Search NASA⌕ Search

SEARCH · Search NASA

Results for “Energy sources”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Energy Master Planning for Resilient Public Communities—Best Practices from U.S. Military Installations

Until recently, most planners at military installations addressed energy systems for new facilities on an individual facility basis without consideration of community-wide goals relevant to energy sources, renewables, storage, or future energy generation needs. Building retrofits of public buildings typically do not address energy needs beyond the minimum code requirements making it difficult, if not impossible, to achieve community-level targets on a building-by-building basis. Planning on the basis of cost and general reliability may also fail to deliver community-level resilience. For example, many building code requirements focus on hardening to specific threats, but in a multi-building community, only a few of these buildings may be mission-critical. Over the past two decades, the frequency and duration of regional power outages and water utility disruptions from weather, man-made events, and aging infrastructure have increased. Major disruptions of electric and thermal energy have degraded critical mission capabilities and caused significant economic impacts. In 2016, the U.S. Department of Defense issued guidance that each Service (Army, Navy, Air Force, Marines) complete comprehensive energy plans for the installations that consumed 75% of total building energy. Guidance was updated in 2017 to include metrics for energy resilience, and in some cases, water. This paper describes how community level quantitative and qualitative resilience analysis and metrics have been incorporated into community energy and water planning best practices for military installations in three geographically diverse locations. It is based on research performed under the International Energy Agency’s “Energy in Buildings and Communities Program Annex 73,” focusing on development of guidelines and tools that support the planning of Net Zero Energy Resilient Public Communities as well as research performed under the Department of Defense Environmental Security Technology Certification Program project EW18-D1- 5281, “Technologies Integration to Achieve Resilient, Low- Energy Military Installations.” The first case study reviews progress made on an energy and water planning study conducted at Fort Bliss, Texas. The second and third describes planning conducted at Fort Bragg, North Carolina and the Joint Region Marianas, Guam, respectively, under the updated guidance from 2017 regarding energy and water resilience. Analysis methods, key metrics, and key infrastructure and operational constraints are described, as well as technical, economic and business concepts used during the planning process.

Urban, Angela B.↗
DOE OSTI2022

Physics-Informed Neural Network Modeling of Li-Ion Batteries: Preprint

Li-ion batteries (LIB) are a promising solution to enable storage of intermittent energy sources due to their high energy density. However, LIBs are known to significantly degrade after about 1000 charge-discharge cycles. LIBs degrade following different degradation modes and at a rate that depends on the operating conditions (external temperature, load). To plan the installation of batteries, appropriate understanding and prediction capabilities of their lifecycle is needed. High-fidelity numerical models of LIBs such as the pseudo-two-dimensional (P2D) model have been shown to accurately represent the charge-discharge-cycle of an LIB given ac- curate choice of the physical parameters. Given the large number of P2D parameters, adjusting them using forward runs is intractable. This work describes the development of a physics-informed neural network (PINN) as a surrogate substitute of the P2D model that captures parameter dependence. The PINN is advantageous as it needs little to no data, and can naturally encode the dependence of every model parameter. Here, a specific training procedure is adopted to efficiently cover parameter space, handle model stiffness and enforce boundary conditions. The trained PINN is validated against numerical solutions of the P2D model, and its applicability to battery degradation modeling is discussed.

battery degradation↗
DOE OSTI2023

Techno-Economic Analysis of a Nuclear Reactor System coupled with a Liquid Metal Battery

There is a need for nuclear reactor systems to become more dynamic as defossilization and renewable energy sources continue to reshape the energy grid. One option to meet these changing demands is to pair the reactor with an energy storage system. This work details a technoeconomic analysis and sensitivity analysis of a high temperature gas cooled microreactor, supercritical carbon dioxide power cycle, and liquid metal battery. Dynamic modeling has shown that these three systems compliment each other to provide load following and black-start capabilities to the grid. Current cost estimates for installing the systems yield a competitive levelized cost of electricity and levelized cost of storage, indicating that such a combined system may be economically viable as technological advances lead to them being technically feasible.

