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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
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Multiscale Electricity Modeling for Evaluating Carbon Capture and Sequestration Technologies (MEME-CCS)

This effort employs and adapts a rigorous multiscale electricity modeling platform at the National Renewable Energy Laboratory (NREL) to evaluate carbon capture and sequestration (CCS) and negative emissions technologies (NET) from the ARPA-E FLECCS program. NREL's modeling platform includes the Regional Energy Deployment System (ReEDS) electric sector capacity expansion model, which projects future electricity generation mixes at sub-state-level resolution that are downscaled to the unit-level to enable hourly, zonal or nodal electricity production cost modeling in the PLEXOS model. The ReEDS-PLEXOS modeling suite is well-established for examining electric sector futures with high renewable energy penetrations, energy storage, electrification, and distributed generation. The key advancement proposed herein utilizes collaboration with CCS experts at the University of Wyoming (U.WY) and the FLECCS technology development teams to create innovative methods for representing CCS and NET in the ReEDS and PLEXOS models. Expanded technology options and new operational parameterizations are integrated into these models to allow an unprecedented combination of scope and resolution for exploring the future of CCS and NET. The resulting capabilities permit wide-ranging scenario analysis to assess CCS and NET deployment under alternative scenarios of CO2 prices and competitiveness of flexible CCS and NET technologies. We demonstrate sample deployment and operational outcomes to show how these models are being used to assess the future potential for FLECCS technologies and their impacts on the U.S. electricity system. These products will help an emerging CCS/NET industry in the United States by providing economics-driven guidance to technology developers while informing policy and investment decisions in the public and private sectors.

capacity expansion↗
DOE OSTI2023

Power Electronics for Electrolyzer Applications to Enable Grid Services

The Power Electronics for Electrolyzer Applications to Enable Grid Services project aims to develop smart converter for dedicated electrolyzer applications to enable grid services via standardization of control interfaces between hydrogen electrolyzer system low-level controls and power converter controls. The project provides additional revenue source for electrolyzer through participation in grid services and reduces the cost of deployment and controls integration through standardization and risk due to supply-chain issues. It also enables the adoption of green hydrogen via standardizing the integration of energy storage, renewables, and distributed energy resources. Furthermore, the work provides a controlled validation environment to evaluate scalable integration solution for hydrogen production technologies, improves overall reliability and maintainability of the electrolyzer system for grid applications, and directly contributes to DOE HFTO's "Hydrogen Shot" goal.

energy transitions↗
DOE OSTI2022

Deep learning-accelerated 3D carbon storage reservoir pressure forecasting based on data assimilation using surface displacement from InSAR

Fast forecasting of the reservoir pressure distribution during geologic carbon storage (GCS) by assimilating monitoring data is a challenging problem. Due to high drilling cost, GCS projects usually have spatially sparse measurements from few wells, leading to high uncertainties in reservoir pressure prediction. To address this challenge, we use low-cost Interferometric Synthetic-Aperture Radar (InSAR) data as monitoring data to infer reservoir pressure build up. We develop a deep learning-accelerated workflow to assimilate surface displacement maps interpreted from InSAR and to forecast dynamic reservoir pressure. Employing an Ensemble Smoother Multiple Data Assimilation (ES-MDA) framework, the workflow updates three-dimensional (3D) geologic properties and predicts reservoir pressure with quantified uncertainties. We use a synthetic commercial-scale GCS model with bimodally distributed permeability and porosity to demonstrate the efficacy of the workflow. A two-step CNN-PCA approach is employed to parameterize the bimodal fields. The computational efficiency of the workflow is boosted by two residual U-Net based surrogate models for surface displacement and reservoir pressure predictions, respectively. The workflow can complete data assimilation and reservoir pressure forecasting in half an hour on a personal computer.

