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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

State Strategies for Valuing Distributed Energy Resources in Cost-Effective Locations

The deployment of Distributed Energy Resources (DERs), small-scale electricity generation sources or controllable loads connected to the distribution system or a facility served by the distribution system, can have wide-ranging and positive impacts for the electricity grid. Interconnecting a DER in a high-value location on the grid can unlock benefits beyond those that accrue to the off-taker or asset owner. When DERs are deployed optimally, not only can the individual consumer benefit, but the local utility, the electricity system, and other ratepayers can benefit as well. By engaging in locational DER planning, states can help protect their communities and ratepayers and can help ensure the reliability and resilience of the electricity system. This report explores states' role in better integrating locational value into DER siting and development. It first outlines some of the benefits that can be achieved through the deployment of DERs in high-value locations. It then looks at state policy and regulatory roles and the tools states can use to influence the location of DER development. The report next examines the challenges of locational DER planning, with a particular focus on the challenges that states face in influencing the location of DER development. It highlights three case studies from states that engaged in locational DER planning through the Multistate Initiative to Develop Solar in Locations that Provide Benefits to the Grid and then presents several lessons learned from these efforts. The final section of this report summarizes some recent publications related to locational value of DERs in the form of an abbreviated literature review.

14 SOLAR ENERGY↗
DOE OSTI2023

Defects go green: using defects in nanomaterials for renewable energy and environmental sustainability

Induction of point defects in nanomaterials can bestow upon them entirely new physics or augment their pre-existing physical properties, thereby expanding their potential use in green energy technology. Predicting structure-property relationships for defects a priori is challenging, and developing methods for precise control of defect type, density, or structural distribution during synthesis is an even more formidable task. Hence, tuning the defect structure to tailor nanomaterials for enhanced device performance remains an underutilized tool in materials design. We review here the state of nanomaterial design through the lens of computational prediction of defect properties for green energy technology, and synthesis methods to control defect formation for optimal performance. We illustrate the efficacy of defect-focused approaches for refining nanomaterial physics by describing several specific applications where these techniques hold potential. Most notably, we focus on quantum dots for reabsorption-free solar windows and net-zero emission buildings, oxide cathodes for high energy density lithium-ion batteries and electric vehicles, and transition metal dichalcogenides for electrocatalytic green hydrogen production and carbon-free fuels.

14 SOLAR ENERGY↗
DOE OSTI2023

Conservation of Helium while Maintaining High System Purity

Recent helium shortages and helium price increases have lead to an increased emphasis being placed on conserving helium. The need to conserve helium must be balanced with need to maintain the high levels of purity necessary to prevent operational problems caused by contamination. Helium losses and contamination control are especially important for test stands that have cryogenic distribution systems operating continuously with frequent changeover of cryogenic temperature components that are being tested. This paper describes a mathematical model to estimate the quantity of helium lost and the purity of the helium after the pump and backfill procedure is complete. The process to determine the optimal time during pump down to cut off pumping and start backfilling is described. There is a tradeoff between trying to achieve the lowest possible pressure during pumping and the quantity of air leaking into the volume while pumping is occurring. An additional benefit of careful selection of pump and backfill parameters in conjunction with real-time pressure monitoring can reduce the labor and time required to complete a successful pump and backfill procedure. This paper is intended to be a tool for engineers to review their pump and backfill procedures and measured data to optimize helium losses, system purity, and labor required.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

2022 Small Turbine Certification Awardee: Ryse Energy LLC - Americas

To meet the growing demands of the distributed wind market and comply with current U.S. standards, Ryse Energy LLC-Americas (Ryse Energy, formerly Primus Wind Power) plans to update all six products in its AIR Range family of micro wind turbines. A new, more cost-effective circuit board will be paired with other hardware upgrades, advanced control algorithms, and a Bluetooth function that allows users to more easily program and control the turbine. This Competitiveness Improvement Project (CIP) award will fund certification testing of the new circuit board to make sure it meets American National Standards Institute/American Clean Power Association (ANSI/ACP) and UL Federal Communications Commission (FCC) safety and quality standards. Primus Wind Power developed the prototype for the new circuit board with an earlier round of CIP funding, and Primus has received CIP awards supporting other certification and optimization projects.