25 ENERGY STORAGE↗
DOE OSTI2022

HAWC Study of the Ultra-high-energy Spectrum of MGRO J1908+06

We report TeV gamma-ray observations of the ultra-high-energy source MGRO J1908+06 using data from the High Altitude Water Cherenkov Observatory. This source is one of the highest-energy known gamma-ray sources, with emission extending past 200 TeV. Modeling suggests that the bulk of the TeV gamma-ray emission is leptonic in nature, driven by the energetic radio-faint pulsar PSR J1907+0602. Depending on what assumptions are included in the model, a hadronic component may also be allowed. Using the results of the modeling, we discuss implications for detection prospects by multi-messenger campaigns.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Illinois Compressed Air Energy Storage

Compressed Air Storage Energy (CAES) is one of the few mid- technology readiness level (TRL) energy storage technologies that can address the long-duration infrastructure needed for dealing with variable electric output from renewable energy sources and be reliable backup source for replacing natural gas during supply interruptions. In CAES the goal is to capture and store compressed air in subsurface sedimentary strata when off-peak power is available, or there is a need for grid balancing. The stored high-pressure air is returned to the surface and used to power turbines during reductions in either renewable energy or supply issues with fossil fuels. The Illinois CAES project evaluates the feasibility of capturing surplus electrical energy from renewable sources and off-peak energy at a fossil fuel power plant at the University of Illinois Urbana - Champaign (UIUC) campus. The UIUC Abbott Power Plant uses natural gas and coal to generate electricity (capacity: 35 MWe by coal and 49 MWe by NG). UIUC receives additional electricity from on campus solar farm, and off-campus wind farm. Also, UIUC offsets electricity usage by integrating geothermal energy systems into building heating Also, UIUC offsets steam, hot and chilled water usage by integrating geothermal energy systems into building heating and cooling systems. Furthermore, the two UIUC solar farms (Solar Farm 1 is 21 acres and Solar Farm 2 is 54 acres) to generate 4.68 megawatts (MW) and 12.1 MW, respectively. Campus receives 8.6% of the wind-generated electricity from the Rail Splitter Wind Farm. The project objectives were to design an integrated system to 1) capture surplus electrical energy from renewable sources and the Abbott Power Plant using a CAES system, 2) store both the compressed air and the thermal heat generated by compression in the subsurface as part of an adiabatic system, 3) simulate the movement of the air and heat in the subsurface, 4) recover the compressed air and stored thermal heat to rotate turbine generators during sustained interruption due to weather events or fossil fuel disruptions.

03 NATURAL GAS↗
DOE OSTI2023

CHARACTERIZATION AND DEVELOPMENT OF THE ALL–AQUEOUS COPPER THERMALLY REGENERATIVE AMMONIA BATTERY

Low temperature thermal energy is an abundant and untapped energy source for large scale electrical power and energy production around the world. Many different stationary sources such as industrial and thermal power plants produce significant amounts of low temperature heat that goes unused. The primary reason for the energy loss is because the temperature of the heat is often too low for traditional systems to economically generate power from this waste heat. Many different technologies are being researched to produce grid–scale power from low temperature heat to increase grid efficiency, advance decarbonization efforts, and progress electrical power production to a more distributed system. A thermally regenerative battery (TRB) is one promising technology in this space, as it has large power and energy densities relative to competitive technologies and therefore has the potential to disrupt the current power generation mix. TRBs combine two unit operations (a redox flow battery and a distillation column) which are easily scalable and mature, meaning that if a viable TRB chemistry can be identified, its development to pilot and commercial scale can be streamlined. The all–aqueous, copper thermally regenerative ammonia battery (Cuaq–TRAB) was the first TRB that did not use an electrodeposition/dissolution reaction by stabilizing Cu(I) and Cu(II) in the electrolyte, allowing all electroactive species to be stable in an aqueous phase. Through these electrolyte modifications, coulombic efficiency, open circuit potential, and copper solubility all increased, resulting in large gains to power density, energy density, and energy efficiency.

Cross, Nicholas↗
DOE OSTI2022

Trends in Formic Acid Electro-Oxidation on Transition Metals Alloyed with Platinum and Palladium