25 ENERGY STORAGE↗
DOE OSTI2025

Deep Reinforcement Learning for Distribution System Operations: A Tutorial and Survey

Here, the rapid evolution of modern electric power distribution systems into complex networks of interconnected active devices, distributed generation (DG), and storage poses increasing difficulties for system operators. The large-scale integration of distributed energy resources (DERs) and the rapid exchange of measurement data via communication networks present major opportunities for advancing grid operations but also introduce greater uncertainty, higher data dimensionality, more complex network and device models, and challenging control and optimization problems. Deep reinforcement learning (DRL) algorithms are promising in addressing these challenges. However, they have not been effectively adapted for power systems applications, requiring extensive customization for implementation and evaluation. This has resulted in reproducibility challenges and a steep learning curve for researchers new to applying DRL algorithms to the power systems domain. To bridge these gaps, this tutorial aims to serve as a valuable resource for researchers interested in exploring learning-based algorithms to operate active power distribution networks. Specifically, this work presents a generalized process for translating sequential decision-making problems in power distribution systems into Markov decision process (MDP) formulations, illustrated through concrete grid service examples. Additionally, we introduce a simple environment design strategy to develop and evaluate example DRL algorithms for distribution system applications, complete with an included code repository to guide users through environment construction.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Challenges of designing, operating, and managing brine extraction at a carbon dioxide storage site

Developing and deploying commercial-scale carbon dioxide (CO 2 ) storage at a site requires operational strategies to manage pressure and CO 2 plume distribution. In the natural gas storage and oil and gas industries, fluid extraction has been successfully implemented as a strategy to manage pressure and control fluid distribution (i.e., hydrocarbon recovery and natural gas storage efficiency, respectively). The technical challenges of integrating brine extraction at CO 2 storage sites are expected to be similar, if not identical, to those in the natural gas storage and petroleum industries. Major technical challenges of using brine extraction to manage storage in a storage unit include (1) designing and completing the brine extraction wells, (2) locating the brine extraction well, (3) assessing the efficacy of brine extraction, and (4) handling the extracted brine. The design and completion of the brine extraction well include the well type (i.e., vertical or horizontal) and perforated interval. The location of the well is influenced by the timing of extraction operations (i.e., prior or after CO 2 injection starts) and project objectives (i.e., desired directional movement of the CO 2 plume and avoid or minimize CO 2 extraction). The efficacy of brine extraction requires a baseline projection of the pressure and CO 2 plume distribution. Options for handling extracted brine include subsurface disposal, surface disposal, and industrial use. Here this paper addresses means of overcoming technical challenges that may likely be encountered before or after CO 2 injection starts.

25 ENERGY STORAGE↗
DOE OSTI2022

Dataset of ultralow temperature refrigeration for COVID 19 vaccine distribution solution

Abstract Most COVID-19 vaccines require temperature control for transportation and storage. Two types of vaccine have been developed by manufacturers (Pfizer and Moderna). Both vaccines are based on mRNA and lipid nanoparticles requiring low temperature storage. The Pfizer vaccine requires ultra-low temperature storage (−80 °C to −60 °C), while the Moderna vaccine requires −30 °C storage. However, the last stage of distribution is quite challenging, especially for rural or suburban areas, where local towns, pharmacy chains and hospitals may not have the infrastructure required to store the vaccine at the required temperature. In addition, there is limited data available to address ancillary challenges of the distribution framework for both transportation and storage stages, including safety concerns due to human exposure to large amounts of CO 2 from dry-ice sublimation, issues due to the pressure increase caused by dry-ice sublimation, and the potential issue caused by non-uniform cryogenic temperatures. As such, there is a need for test dataset to assist the development of a quick, effective, secure, and safe solution to mitigate the challenges faced by vaccine distribution logistics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Hydrogen and Battery Storage Technologies for Low-Cost Energy Decarbonization in Distribution Networks

Deep energy decarbonization cannot be achieved without high penetration of renewables. At higher renewable energy penetrations, the variability and intermittent nature of solar photovoltaic (PV) electricity can cause ramping issues with existing fossil fuel generation, requiring longer term energy storage to increase the reliability of grid operation. Here, a proton exchange membrane electrolyzer can produce H 2 and serves as a utility controllable load. The produced H 2 can then be stored and converted back into electricity, or mixed with natural gas, or used as transportation fuel, or chemical feedstock. This paper considers the perspective of the distribution system operator that operates the distributed energy resources on a standard IEEE 33-node distribution network considering the technical and physical constraints with the goal of minimizing total investment and operation cost. Different case studies, at very high PV penetrations are considered to show the challenges and path to net-zero emission energy production using H 2 energy. Sensitivity of utility PV costs and electrolyzer capital costs on producing H 2 at $\$1$ /kg are presented showing that the distribution network could produce 100% renewable electricity and H 2 could be produced at a cost of $\$1$ /kg by 2050 with conservative cost estimates and by 2030 with accelerated cost declines.