CIP↗
DOE OSTI2016

Metal oxide core shell nanostructures as building blocks for efficient light emission (SISGR)

The objective of this research is to synthesize core-shell nano-structured metal oxide materials and investigate their structural, electronic and optical properties to understand the microscopic pathways governing the energy conversion process, thereby controlling and improving their efficiency. Specifically, the goal is to use a single metal oxide core-shell nanostructure and a single excitation source to generate photons with long emission lifetime over the entire visible spectrum and when controlled at the right ratio, generating white light. In order to achieve this goal, we need to control the energy transfer between light emitting elements, which dictates the control of their interatomic spacing and spatial distribution. We developed an economical wet chemical process to form the nanostructured core and to control the thickness and composition of the shell layers. With the help from using DOE funded synchrotron radiation facility, we delineated the growth mechanism of the nano-structured core and the shell layers, thereby enhancing our understanding of structure-property relation in these materials. Using the upconversion luminescence and the lifetime measurements as effective feedback to materials sysnthes is and integration, we demonstrated improved luminescence lifetimes of the core-shell nano-structures and quantified the optimal core-multi-shell structure with optimum shell thickness and composition. We developed a rare-earths co-doped LaPO 4 core-multishell structure in order to produce a single white light source. It was decided that the mutli-shell method would produce the largest increase in luminescence efficiency while limiting any energy transfer that may occur between the dopant ions. All samples resulted in emission spectra within the accepted range of white light generation based on the converted CIE color coordinates. The white light obtained varied between warm and cool white depending on the layering architecture, allowing for the utilization into a wide range of applications. With DOE’s overall mandate to reduce the US’s dependence on rare-earth elements, this work demonstrated how to make every RE atom count toward white light generation by controlling their spatial distribution thereby significantly reducing the amount needed. This is one approach toward reducing the dependence on RE elements and the strategies we developed can be used in discovering RE-free systems. The project is of benefit to the general public as it improves the optical efficiency of these materials, while reducing US’s dependence on RE elements.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

MPC-based double-layer real-time conditional cSelf-restoration for interconnected microgrids

In this report, we propose a novel model predictive control(MPC)-based real-time conditional self-restoration energy management system (CSR-EMS) for interconnected microgrids (IMGs) integrated with renewable energy sources (RESs) and energy storage systems (ESSs). Superior to the existing IMG self-restoration methods, the “conditionality” of the proposed CSR-EMS can economically realize self-restoration and grid-assisted restoration during energy deficiency or faults, in both islanded and grid-connected modes. Cost minimization is implemented as the objective function to judge in real-time which restoration mode is economically preferred. The proposed CSR-EMS comprises two layers–the lower layer operates locally to eliminate electricity fluctuations created by RESs and ensure economic effectiveness within an MG, whereas the upper layer oversees the real-time operational status of the IMG system and determines power exchange among microgrids (MGs) during abnormalities. In detail, when a microgrid inside the IMG system experiences an energy deficiency, the CSR-EMS, on an MPC basis, intelligently optimizes power production from each dispatchable distributed generator (DG), ESS, power imported from the main grid, and power exchange among the IMGs to maintain the demand–supply balance, while considering system recovery cost, state of charge (SoC) of ESSs and operation modes of the IMGs (i.e., grid-connected or islanded mode). Simulation results and comparisons with existing IMG self-healing EMSs demonstrate the economic efficacy of the proposed CSR-EMS strategy during normal and abnormal operations, which can be used as an energy control framework for modern power systems with multiple interconnected microgrids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Understanding and control of Zener pinning via phase field and ensemble learning

