Some quantum advantages have been observed, including theoretical speedup, accurate simulation results, scalable qubit usage, efficient QUBO mapping. In particular, the review emphasizes the importance of integrating quantum optimization techniques with classical control frameworks, these hybrid approaches demonstrate the potential to improve real-time grid management and operational reliability. A significant portion of the analysis is devoted to the practical limitations of current quantum devices. Present-day quantum hardware, operating in the noisy intermediate-scale quantum (NISQ) era, remains highly sensitive to noise and limited in qubit connectivity, which constrains the scale and accuracy of implemented algorithms. The review delves into specific challenges such as the need for qubit-efficient encoding techniques and error mitigation strategies that are critical for handling real-world grid optimization problems. In addition, the work draws attention to the performance discrepancies between theoretical quantum speedups and experimental validations, underscoring the importance of rigorous benchmark studies using representative power grid test cases. In summary, this review highlights both the promise and limitations of quantum computing for power system optimization. It provides a comprehensive overview of the state-of-the-art technologies, categorizes recent advancements in algorithm design, and discusses practical considerations for implementation, and serves as an informative resource on current research. Future research directions include developing robust hybrid frameworks, advancing qubit-efficient formulations, and scaling up experimental demonstrations to confirm the theoretical advantages of quantum methods in large-scale power system operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Autonomous Energy Grids

Current frameworks to monitor, control, and optimize large-scale energy systems are becoming increasingly inadequate because of significantly high penetration levels of variable generation and distributed energy resources being integrated into electric power systems; the deluge of data from pervasive metering of energy grids; and a variety of new market mechanisms, including multilevel ancillary services. This paper outlines the concept of autonomous energy grids (AEGs). These systems are supported by a scalable, reconfigurable, and self-organizing information and control infrastructure, are extremely secure and resilient (self-healing), and can self-optimize in real time to ensure economic and reliable performance while systematically integrating energy in all forms. AEGs rely on cellular building blocks that can self-optimize when isolated from a larger grid and participate in optimal operation when interconnected to a larger grid. This paper describes the key concepts and research necessary in the broad domains of optimization theory, control theory, big data analytics, and complex system theory and modeling to realize the AEG vision.

autonomous energy grids↗
DOE OSTI2010

Evaluation of PV performance models and their impact on project risk.

Photovoltaic systems are often priced in $/W{sub p}, where Wp refers to the DC power rating of the modules at Standard Test Conditions (1000 W/m{sup 2}, 25 C cell temperature) and $ refers to the installed cost of the system. However, the true value of the system is in the energy it will produce in kWhs, not the power rating. System energy production is a function of the system design and location, the mounting configuration, the power conversion system, and the module technology, as well as the solar resource. Even if all other variables are held constant, the annual energy yield (kWh/kW{sup p}) will vary among module technologies because of differences in response to low-light levels and temperature. Understanding energy yield is a key part of understanding system value. System performance models are used during project development to estimate the expected output of PV systems for a given design and location. Performance modeling is normally done by the system designer/system integrator. Often, an independent engineer will also model system output during a due diligence review of a project. A variety of system performance models are available. The most commonly used modeling tool for project development and due diligence in the United States is probably PVsyst, while those seeking a quick answer to expected energy production may use PVWatts. In this paper, we examine the variation in predicted energy output among modeling tools and users and compare that to measured output.

14 SOLAR ENERGY↗