25 ENERGY STORAGE↗
DOE OSTI2023

Optimization of Energy Storage System Economics and Controls by Incorporating Battery Degradation Costs in REopt

The use of stationary electrochemical energy storage systems utilizing lithium-ion batteries has increased rapidly as the production scale and price for lithium-ion batteries has decreased. These energy storage systems are crucial for maintaining grid resiliency, especially for grids operating with high penetration of renewable energy generation assets or for with a variety of distributed energy generation and storage systems. One challenging factor for the development of battery energy storage systems is estimating the proper sizing, in terms of both power and energy, that minimizes total costs over the lifetime of the systems; this calculation is difficult in simple cases, where a battery is costed independently, but is extremely challenging when building loads and electrical generation by photovoltaic resources are also considered. REopt is a techoeconomic optimization tool developed by NREL to address these challenges. Previously, battery degradation has been priced by simply assuming a 10-year replacement schedule for battery systems. However, this does not account for varying degradation trends observed across real-world batteries, or allow for batteries to be operated in a degradation-aware manner that optimizes battery dispatch based on operating costs. This work incorporates a battery life model into REopt. This battery life model is simple, so that it may be solvable within the constrains of a mixed-integer linear optimization problem, but is fit to accelerated aging data recorded in the lab. To achieve the best possible accuracy for lifetime estimates given these constraints, parameters for the battery life model in REopt are estimated by fitting 20-year simulations of battery life after identifying state-space battery degradation model from accelerated aging data. Comparisons of battery life predicted in REopt and from the state-space battery degradation model to ensure validity of lifetime estimates made by REopt. Battery life and cost is optimized by controlling three decision to minimize system life cost: battery sizing, daily state-of-charge, and daily energy-throughput. The cost of battery degradation as a function of these control variables is then estimated assuming two possible maintenance strategies: replacement, where the entire battery system is replaced if cell reach an end-of-life capacity threshold; and augmentation, which establishes a fund to pay for continual purchase of new batteries to maintain the initial energy capacity of the system. These two strategies offer conservative (for replacement) and optimistic (for augmentation) bounds for total system cost. The degradation cost incurred by these strategies is then used to control battery dispatch decisions, operating the battery in a degradation-aware manner that maximizes battery lifetime while also providing energy when favorable. Because the mixed-integer linear program has perfect foresight of future energy needs, batteries with degradation costs are always operated using 'just-in-time' charging, which is unrealistic, as no energy is left in the storage system to perform other energy services or to serve as emergency back-up power. To combat this, an inequality constraint on the average annual state-of-charge is imposed, and the sensitivity of system cost to average stored energy, e.g., the cost of system resiliency, can be quantified. Analysis of results has several conclusions, for instance, oversizing of battery storage systems is not a cost burden when battery storage is an optimal solution, as any additional battery capacity can simply be utilized to avoid costs of purchasing energy from a utility.

battery↗
DOE OSTI2020

A Distributed Control Architecture for Cascaded H-Bridge Converter with Integrated Battery Energy Storage

Cascaded H-bridge topology has been used in grid-tied converters for battery energy storage system due to its modular structure. To fully utilize the converter’s modularity, this paper proposes a hierarchical distributed control architecture that consists of primary control, secondary control and battery state of charge (SOC) balancing control. Primary control ensures accurate current tracking while a distributed secondary control based on consensus algorithm is presented to regulate power sharing among modules and is proved to be stable theoretically. Here, a distributed SOC balancing control is further introduced to improve energy efficiency of battery energy storage system. Finally, the hierarchical distributed control strategy is implemented using hardware controllers and a software platform. Besides, a carrier phase shift control is also implemented to achieve multilevel output voltage and harmonic reduction. The experimental results demonstrate the performance of the proposed control scheme effectively.