Zener pinning refers to the dispersion of fine particles which influences grain size distribution via movement of grain boundaries in a polycrystalline material. Grain size distribution in polycrystals has a significant impact on their properties including physical, chemical, mechanical, and optical to name a few. We explore the use of Phase-field modeling and machine-learning techniques to understand and improve the control of grain size distribution via Zener pinning in polycrystalline materials. We develop a machine learning model that determines the relative importance of various parameters to exercise microstructure control via Zener pinning. Our workflow combines high-throughput phase-field simulations and machine learning to address the computational bottlenecks associated with large-scale simulations as well as identify features necessary for microstructure control in polycrystals. A random forest (RF) regression model was developed to predict grain sizes based on five Phase-field model parameters, achieving an average prediction error of 0.72 nm for the training data and 1.44 nm for the test data. The importance of the input parameters is analyzed using the SHapley Additive exPlanations (SHAP) approach which reveals that diffusivity, volume fraction, and particle diameter are the most important parameters in determining the final grain size. These findings will allow us to select the best second-phase particles, optimize grain size distributions and thus design microstructures with the desired properties. The developed method is a highly versatile and generalizable approach that can be used to assess the combined effects of individual features in the presence of multiple variables.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Distributionally Robust Building Load Control to Compensate Fluctuations in Solar Power Generation

This paper investigates the use of a collection of dispatchable heating, ventilation and air conditioning (HVAC) systems to absorb low-frequency fluctuations in renewable energy sources, especially in solar photo-voltaic (PV) generation. Given the uncertain and time-varying nature of solar PV generation, its probability distribution is difficult to be estimated perfectly, which poses a challenging problem of how to optimally schedule a fleet of HVAC loads to consume as much as local PV generation. We formulate a distributionally robust chance-constrained (DRCC) model to ensure that PV generation is consumed with a desired probability for a family of probability distributions, termed as an ambiguity set, built upon mean and covariance information. We benchmark the DRCC model with a deterministic optimization model and a stochastic programming model in a one-day simulation. We show that the DRCC model achieves constantly good performance to consume most PV generation even in the case with the presence of probability distribution ambiguity.

Zhang, Yiling↗
DOE OSTI2022

Tri-Sectional Approximation of the Shortest Path to Long-Term Voltage Stability Boundary with Distributed Energy Resources

Ensuring long-term voltage stability is critical for reliable operations of power grids. High share of distributed energy resources (DERs) can create complicated system operation modes that may invalidate the traditional long-term voltage stability analysis based on typical operation modes. To address this challenge, this paper investigates how to compute the shortest path to the voltage stability boundary in the DER aggregated load space with large dispersion. Instead of working in the Euclidean space, we establish the analysis and computations on the algebraic power flow manifold to better capture the curvature change of the shortest path along the direction of losing stability. A modified optimal control framework is presented for obtaining the ground-truth of the smooth shortest path on the manifold. To efficiently and accurately solve for the shortest path, we further leverage the geometric features of the power flow manifold and propose a tri-sectional approximation model that is scalable for large-scale systems. Several numerical examples, up to the 1354-bus system, with different DER penetration levels and high dimensional renewable power injection variations are evaluated. The simulation results demonstrate that the tri-sectional approximation achieves high accuracy and efficiency to approximate the shortest path to the voltage stability boundary.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Tailoring catalyst layer structures for anion exchange membrane fuel cells by controlling the size of ionomer aggreates in dispersion

The development of anion exchange membranes and ionomers plays a critical role in performance of anion exchange membrane fuel cells. However, the optimal design of catalyst layer structures still remains unexplored despite its significance. Herein, the rational design guide is presented for cathode and anode catalyst layers using m-TPN1 ionomer by controlling the size of ionomer aggregates in dispersion and comparing various catalyst layer structures. The size of m-TPN1 aggregates decreases, creating a more uniform ionomer distribution and a larger triple-phase-boundary. As ionomer distribution of the cathode catalyst layer becomes more uniform, the power density of membrane-electrode-assembly is enhanced. Also, the power density at fully humidified condition strongly correlates with the porosity of anode catalyst layer due to the limitation of hydrogen transport at anode by water flooding.