Direct formic acid fuel cells (DFAFCs) have emerged as a promising power source to meet increased demands for alternative energy sources in the transportation and portable energy storage sectors. Furthermore, these fuel cells utilize formic acid (FA), a nontoxic and carbon-neutral fuel when produced from biomass or via CO 2 reduction. Despite the promise of DFAFCs, the best monometallic catalysts, platinum and palladium, are poisoned by CO through the indirect oxidation pathway and require large overpotentials. By alloying Pt and Pd with other metals, we aim to improve both the activity and selectivity of these catalysts. Here, we present a systematic density functional theory (GGA-PW91) study of FA electro-oxidation (FAO) on the (111) facet of bimetallic Pt (Pt 3 M) or Pd (Pd 3 M) catalysts (M = Au, Ag, Cu, Pt, Pd, Ir, Rh, Ru, or Re) to evaluate the catalytic performance of these surfaces. For each surface, we calculate free energy diagrams and onset potentials of three key reaction mechanisms: direct oxidation of FA via carboxyl (COOH), direct oxidation of FA via formate (HCOO), and the indirect oxidation of FA that first forms CO en route to full oxidation to CO 2 . We then display the trends in the form of phase diagrams that compare the activity of the calculated surfaces against regions of high activity using the free energies of adsorbed CO and OH as descriptors, enabling high-throughput screening and design of improved catalysts, particularly those alloying Pt or Pd with Ir, Ru, or Re.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Conceptual Design Study of a Thermal Energy Storage System Using Granular Solids for Coal-fired Boilers

<span style="font-size: 12pt;">Presentation for the 2020 International Pittsburgh Coal Conference held virtually from September 8-11, 2020.</span><span style="font-size: 12pt;">Abstract: As electricity from various renewable energy sources is increasingly supplied to the power grid, the grid’s stability will be challenged. Proposed grid-stability solutions will need to address variability of sources, such as solar and wind energy, in order to alleviate problems such as overgeneration and ramping. In regions where increases in solar energy are prominent, such as California, the net power demand curve often referred to as “the Duck Curve,” has caused problems for energy dispatchers, due to over-generation and fast ramping demands. In this project, we investigated a conceptual design for a Thermal Energy Storage System (TESS) integrated into a coal-fired power plant to support grid-scale storage. Implementing a TESS into power plants can allow for an increase in renewable sources, increase grid resilience, and allow conventional coal-fired plants to operate more efficiently. For this project, a TESS using granular solids as the energy-storage media is integrated into a conventional coal-fired power plant. The purpose of the TESS is to store energy from the flue gas during low demand periods and transfer the energy to steam during peak demand periods. The storage allows the stabilization of combustion and related boiler components at low loads and provides the opportunity to avoid some shutdowns. This study uses thermodynamic properties of several candidate thermal storage materials to estimate the size of storage system required for several operating scenarios. </span>

Huckaby, E. David↗
DOE OSTI2021

NRIC Integrated Energy Systems Demonstration Pre-Conceptual Designs

This report presents pre-conceptual design scenarios for a potential multiphase demonstration program for innovative uses of nuclear energy with the National Reactor Innovation Center (NRIC) and the Crosscutting Technology Development Integrated Energy Systems (CTD IES) program in the U.S. Department of Energy’s Office of Nuclear Energy. The demonstration program would address the need for low-carbon energy sources among industry stakeholders by identifying and implementing high-impact advanced nuclear projects within a holistic systems perspective. Battelle Energy Alliance, LLC, the managing and operating contractor for the U.S. Department of Energy’s Idaho National Laboratory (INL) in Idaho Falls, Idaho, is seeking Expressions of Interest (EOI) for industry stakeholder participation in the potential demonstration program. Funding sources have not yet been identified for the demonstration program. Responses to the EOI will shape the development and funding requirements for the potential program and inform the down-selection of project designs for further planning and analysis from the wide set of pre-conceptual design scenarios shown in this report. This introductory section summarizes the need for low-carbon energy sources, describes the phases envisioned for the demonstration program, and outlines the organization of this report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

The Modeling of Synfuel Production Process: ASPEN Model of FT production with electricity demand provided at LWR scale