25 ENERGY STORAGE↗
DOE OSTI2023

An Introductory Guide to Generating, Using, and Storing Energy: Understanding electricity, energy storage, microgrids, and related technologies

This guide was created to empower Energy Storage for Social Equity (ES4SE) community members to learn more about power systems, energy distribution, renewable energy, and battery storage systems. It was developed in an accessible format for audiences of varied backgrounds and requires no previous experience in energy storage. It is intended to serve readers as both an educational and reference document as they consider, procure, and deploy power systems that include battery energy storage technologies and microgrids in their communities. By providing an introductory overview of the basics of electricity, power generation, and household energy consumption and loads, this guide prepares readers to learn even more about battery energy storage, microgrid systems, and the sizing of solar and battery systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

DERMS Online: A New Voltage Sensitivity-Enabled Feedback Optimization Framework: Preprint

This paper proposes a distributed energy resource management system (DERMS) solution by developing a new voltage sensitivity enabled feedback optimization framework. The key idea is to adopt a measurement feedback scheme to reformulate the original nonlinear optimization into a linear programming (LP) problem via perturb and observe-based voltage sensitivity analysis. The proposed solution eliminates the dependence on load knowledge and can be implemented online thanks to an efficient open-source solver for LP problems. The proposed DERMS online platform is generalizable to deal with various types of distributed energy resources (DERs), including distributed photovoltaics (PVs), energy storage, electric vehicles, demand response, etc. Comparison results with other control methods on a realistic distribution feeder in Southern California highlight the feasibility as well as benefits for the proposed framework.

distributed energy resources management↗
NASA NTRS1986

Independent Orbiter Assessment (IOA): Analysis of the electrical power distribution and control/electrical power generation subsystem

The results of the Independent Orbiter Assessment (IOA) of the Failure Modes and Effects Analysis (FMEA) and Critical Items List (CIL) are presented. The IOA approach features a top-down analysis of the hardware to determine failure modes, criticality, and potential critical items. To preserve independence, this analysis was accomplished without reliance upon the results contained within the NASA FMEA/CIL documentation. This report documents the independent analysis results corresponding to the Orbiter Electrical Power Distribution and Control (EPD and C)/Electrical Power Generation (EPG) hardware. The EPD and C/EPG hardware is required for performing critical functions of cryogenic reactant storage, electrical power generation and product water distribution in the Orbiter. Specifically, the EPD and C/EPG hardware consists of the following components: Power Section Assembly (PSA); Reactant Control Subsystem (RCS); Thermal Control Subsystem (TCS); Water Removal Subsystem (WRS); and Power Reactant Storage and Distribution System (PRSDS). The IOA analysis process utilized available EPD and C/EPG hardware drawings and schematics for defining hardware assemblies, components, and hardware items. Each level of hardware was evaluated and analyzed for possible failure modes and effects. Criticality was assigned based upon the severity of the effect for each failure mode.

Patton, Jeff A.↗
DOE OSTI2023

SolarAPP+ Performance Review (2022 Data)

The Solar Automated Permit Processing Plus (SolarAPP+) platform is an online portal to facilitate and expedite rooftop solar photovoltaic (PV) permitting processes. SolarAPP+ allows PV contractors to upload system specifications, have those specifications automatically reviewed for code compliance, and receive instant approval for code-compliant systems. SolarAPP+ also provides inspection checklists to verify installation practices and adherence to approved designs. SolarAPP+ is available to authorities having jurisdiction (AHJs) at no cost. This report is part of an ongoing series of reviews of SolarAPP+ performance. Consistent with previous performance reviews, we summarize SolarAPP+ adoption trends to date and compare various metrics for PV systems permitted through SolarAPP+ versus systems permitted through conventional AHJ permitting processes. As of the end of 2022, the National Renewable Energy Laboratory (NREL) had contacted over 1,500 AHJs with significant solar permitting volume regarding SolarAPP+. Of those, 607 AHJs had at least expressed interest in the platform. 16 AHJs had begun piloting the platform and 15 of these had publicly launched the platform by the end of 2022. In 2022, 206 installers submitted 11,092 permits through the SolarAPP+ platform, including 708 permits for solar+storage systems. SolarAPP+ permits accounted for around 37% of all permits issued in participating AHJs. We compare permitting timelines through SolarAPP+ to traditional AHJ permitting processes to assess the platform's performance. Consistent with previous SolarAPP+ performance reviews, we find that permitting timelines are significantly shorter for SolarAPP+ projects. Based on median timelines, a typical SolarAPP+ project is permitted and inspected 8 business days sooner than traditional projects. We estimate that automatic SolarAPP+ permitting saved between 3,500 and 13,900 hours of AHJ staff time in 2022. Finally, we find evidence that SolarAPP+ may improve inspection outcomes, with SolarAPP+ projects failing inspections about 28% less frequently than traditional projects.

