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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 sources NASA NTRS DOE OSTI

DOE OSTI2020

Demand Response Potential from the Bulk Grid Perspective

Gain insight from an exploration of the potential value of demand response (DR) in future power systems. Using hourly DR resource estimates by sub-sector, electrical end-use, and grid service, we incorporate virtual generator models of DR resources into large-scale production cost models, and analyze the grid-level impacts in terms of the ability of DR to reduce system costs and potentially help integrate renewables or reduce system emissions. Additionally, we examine the value of DR, its revenue, and how these change under different system conditions. In this panel session, we will describe our input data and modeling methods, provide an overview of the production cost models used, and summarize key results from analyses in Florida and the western United States. Findings include variations in DR value by end-use and geography, an ability to increase economic PV deployment potential in high penetration systems, and mixed emissions outcomes depending on system composition and fuel prices.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Cost-Effectiveness of Local Distribution Tied Solar within KyMEA

KyMEA is a Kentucky joint action agency which provides electric transmission and energy services to eleven municipal utilities. One of KyMEA's member cities, Frankfort, has set a goal to reach 100% renewable generation for the city load by 2023 and 100% carbon free city operations and 100% renewable generation community-wide by 2030. The aim of this research was to determine if the Frankfort city government could reduce its power costs by implementing distribution tied solar while also avoiding any cross subsidization between other KyMEA member utilities or other Frankfort Plant Board customers. The project simulated the KyMEA generation portfolio and transmission system from KyMEA's cost perspective using the NREL Engage capacity expansion model. Running in operate mode, the authors used Engage to compare KyMEA's costs to operate the baseline system to its costs to operate the system with Frankfort meeting some of its load with distribution-tied PV. The cost perspective of Frankfort Plant Board, meanwhile, was simulated using Python. The simulations indicated that it would be cost-beneficial to KyMEA, i.e., that no additional costs would be borne by other members, and that it would be cost-neutral to Frankfort Plant Board for Frankfort city government to implement distribution tied utility-scale PV. The new load attributes resulting from the distribution-tied PV lowered KyMEA's cost to serve load by improving system load factor, reducing system transmission and energy costs, and increasing revenues from market energy sales.

14 SOLAR ENERGY↗

DOE OSTI2023

Follow-Up from the Photovoltaic Reliability Workshop (PVRW): Cost Modeling Capabilities to Evaluate Trends in PV Technologies

NREL's Solar and Storage Techno-Economic Analysis (TEA) team reviews live polling results from the Photovoltaic Reliability Workshop (PVRW) related to module technology trends believed to have the greatest reliability impacts. Then, the team reviews the publicly available tools and inputs necessary for evaluating reliability tradeoffs between initial module and system costs, degradation profiles, and levelized cost of energy (LCOE). These tools include the online Detailed Cost Analysis Models (DCAM) and the DuraMAT LCOE calculator.

cost modeling↗

DOE OSTI2021

Hardware-in-the-Loop Evaluation of an Advanced Distributed Energy Resource Management Algorithm

This paper presents the laboratory performance evaluation of voltage regulation under a new distributed energy resource management system (DERMS) algorithm via an advanced hardware-in-tbe-loop (HIL) platform. The HIL platform provides realistic testing in a laboratory environment, including the accurate modeling of a full-scale real-world distribution system from a utility partner, the DERMS software controller, and power hardware photovoltaic (PV) inverters. The new DERMS algorithm is developed based on online multiobjective optimization (OMOO) algorithms that perform fast dispatch of distributed solar PV simulated in a real-time digital simulator and real physical hardware devices. Experimental tests confirm the correct functioning of the HIL platform for evaluating controller algorithms and satisfactory voltage regulation performance of the developed OMOO algorithms.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2024

PV-Finder: ML Based Algorithm for Primary Vertex Identification

he CMS detector at the High-Luminosity Large Hadron Collider (HL-LHC) will operate in challenging conditions with expected pile-up of up to 200 collisions per bunch crossing, necessitating the development of a more resilient primary vertex (PV) reconstruction method to ensure the integrity of data analysis and the efficiency of the CMS triggering system. This contribution describes preliminary studies on a new ML based PV-Finder method for PV identification. The method is based on a model trained using Kernel Density Estimations (KDEs) derived from the positions of reconstructed tracks at the beamline, incorporating uncertainties from track parameters. It also utilizes target histograms, modeled as Gaussian distributions centered on the actual ground truth values of specific primary vertices.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS2010