42 ENGINEERING↗
DOE OSTI2019

Identification and Validation of Virtual Battery Model for Heterogeneous Devices

The potential of distributed energy resources in providing grid services can be maximized with the recent advancements in demand side control. Effective utilization of this control strategy requires the knowledge of aggregate flexibility of the distributed energy resources (DERs). Recent works have shown that the aggregate flexibility of DERs can be modeled as a virtual battery (VB) whose state evolution is governed by a first order system including self dissipation. The VB parameters (self dissipation rate, energy capacity) are obtained by solving an optimization problem which minimizes the tracking performance of the ensemble and the proposed first order model. For the identified first order model, time varying power limits are calculated using binary search algorithms. Finally, this proposed framework is demonstrated for different homogeneous and heterogeneous ensembles consisting of air conditioners (ACs) and electric water heaters (EWHs).

Nandanoori, Sai Pushpak↗
DOE OSTI2018

Stochastic Dual Algorithm for Voltage Regulation in Distribution Networks with Discrete Loads

This paper considers power distribution networks with distributed energy resources and designs an incentive-based algorithm that allows the network operator and customers to pursue given operational and economic objectives while concurrently ensuring that voltages are within prescribed limits. Heterogeneous DERs with continuous and discrete control commands are considered. We address four major challenges: discrete decision variables, non-convexity due to a Stackelberg game structure, unavailability of private information from customers, and asynchronous operation. Starting from a non-convex setting, we develop a distributed stochastic dual algorithm that solves a relaxed problem, and prove that the proposed algorithm achieves the global optimal solution of the original problem on average. Feasible values for discrete decision variables are also recovered. Stability of the algorithm is analytically established and numerically corroborated.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Leveraging Existing Assets for Long Duration Energy Storage

Increased renewables penetration to electrical grid is necessary to reduce overall emissions from the electrical power generation sector. Nonetheless, its integration creates challenges to grid operators who must match the power being generated by intermittent renewables and other traditional energy sources with the demand from consumers, while ensuring the reliability and power quality for the entire system. Energy storage has been proposed as an alternative to natural gas peaking plants and a form to deliver excess renewable energy generation at times of peak demand. For energy storage to provide benefits to end customers (energy consumers), it must be reliable, efficient, and cost effective. The Illinois Sustainable Technology Center (ISTC), one of the surveys that integrate the Prairie Research Institute (PRI), aims to develop a Center for Energy Storage at Existing Assets (CESEA) at UIUC with the participation of Waste Pressure Corp and Ecotek Engineering USA LLC. CESEA will focus on LDES systems that can integrate to existing infrastructure in a manner that reduces the initial capital expenditure and demonstrates the ability to repurpose fossil assets that would otherwise become stranded, to serve the energy transition. CESEA aims to leverage UIUC’s unique facilities to validate LDES systems performance at a relevant operating environment. UIUC’s facilities include a 85-MW combined heat and power (CHP) power plant, two (2) solar PV plants totaling over 18 MWdc of installed capacity, an electrical grid along with a substation at transmission and distribution voltages, a 22-mile gas pipeline network operating at two pressure levels, along with steam and chilled water distribution networks. The new LDES systems will connect to the existing UIUC grid through a new test electrical station, which will have the capacity to accommodate additional connections to test new devices and technologies as part of future CESEA R&D activities. The test electrical station will contain meters, instrumentation, and controls to accurately capture data and allow optimization of control algorithms. CESEA will initially focus on technologies that: i) utilize existing equipment or facilities to perform at least one of the process steps in LDES (charging, storage, or discharging), ii) leverage mature or commercially available components or controls, iii) show potential for cost-leadership in 10+ hour storage at a commercial scale. Initial technologies that were identified to meet these criteria include Compressed Gas Energy Storage (CGES), and TES. CGES stores electricity by raising the pressure of a compressible gas inside a control volume and converting the stored energy to electricity via expansion-generation. CGES is a generalization of CAES that covers any working gas (not just air). A successful CGES demo will help to circumvent many challenges faced by CAES (long development times due to site prospecting, high cost of compression and storage, heat recovery management, etc.) by: 1) utilizing existing infrastructure (compressors, pipelines, underground storage or pressure vessels) used in the transportation and storage of industrial gases for LDES charging and storage; 2) deploying over sites already-developed for industrial applications with minor additional work; 3) leveraging the price structure of commercial industrial gas to cover the costs of electricity used during charging. A previous DOE-sponsored conceptual study (DE-FE-0032018) estimated the levelized cost of energy of a 1.1 MW / 17 MWh CGES system at $0.08/kWh, with a commercial 10x scale system cost estimated at <$0.04/kWh (Giardinella, 2022). The pilot-sized system was estimated to avoid up to 2693 tons of CO2/year.