Synfuels, or electro-fuels (e-fuels) have the unique potential to significantly reduce greenhouse gas (GHG) emissions across the transportation sector. This is especially true for applications with substantial payloads and daily miles traveled, such as long-haul heavy-duty vehicles, rail locomotives, marine vessels and aviation aircrafts that are challenging to directly electrify via battery or fuel cell powertrain technologies. Synfuels, or electro-diesel/electro-jet fuels, have similar properties with the incumbent petroleum fuels, compatible with current infrastructure but have much lower GHG emissions relative to the petroleum counterpart, because they utilize waste carbon dioxide (CO2) streams and green hydrogen (H2) sourced from electrolysis. To achieve substantial reductions in GHG emissions, electricity sources must be zero carbon or near-zero carbon, which is the case with solar, wind, hydro and nuclear power. Compared to the intermittency of solar, wind and hydro, nuclear energy provides a steady energy source. In addition, it’s advantageous for nuclear power to produce synfuels because it provides not only near-zero carbon electricity to displace grid electricity, but also near-zero carbon steam to displace carbon-intensive natural gas combustion for steam generation. The availability of electricity and steam also enables more efficient green hydrogen production by using high-temperature electrolysis. In this work, Argonne National Laboratory (ANL) models a synfuel production process via the Fischer- Tropsch (FT) reaction by using nuclear power to provide electricity and steam. In 2021, using ASPEN Plus software, ANL established a detailed process model of a stand-alone FT production facility, assuming feedstocks of pure CO2 and H2. This stand-alone model can be expanded to integrate H2 production from nuclear power via low-temperature and high-temperature electrolysis at light-water reactor (LWR) scale. This report summarizes the stand-alone ASPEN Plus model results with a detailed mass and energy analysis. Our modeled facility produces 351 MT/day (130,000 gal/day) of FT fuel (a mixture of naphtha, jet fuel, and diesel) by converting 223 MT/day of H2 and 2,387 MT/day of CO2. The FT fuel production energy efficiency is 58% and the carbon conversion efficiency (from CO2 to FT fuel) is 46%. The production of green hydrogen requires 390–470 MWe of electricity, which is compared with the capacity of an LWR plant. For the stand-alone FT process, the detailed energy demand (electricity and heat) is summarized in the table below. Based on the energy supply source and the required temperature, potential insertion points of nuclear energy are identified. Based on the potential nuclear energy utilization, this report discusses potential modification options for expanding the system boundary to integrate nuclear power use, for example on-site hydrogen production via water electrolysis. Modeling of the integrated system is conducted by closely working with ANL and Idaho National Laboratory (INL) collaborators to harmonize design parameters of nuclear plants and the FT production process.

Zang, Guiyan↗
DOE OSTI2023

Harness the power of atomistic modeling and deep learning in biofuel separation

Biofuels offer a remarkable, sustainable energy source for a future of clean energy. The development of efficient biofuel separation plays a crucial role in achieving cost-effective utilization of biofuel. In this chapter, we provide an overview of the recent advancements in atomistic-level modeling and deep learning in the rational design of novel, efficient biofuel separation. The fundamental principles of quantum and statistical mechanics are covered in appropriate detail to highlight their underlying differences in theory. The methodologies of several molecular representations and deep learning algorithms applicable to biofuel separation are briefly demonstrated as well. The applications, successes, and risks of employing density functional theory, ab initio molecular dynamics, classical molecular dynamics, and deep learning are provided to showcase their recent accomplishments in biofuel separation as well as potential improvements in both methodology and application. Lastly, a vision for the future growth of these methods is illustrated.

deep learning, artificial intelligence, biofuels, ↗
DOE OSTI2022

The value of in-reservoir energy storage for flexible dispatch of geothermal power

Geothermal systems making use of advanced drilling and well stimulation techniques have the potential to provide tens to hundreds of gigawatts of clean electricity generation in the United States by 2050. With near-zero variable costs, geothermal plants have traditionally been envisioned as providing “baseload” power, generating at their maximum rated output at all times. However, as variable renewable energy sources (VREs) see greater deployment in energy markets, baseload power is becoming increasingly less competitive relative to flexible, dispatchable generation and energy storage. Herein we conduct an analysis of the potential for future geothermal plants to provide both of these services, taking advantage of the natural properties of confined, engineered geothermal reservoirs to store energy in the form of accumulated, pressurized geofluid and provide flexible load-following generation. We develop a linear optimization model based on multi-physics reservoir simulations that captures the transient pressure and flow behaviors within a confined, engineered geothermal reservoir. We then optimize the investment decisions and hourly operations of a power plant exploiting such a reservoir against a set of historical and modeled future electricity price series. Further, we find that operational flexibility and in-reservoir energy storage can significantly enhance the value of geothermal plants in markets with high VRE penetration, with energy value improvements of up to 60% relative to conventional baseload plants operating under identical conditions. Across a range of realistic subsurface and operational conditions, our modeling demonstrates that confined, engineered geothermal reservoirs can provide large and effectively free energy storage capacity, with round-trip storage efficiencies comparable to those of leading grid-scale energy storage technologies. Optimized operational strategies indicate that flexible geothermal plants can provide both short- and long-duration energy storage, prioritizing output during periods of high electricity prices. Sensitivity analysis assesses the variation in outcomes across a range of subsurface conditions and cost scenarios.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Effect of particle size and moisture on flow performance of loblolly pine anatomical fractions: Experimental findings and model predictions