Low Mass Printable Devices for Energy Capture, Storage, and Use

The energy-efficient, environmentally friendly technology that will be presented is the result of a Space Act Agreement between NthDegree Technologies Worldwide, Inc., and the National Aeronautics and Space Administration's (NASA's) Marshall Space Flight Center (MSFC). The work combines semiconductor and printing technologies to advance lightweight electronic and photonic devices having excellent potential for commercial and exploration applications. Device development involves three projects that relate to energy generation and consumption: (1) a low-mass efficient (low power, low heat emission) micro light-emitting diode (LED) area lighting device; (2) a low-mass omni-directional efficient photovoltaic (PV) device with significantly improved energy capture; and (3) a new approach to building super-capacitors. These three technologies, energy capture, storage, and usage (e.g., lighting), represent a systematic approach for building efficient local micro-grids that are commercially feasible; furthermore, these same technologies, appropriately replacing lighting with lightweight power generation, will be useful for enabling inner planetary missions using smaller launch vehicles and to facilitate surface operations during lunar and planetary surface missions. The PV device model is a two sphere, light trapped sheet approximately 2-mm thick. The model suggests a significant improvement over current thin film systems. For lighting applications, all three technology components are printable in-line by printing sequential layers on a standard screen or flexographic direct impact press using the three-dimensional printing technique (3DFM) patented by NthDegree. One primary contribution to this work in the near term by the MSFC is to test the robustness of prototype devices in the harsh environments that prevail in space and on the lunar surface. It is anticipated that this composite device, of which the lighting component has passed off-gassing testing, will function appropriately in such environments consistent with NASA s exploration missions. Advanced technologies such as this show promise for both space flight and terrestrial applications.

Frazier, Donald O.↗

DOE OSTI2021

Hardware-in-the-Loop Evaluation of an Advanced Distributed Energy Resource Management Algorithm

This paper presents the laboratory performance evaluation of voltage regulation under a new distributed energy resource management system (DERMS) algorithm via an advanced hardware-in-the-loop (HIL) platform. The HIL platform provides realistic testing in a laboratory environment, including the accurate modeling of a full-scale real-world distribution system from a utility partner, the DERMS software controller, and power hardware photovoltaic (PV) inverters. The new DERMS algorithm is developed based on online multi-objective optimization (OMOO) algorithms that perform fast dispatch of distributed solar PV simulated in a real-time digital simulator and real physical hardware devices. Experimental tests confirm the correct functioning of the HIL platform for evaluating controller algorithms and satisfactory voltage regulation performance of the developed OMOO algorithms.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2021

Hardware-in-the-Loop Evaluation of an Advanced Distributed Energy Resource Management Algorithm: Preprint

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2023

Lessons Learned From Techno-Economic Analysis of Solar Photovoltaics and Battery Energy Storage at a Vietnam Industrial Park

Through the Clean Energy Investment Accelerator (CEIA), engineers from the United States (U.S.) National Renewable Energy Laboratory (NREL) conducted a case study analysis evaluating the techno-economic feasibility of battery energy storage systems (BESS) at an industrial park in Vietnam. The analysis uses NREL's REopt platform, a distributed energy modeling and optimization tool, to identify the cost-optimal system sizing for BESS, in conjunction with onsite renewables such as solar photovoltaics (PV), to reduce electricity costs, increase onsite renewable energy generation utilization, and improve resilience to grid outages.

batteries↗

DOE OSTI2026

Stakeholder-guided holistic, Adaptive Framework for enhancing community Energy Resilience (SAFER) (Final Technical Report)

The Stakeholder-guided holistic, Adaptive Framework for enhancing community Energy Resilience (SAFER) project advances resilience science and engineering by addressing challenges in rural Kansas communities where aging infrastructure, extreme weather, and socioeconomic disparities heighten vulnerability to energy disruptions. Traditional approaches often focus on technical performance while overlooking community concerns and priorities. SAFER responds by integrating community perspectives with advanced analytical frameworks to create a holistic model for measuring and improving resilience. Project objectives included developing novel resilience metrics, advancing modeling frameworks that capture interdependencies across infrastructures, and embedding community-centric indicators directly into planning processes for distributed energy resources. The key technical innovations included the creation of self-organizing map (SOM)-based indices for objective resilience quantification, hetero-functional graph theory (HFGT) models linking power, water, transportation, and community assets, and graph neural network (GNN) tools for identifying critical nodes in complex systems. Community-centric energy planning was demonstrated through optimal siting and sizing of (photovoltaic) PV and battery storage, ensuring resilience enhancements also addressed energy burden and energy insecurity. SAFER engaged community partners in Dodge City and Ford County through surveys, focus groups, and workshops, generating more than 600 responses that established baseline measures of energy burden, financial insecurity, and willingness-to-pay to avoid outages. This data, organized in terms of a community capitals framework, informed the development of weighted reliability indices that better reflect community costs than traditional utility metrics. SAFER’s GNN-based critical node identification framework identified expert-labelled critical nodes with over 99% accuracy, while also uncovering additional functionalities essential for proactive resilience planning. The project’s models demonstrated that optimal PV and storage deployment could improve resilience indices by over 11 percent, with dispatch strategies further enhancing outcomes, confirming both the technical effectiveness and economic feasibility of these approaches. Through its combined emphasis on rigorous modeling, community-focused planning, and community engagement, SAFER advances the state of resilience research while delivering direct benefits to rural communities. The project provides tools, guidelines, and resilience heatmaps that help utilities, local governments, and residents better anticipate disruptions, prioritize investments, and strengthen the capacity to withstand and recover from energy-related hazards. Furthermore, the developed HFG and GNN frameworks are designed for transferability, allowing them to be adapted for resilience planning in other communities with minimal retraining. This inductive learning capability provides a scalable pathway to extend the SAFER project’s impact. Thus, creating a foundation for a nationally applicable model of infrastructure resilience. Additionally, the HFG can also be extended to include other FEMA community lifelines.