14 SOLAR ENERGY↗
DOE OSTI2022

Volt-VAR Optimization in Distribution Networks Using Twin Delayed Deep Reinforcement Learning

Modern distribution grids are undergoing new challenges due to the stochastic nature of distributed energy resources (DERs). High penetration of DERs has a significant impact on Volt-VAR profile and system power losses. This work proposes a deep reinforcement learning (DRL)-based Volt-VAR optimization approach for improving voltage profile and reducing system power loss under high penetration of distributed energy resources, such as battery energy storage and solar photovoltaic units in distribution grids. The twin delayed deep deterministic policy gradient (TD3) method-based DRL agent is proposed to configure optimal set-points of reactive power outputs of fast responding smart inverters. The agent schedules the reactive power of inverters according to their physical capabilities, such as minimum allowed power factor, e.g., 0.9 leading/lagging. The reward function of the proposed DRL scheme is designed carefully to ensure a proper voltage profile of the grids with effective scheduling of reactive power outputs from inverters. The performance of the proposed model is verified on modified IEEE 34- and 123-bus systems and compared with base case with no reactive supply by inverters, and local droop Volt-VAR control approach. The results show that the proposed method performs better than the local droop control and deep deterministic policy gradient (DDPG)-based DRL method for reducing voltage fluctuation and minimizing power loss.

Hossain, Rakib↗
DOE OSTI2021

The Role of Surface Hydrophobicity on the Structure and Dynamics of CO2 and CH4 Confined in Silica Nanopores

Advancing a portfolio of technologies that range from the storage of excess renewable natural gas for distributed use to the capture and storage of CO 2 in geological formation are essential for meeting our energy needs while responding to challenges associated with climate change. Delineating the surface interactions and the organization of these gases in nanoporous environments is one of the less explored approaches to ground advances in novel materials for gas storage or predict the fate of stored gases in subsurface environments. To this end, the molecular scale interactions underlying the organization and transport behavior of CO 2 and CH 4 molecules in silica nanopores need to be investigated. To probe the influence of hydrophobic surfaces, a series of classical molecular dynamics (MD) simulations are performed to investigate the structure and dynamics of CO 2 and CH 4 confined in OH-terminated and CH 3 -terminated silica pores with diameters of 2, 4, 6, 8, and 10 nm at 298 K and 10 MPa. Higher adsorption extents of CO 2 compared to CH 4 are noted on OH-terminated and CH 3 -terminated pores. The adsorbed extents increase with the pore diameter. Further, the interfacial CO 2 and CH 4 molecules reside closer to the surface of OH-terminated pores compared to CH 3 -terminated pores. The lower adsorption extents of CH 4 on OH-terminated and CH 3 -terminated pores result in higher diffusion coefficients compared to CO 2 molecules. The diffusivities of both gases in OH-terminated and CH 3 -terminated pores increase systematically with the pore diameter. The higher adsorption extents of CO 2 on OH-terminated and CH 3 -terminated pores are driven by higher van der Waals and electrostatic interactions with the pore surfaces, while CH 4 adsorption is mainly due to van der Waals interactions with the pore walls. These findings provide the interfacial chemical basis underlying the organization and transport behavior of pressurized CO 2 and CH 4 gases in confinement.

Mohammed, Sohaib↗
DOE OSTI2022

Optimal Portfolio Design of Distributed Energy Resources on Puerto Rico Distribution Feeders with Long Outages after Hurricane Maria

This work details a project to design reliable, resilient, and cost-effective networked microgrids considering grid constraints and resilience metrics focused on Puerto Rico distribution feeder locations with long outages after Hurricane Maria. The project consisted primarily of modeling and simulation tasks that accomplished the following objectives: 1. Selected 10 distribution feeder models in vulnerable areas. The sample feeders are geographically distributed across Puerto Rico and vary in length to capture the wide variety of feeders on the island. 2. Determined the optimal location and sizing of distributed energy resources (DERs) on the identified distribution feeders. The systems considered as part of the microgrid solutions were solar photovoltaic (PV), battery energy storage systems (BESS) and distributed fossil fuel generation (DFFG). 3. Estimated the cost-benefit of the proposed DER portfolios. 4. Provided a set of final recommendations that inform decision making on how to do targeted planning analysis for microgrids that can supply energy to critical infrastructures.

24 POWER TRANSMISSION AND DISTRIBUTION↗