25 ENERGY STORAGE↗
DOE OSTI2018

Resilient Cyber Infrastructure for the Minimum Wind Curtailment Remedial Control Scheme

Integration of Distributed Energy Resources (DER) has been significantly increasing in the electric power grid. One of the predominant renewable energy sources, wind energy, possess a significant challenge for grid operations, due to its intermittency, uncertainty and variability. In changing operating conditions with wind uncertainty, part of wind may need to be curtailed for the safe utilization of the transmission system. In order to minimize the wind power curtailment and to keep the power flow on a transmission line under limits, a cyber resilient Distributed Remedial Action Scheme (DRAS) is developed in this paper. The RAS is formulated as an optimization problem, considering the real power flow constraints of the transmission lines and given wind variability. In this paper, DRAS is developed to operate even with failures in the computing nodes to make it cyber-resilient. Distributed simplex algorithm is used for optimization in the distributed RAS. The DRAS is implemented in a decentralized platform called Resilient Information Architecture Platform for Smart Grid (RIAPS) to enable cyber resiliency of DRAS. The effectiveness of the proposed approach is validated through offline simulations of the New England 39 bus system and online simulations of IEEE 14 bus test system. In conclusion, a cyber physical test bed utilizing Real Time Digital Simulator (RTDS), Phasor Measurement Units (PMUs), and BeagleBones has been used for the online simulation and validation of the developed cyber-resilient RAS.

17 WIND ENERGY↗
DOE OSTI2012

EV Charging Through Wireless Power Transfer: Analysis of Efficiency Optimization and Technology Trends

This paper is aimed at reviewing the technology trends for wireless power transfer (WPT) for electric vehicles (EV). It also analyzes the factors affecting its efficiency and describes the techniques currently used for its optimization. The review of the technology trends encompasses both stationary and moving vehicle charging systems. The study of the stationary vehicle charging technology is based on current implementations and on-going developments at WiTricity and Oak Ridge National Lab (ORNL). The moving vehicle charging technology is primarily described through the results achieved by the Korean Advanced Institute of Technology (KAIST) along with on-going efforts at Stanford University. The factors affecting the efficiency are determined through the analysis of the equivalent circuit of magnetic resonant coupling. The air gap between both transmitting and receiving coils along with the magnetic field distribution and the relative impedance mismatch between the related circuits are the primary factors affecting the WPT efficiency. Currently the industry is looking at an air gap of 25 cm or below. To control the magnetic field distribution, Kaist has recently developed the Shaped Magnetic Field In Resonance (SMFIR) technology that uses conveniently shaped ferrite material to provide low reluctance path. The efficiency can be further increased by means of impedance matching. As a result, Delphi's implementation of the WiTricity's technology exhibits a WPT efficiency above 90% for stationary charging while KAIST has demonstrated a maximum efficiency of 83% for moving vehicle with its On Line Vehicle (OLEV) project. This study is restricted to near-field applications (short and mid-range) and does not address long-range technology such as microwave power transfer that has low efficiency as it is based on radiating electromagnetic waves. This paper exemplifies Delphi's work in powertrain electrification as part of its innovation for the real world program geared toward a safer, greener and more connected driving. Moreover, it draws from and adds to Dr. Andrew Brown Jr.'s SAE books 'Active Safety and the Mobility Industry', 'Connectivity and Mobility Industry', and 'Green Technologies and the Mobility Industry'. Magnetic resonant coupling is the foundation of modern wireless power transfer. Its efficiency can be controlled through impedance matching and magnetic field shaping. Current implementations use one or both of these control methods and enable both stationary and mobile charging with typical efficiency within the 80% and 90% range for an air gap up to 25 cm.