14 SOLAR ENERGY↗

DOE OSTI2021

Renewable energy analysis in indigenous communities using bottom-up demand prediction

This paper provides a methodology for the holistic analysis of hybrid renewable energy systems in rural communities. Electric demand is an important component for modeling and analysis of renewable energy systems. Typically, electric demand data is not available due to the internal privacy policies of utility providers. Therefore, this study proposes the use of bottom-up approaches for the development of the electric demand profile, considering the general homogeneity of residential and commercial buildings in rural communities. As a test case, this study develops the electric demand profile and investigates the technical and environmental feasibility of a hybrid renewable energy system for the New Town community on the Fort Berthold Indian Reservation (FBIR) in North Dakota. This study conducts the hybrid renewable energy system’s analysis by developing scripts in the LK scripting language and integrating System Advisor Model software’s open-source modules for modeling of renewable energy systems. Here, the results for the validation testbed of this study show that hybrid renewable resources have higher ratios of energy used for self-consumption to the total energy generated compared to stand-alone wind and PV farms.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Solid-State Transformer and Hybrid Transformer With Integrated Energy Storage in Active Distribution Grids: Technical and Economic Comparison, Dispatch, and Control

Solid-state transformer (SST) and hybrid transformer (HT) are promising alternatives to the line-frequency transformer (LFT) in smart grids. The SST features medium-frequency isolation, full controllability for voltage regulation, reactive power compensation, and the capability of battery energy storage system (BESS) integration with multiport configuration. The HT has a partially-rated converter for fractional controllability and can integrate a small BESS. Fast grid-edge voltage fluctuations from increased solar photovoltaic (PV) and electric vehicle (EV) penetration are difficult to manage for mechanical load tap changers. Hence, along with the trend towards more BESS in the grid, the controllability and the storage integration capability of the SST and HT are of strong interest. However, a review of literature shows existing SST and HT research is mostly at converter level, while system-level assessments are scarce. Assessing technical and economic impacts is critical to understanding the benefits and role of the SST and HT to guide future research, which is presented for the first time in this article. Experimental results from medium-voltage (MV) SST and MV HT prototypes are shown to confirm equipment-level feasibility, where the voltage controllability waveforms of a MV HT prototype are reported for the first time. Comparative simulations are performed on a modified IEEE 34-bus system. Here, a grid-model-less decentralized grid-edge voltage control method and a day-ahead BESS dispatch method are proposed for the SST and HT. The simulations show that the SST and HT with integrated storage can host more PV, achieve peak shaving, mitigate voltage fluctuation and reverse power flow, and support energy arbitrage for operational cost reduction, as compared to the LFT. Moreover, comprehensive analyses of net present value (NPV) and internal rate of return (IRR) are performed under different installed PV capacities, HT’s partial converter ratings, and BESS capacities. Sensitivities to future cost reductions of the PV and BESS are studied. Although the NPV and IRR are currently negative, 60% capital cost reduction or 150% revenue increase will make the SST and HT economically viable in the use case studied.

14 SOLAR ENERGY↗

DOE OSTI2023

Modeling and design of III-V heterojunction solar cells for enhanced performance

Heterojunctions can increase the efficiency of solar cell devices relative to homojunctions, but there is a large parameter space with significant tradeoffs that must be considered. Here, we present an experimental and computational study of III-V heterojunction solar cells and show how the emitter doping, emitter band gap, and heteroband offsets impact device efficiency. Efficiency is maximized by pushing the junction depletion region into the wider band gap material while minimizing the effects of heteroband offsets through optimized choice of emitter band gap, emitter electron affinity, and/or emitter doping density. We use these results to guide optimization of devices grown by halide vapor phase epitaxy, achieving 27% efficiency in a GaAs/GaInPAs heterojunction device. We also show that heterojunctions yield proportionally larger efficiency improvements in lower-quality materials. Although the modeling was developed and validated using III-V materials, the results are theoretically applicable to materials systems outside III-Vs.