The rising energy demand has highlighted biomass as a promising next-generation energy source. However, commercializing biomass-derived energy faces challenges, particularly in handling biomass feedstock. Factors like particle size, shape, moisture content, and surface roughness significantly impact biomass flowability. This study addresses a crucial knowledge gap by examining the effects of particle size and moisture content on the flow behavior and shear properties of different anatomical fractions of loblolly pine (Pinus taeda). The bulk shear behavior was examined using a Schulze ring shear tester, while flow performance was tested through gravity-driven flow experiments in a variable wedge-shape hopper. Results were incorporated into empirical and machine learning-based flow prediction models to evaluate their accuracy and limitations. The study found that samples with higher moisture content show higher unconfined yield strength. The critical arching distance increased with particle size, e.g., from approximately 13 and 33 mm for 2- and 6-mm whole chips, respectively at a 32-degree inclination angle. Conversely, the flow rate decreased for a given hopper opening as particle size increased. For instance, at a 60-mm hopper opening and a 32-degree inclination angle, the mass flow rates for 2- and 6-mm whole chips were 7.83 and 6.42 tonne/h, respectively. The empirical model consistently overpredicted the mass flow rate for all anatomical fractions, while the machine learning model more accurately predicted the central tendency of flow rate but was insensitive to varying tissue proportions. These novel findings provide comprehensive characterization of anatomical fractions, reveal significant combined effects of particle size and moisture content on biomass flow behavior, and demonstrate a better predictive accuracy of a machine learning model, all of which are useful for optimizing material handling strategies and biomass utilization technologies in the industry.

09 - BIOMASS FUELS↗
DOE OSTI2022

Nonlinear gyrokinetic predictions of SPARC burning plasma profiles enabled by surrogate modeling

Multi-channel, nonlinear predictions of core temperature and density profiles are performed for the SPARC tokamak accounting for both kinetic neoclassical and fully nonlinear gyro-kinetic turbulent fluxes. A series of flux-tube, nonlinear, electromagnetic simulations using the CGYRO code with six gyrokinetic species are coupled to a nonlinear optimizer using Gaussian process regression techniques. The simultaneous evolution of energy sources, including alpha heat, radiation, and energy exchange, coupled with these high fidelity models and techniques, leads to a converged solution in electron temperature, ion temperature and electron density channels with a minimal number of expensive gyrokinetic simulations without compromising accuracy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Measurement-Based Approach for Inertia-Trend Analysis of the US Western Interconnection

Rising deployment of inverter-based resources (IBRs), characterized by a lack of rotating mass, is decreasing the total inertia of the system. This can lead to an increased Rate of Change of Frequency (RoCoF) during the disturbance and false activation of protective devices. There is a need to assess the inertia over the past decade amidst the evolving landscape of renewable energy sources to develop strategies for integrating energy storage, enhancing resilience measures, and ensuring the stable and reliable operation of the grid. Therefore, a realistic assessment of the inertia trend using a measurement-based approach that addresses the limitations of existing models is proposed. An inertia study of the Western Interconnection in the United States is performed utilizing the data from 2013 to 2022, obtained from FNET/ GridEye network. The three-second RoCoF time window is chosen for the study as it showed an optimum balance between a strong correlation with the power imbalance (ΔP) and minimum inclusion of primary response from governor. The obtained inertia trend result shows a small percentage declination of inertia over the decade. By examining the result alongside a generation mix graph, insights are gained into the dynamic interplay between shifting energy landscape and system inertia.

Dulal, Saurav↗
DOE OSTI2024

Preliminary Design of Nuclear Reactor Heat Delivery Systems: Integration with Reference Oil Refinery, Methanol Synthesis, and Hydrogen Production

The Department of Energy’s (DOE) Integrated Energy Systems (IES) program is generating comprehensive analyses validating the opportunity for using nuclear energy in a variety of applications including future clean grids, providing heat for direct use, and providing heat to help reduce emissions in chemical commodities. This work focuses on the preliminary designs of thermal delivery systems that can integrate heat produced from a nuclear core to industrial processes. The key research question that needs to be answered is: what is the prospective method for integrating nuclear generated heat energy into non-electric applications that can facilitate combined heat and power operations by advanced nuclear reactor systems? This research is a composition of case studies showing preliminary conceptual designs for thermal delivery systems integrating advanced nuclear systems with a few industrial systems including high temperature steam electrolysis, a reference oil refinery, and potential future methanol systems that supplant some natural gas use with nuclear energy. Piping and instrumentation diagrams have been developed to show the conceptual integration of nuclear systems with representative industrial systems. Different features of the configurations are dependent on the specific integration requirements including energy source conditions, demand quantity, and require energy application conditions. Design concepts are validated using thermodynamic balance calculations to verify system performance including calculating system losses during transport. Key components: pumps and compressors, heat exchangers, and network piping are reported with key design information and sizing.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