25 ENERGY STORAGE↗
DOE OSTI2026

Co-Simulation Meets AI: MCP-Driven Power System Analysis

GridGPT, a fine-tuned Generative AI model is designed for on-premise use in grid control rooms. This presentation will demonstrate how eGridGPT can seamlessly integrate with control room solutions to offer operators, engineers, and corporate users enhanced guidance and decision support. It is to show how this innovative AI solution can improve state estimation, boost variable energy forecasting, and optimize grid operations. By leveraging eGridGPT's unique features, audience will learn to unlock new levels of automation, predictive analytics, and reliability within their power systems, ultimately leading to reduced downtime and improved operational efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

In‐mold rheology and automated process control for injection molding of recycled polypropylene

Abstract Manufacturing plastic parts with secondary feedstocks has risen to the forefront of importance in recent years. However, the variation in molecular weight and rheology of secondary feedstock can lead to inconsistent part quality. This work evaluates the effectiveness of a novel closed‐loop adaptive process control system that adjusts nozzle pressure in response to in‐mold pressure data. Five different recycled polypropylene blends, with a broad distribution of flow properties, were evaluated to determine the effectiveness of the control system at reducing processing variation. The experimental results show that the process control strategy reduced the variation within the mold, as seen by in‐mold pressure curves and calculated in‐mold viscosity values. Additionally, the parameters that control the automated process adjustments were investigated, showing the importance of optimization. The analysis of the correlation between in‐mold rheology and mechanical properties showed a slight variation in the mechanical properties and parts weight with a coefficient of variation of under 5%. Overall, the results demonstrate the ability of pressure‐controlled molding and automated viscosity adjustment to reduce the variability when molding a secondary feedstock. Highlights Pressure‐controlled injection molding of recycled polypropylene. Automated closed‐loop adaptive process control methodology. Methodology resulted in a reduction in pressure variation during molding. Changes in mechanical properties and in‐mold viscosity were investigated. Results show the potential of pressure‐controlled molding at reducing variation.

Krantz, Joshua↗
DOE OSTI2013

Nanocluster production for solar cell applications

This research focuses on the fabrication and characterization of silver (Ag) and silicon (Si) nanoclusters that might be used for solar cell applications. Silver and silicon nanoclusters have been synthesized by means of dc magnetron sputtering and inert gas condensation inside an ultra-high vacuum compatible system. We have found that nanocluster size distributions can be tuned by various source parameters, such as the sputtering discharge power, flow rate of argon inert gas, and aggregation length. Quadrupole mass filter and transmission electron microscopy were used to evaluate the size distribution of Ag and Si nanoclusters. Ag nanoclusters with average size in the range of 3.6–8.3 nm were synthesized (herein size refers to the nanocluster diameter), whereas Si nanoclusters' average size was controlled to range between 2.9 and 7.4 nm by controlling the source parameters. This work illustrates the ability of controlling the Si and Ag nanoclusters' sizes by proper optimization of the operation conditions. By controlling nanoclusters' sizes, one can alter their surface properties to suit the need to enhance solar cell efficiency. Herein, Ag nanoclusters were deposited on commercial polycrystalline solar cells. Short circuit current (I{sub SC}), open circuit voltage (V{sub OC}), fill factor, and efficiency (η) were obtained under light source with an intensity of 30 mW/cm{sup 2}. A 22.7% enhancement in solar cell efficiency could be measured after deposition of Ag nanoclusters, which demonstrates that Ag nanoclusters generated in this work are useful to enhance solar cell efficiency.

36 MATERIALS SCIENCE↗