14 SOLAR ENERGY↗

DOE OSTI2022

Impact of Thermal Plant Operational Flexibility on VRE Curtailment: The Curtailment Paradox

This presentation provides an overview of a previously published paper in Joule "The curtailment paradox in the transition to high solar power systems." The adoption of PV and battery storage has accelerated globally in recent years, driven by rapid cost declines. A corresponding increase in curtailment is anticipated as PV growth continues. This study explores the effect of system flexibility options on curtailment across increasing PV penetration levels. Results highlight a paradox where thermal generator flexibility matters most only at mid-PV penetration levels (25%-40%), and a misalignment exists where PV provides system value by providing operating reserves without sufficient opportunity for monetary compensation.

capacity expansion modeling↗

NASA NTRS2016

What Causes Mars' Annular Polar Vortices?

A distinctive feature of the Martian atmosphere is that the winter polar vortices exhibit annuli of high potential vorticity (PV) with a local minimum near the pole. These annuli are seen in observations, reanalyses, and free-running general circulation model simulations of Mars, but are not generally a feature of Earth's polar vortices, where there is a monotonic increase in magnitude of PV with latitude. The creation and maintenance of the annular polar vortices on Mars are not well understood. Here we use simulations with a Martian general circulation model to the show that annular vortices are related to another distinctive, and possibly unique in the solar system, feature of the Martian atmosphere: the condensation of the predominant atmospheric gas species (CO2) in polar winter regions. The latent heat associated with CO2 condensation leads to destruction of PV in the polar lower atmosphere, inducing the formation of an annular PV structure.

Toigo, A. D.↗

DOE OSTI2021

Influence of Business Models on PV-Battery Dispatch Decisions and Market Value

PV-battery hybrid projects dominate interconnection queues in some regions in the United States, but few projects have been operational long enough to assess how the hybrid capabilities may be used in practice. We interview plant operators and analyze empirical dispatch data for eleven large-scale PV-battery hybrids in three organized wholesale markets in the United States. We use the dispatch data and wholesale market prices to estimate the market value of our sample hybrids in 2020. The empirical increase in market value of a PV-battery hybrid relative to a standalone PV plant varies by project and ranges from $\$$1 to $\$$48/MWhsolar. The premium is driven by market, location, technical characteristics of the PV and battery asset, and battery dispatch strategies. In contrast to the widespread assumptions in the PV-battery hybrid modeling literature, only three of the eleven project operators optimize battery usage for wholesale market revenue as merchant plants. Instead, the majority of operators in the sample have alternate objectives. For example, load-serving entities target peak load reductions, incentive program participants focus on compliance with program requirements, and large energy consumers prioritize resiliency and utility bill minimization. Understanding prevalent dispatch signals and the degree of alignment with system-wide grid needs can increase the market value of PV-battery hybrids.

14 SOLAR ENERGY↗

DOE OSTI2021

Deciphering Degradation: Machine Learning on Real-World Performance Data (Final Report)

This project addresses a fundamental flaw in solar PV research and solar project financing; the assumed rate of degradation for solar plants. The solar industry currently relies on an out-dated report that observed a 0.5% degradation rate based on a small sample size of systems (~100). While the research conducted at the time was new and innovative, the solar community has not updated this research and universally applies this 0.5% degradation assumption in financial models. Our project updates this assumption by analyzing observed degradation from the industry’s largest dataset of operating solar assets (>10,000 systems) and creating the first machine-learning model based on these observed results to quantify and identify features that drive degradation. There are two strategic goals for this award: reduce the cost of capital (enable solar to attract more capital) and improve the reliability of solar itself. These dual goals are achieved by leveraging an industry dataset to observe system degradation on a large scale, deploying advanced data analysis and machine learning methods to quantify and predict system reliability, and engaging with industry stakeholders to help them accurately price degradation in financial models.

14 SOLAR ENERGY↗

DOE OSTI2024

Distributionally Robust Decentralized Volt-Var Control With Network Reconfiguration

Here, this paper presents a decentralized volt-var optimization (VVO) and network reconfiguration strategy to address the challenges arising from the growing integration of distributed energy resources, particularly photovoltaic (PV) generation units, in active distribution networks. To reconcile control measures with different time resolutions and empower local control centers to handle intermittency locally, the proposed approach leverages a two-stage distributionally robust optimization; decisions on slow-responding control measures and set points that link neighboring subnetworks are made in advance while considering all plausible distributions of uncertain PV outputs. We present a decomposition algorithm with an acceleration scheme for solving the proposed model. Numerical experiments on the IEEE 123 bus distribution system are given to demonstrate its outstanding out-of-sample performance and computational efficiency, which suggests that the proposed method can effectively localize uncertainty via risk-informed proactive timely decisions.

24 POWER TRANSMISSION AND DISTRIBUTION↗