California Price Response Potential Study

California's energy landscape is undergoing a significant transformation, driven by the increasing integration of renewable energy sources, the increased adoption of distributed energy resources, the electrification of end-use loads, and the growing need for grid efficiency. To address these challenges, recent revisions to the State’s Load Management Standards (LMS) require all of California’s large utilities and community choice aggregators (CCAs) to offer dynamic electricity pricing options to customers by 2027. Dynamic pricing, which involves varying electricity rates based on real-time supply and demand conditions, offers a promising solution for optimizing grid operations, reducing costs, and incentivizing efficient use of grid capacity. Effective implementation of dynamic pricing requires understanding the potential impacts on customer bills, system load, and the cost-effectiveness of automation technologies. This study aims to evaluate the load response of various end-use devices to hourly dynamic prices. The end-uses studied here are space cooling, space heating, water heating, crop irrigation, pool and spa pumps, and electric vehicle (EV) charging, all for both residential and commercial applications, except for crop irrigation. In 2030, these end uses are forecasted to account for 18% of annual electricity demand in the state, but 40% of demand in the peak net load hour. By modeling possible price-responsive load dispatch algorithms and assessing the resulting impacts on both individual bills and the overall grid, we seek to inform policymakers and utilities about the potential benefits and challenges associated with dynamic pricing, and considerations for the design of dynamic pricing tariffs. Additionally, we will explore the cost effectiveness of adopting automation technologies to enable devices to respond more effectively to real-time price signals. This study considers a range of price profiles, accounting for differences across utilities and customer classes, and presents scenarios for dynamic price design via variation in the percentage of total customer electric costs that are allocated dynamically (versus constituting a fixed portion of the hourly volumetric price). We present results focused primarily on 2030, forecasting electricity prices under both low and high-cost scenarios, to inform longer-term tariff design considerations. We design tariffs by starting with 2019 prices that were calculated according to CalFUSE guidance (CPUC, 2022) and that have been used in recent studies; these prices are all-in volumetric rates that vary by utility and are revenue-neutral to each customer class. They are developed by considering six electricity cost components that are allocated hourly based on system load indicators (gross and net load, and wholesale prices). These prices are forecasted to 2030 for low and high cost scenarios, considering recent trends in total electricity costs with and without years of substantial wildfire mitigation investments. These tariffs, which allocate all costs on an hourly basis, are considered our “Full” dynamic tariff design scenario, while two additional scenarios explore allocating a portion of costs as a flat volumetric charge: the “Medium” scenario allocates 50% of revenue dynamically (and keeps 50% flat), while the “Mild” scenario allocates 20% of revenue dynamically. The 20% dynamic allocation on the Mild scenario aims to represent a case where only the marginal operating costs of the grid are included in the dynamic price.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Electrodes with Electrodeposited Water-excluding Polymer Coating Enable High-Voltage Aqueous Supercapacitors

Aqueous supercapacitors are powerful energy sources, but they are limited by energy density that is much lower than lithium-ion batteries. Since raising the voltage beyond the thermodynamic potential for water splitting (1.23 V) can boost the energy density, there has been much effort on water-stabilizing salvation additives such as Li 2 SO 4 that can provide an aqueous electrolyte capable of withstanding ~1.8 V. Guided by the first-principles calculations that reveal water can promote hydrogen and oxygen evolution reactions, here, we pursue a new strategy of covering the electrode with a dense electroplated polymerized polyacrylic acid, which is an electron insulator but a proton conductor and proton reservoir. The combined effect of salvation and coating expands the electrochemical window throughout pH 3 to pH 10 to 2.4 V for both fast and slow proton-mediated redox reactions. This allows activated carbon to quadruple the energy density, a kilogram of nitrogen-doped graphene to provide 127 Watt-hour, and both to have improved endurance because of suppression of water-mediated corrosion. Therefore, aqueous supercapacitors can now achieve energy densities quite comparable to that of a lithium-ion battery, but at 100 times the charging/discharging speed and cycle durability.

Dong, Wujie↗