SEARCH · Engineering Papers

Results for “variable energy generation”

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.

SourceResearch connectionFrom yearThrough year

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

At least 235 records · Page 13

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Nuclear Energy in Long-Term System Models: A Multi-Model Perspective

Long-term energy system models–including electric sector capacity expansion models–are widely used tools for informing planning, technology assessment, and policy analysis. Recent decarbonization goals and rapid technological change have increased the need to appropriately represent economic characteristics and technical details of energy system resources, including variable renewable energy, energy storage technologies, carbon-capture-equipped capacity, and nuclear energy. Nuclear power represents about 20% of electricity generation and 50% of carbon-free electricity in the United States as of 2021. However, there are many perspectives on the role of existing and new nuclear in the future U.S. energy system, which is reflected in the broad range of potential contributions reported in the literature. This project aims to understand how issues central to nuclear energy are represented in long-term energy models. Building on earlier collaborations that focused on variable renewable energy and energy storage, this project convenes four modeling teams that use national-scale long-term energy system models from the Electric Power Research Institute, the National Renewable Energy Laboratory, the U.S. Energy Information Administration, and the U.S. Environmental Protection Agency to share methods and data, update models, run coordinated scenarios, and identify research needs. Improving tools can provide more insightful analyses and ensure that methods are more transparent.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Leveraging concentrating solar power plant dispatchability: A review of the impacts of global market structures and policy

Concentrating solar power (CSP) integrated with thermal energy storage delivers flexible and dispatchable power, which is an increasingly valuable quality as electricity systems integrate growing penetrations of variable renewable energy. Valuing and compensating CSP's dispatchability and flexibility requires electricity market structures and policies that appropriately remunerate generation during high-value portions of the day. In this paper, we review previous analyses of CSP economics and deployment, and we find that continued CSP growth will require valuation mechanisms that appropriately compensate for CSP's flexibility during both plant design and plant operation. We then review market structures that drive CSP operations and dispatch in jurisdictions where CSP is being developed, with perspectives from Spain, Chile, Australia, Morocco, South Africa, the United States, China, and the United Arab Emirates (Dubai). Despite broad agreement that CSP's dispatchability provides value to electricity grids, countries' policies for remunerating and leveraging such dispatchability varies widely. As deployment of CSP and variable renewable energy grows, it will be increasingly important to redesign current integration policies to signal the delivery of CSP's grid services more appropriately.

14 SOLAR ENERGY↗

DOE OSTI2022

Charting Hydropower's Role in the Next-Generation Grid

To build a 100% clean energy power sector, the United States is adding more energy storage and variable renewable energy sources, like solar power and wind energy, to the grid. Hydropower and pumped storage hydropower (PSH) can help with both. These technologies already play a key role in providing flexible, low-carbon electricity to the U.S. power grid, and this role will become even more valuable as that grid evolves. That's why researchers at the National Renewable Energy Laboratory (NREL) are analyzing how the U.S. electricity sector could invest in hydropower and PSH using new data and modeling capabilities.

HYDRO ENERGY↗

DOE OSTI2021

A Nonstationary and Non-Gaussian Moving Average Model for Solar Irradiance

Historically, power has flowed from large power plants to customers. Increasing penetration of distributed energy resources such as solar power from rooftop photovoltaic has made the distribution network a two-way-street with power being generated at the customer level. The incorporation of renewables introduces additional uncertainty and variability into the power grid. Distribution network operation studies are being adapted to include renewables; however, such studies require high quality solar irradiance data that adequately reflect realistic meteorological variability. Data from satellite-based products are spatially complete, but temporally coarse, whereas solar irradiances exhibit high frequency variation at very fine timescales. We propose a new stochastic method for temporally downscaling global horizontal irradiance (GHI) to 1 min resolution, but we do not consider the spatial aspect due to limited availability of the in situ irradiance measurements. Solar irradiance's first and second-order structures vary diurnally and seasonally, and our model adapts to such nonstationarity. Empirical irradiance data exhibits highly non-Gaussian behavior; we develop a nonstationary and non-Gaussian moving average model that is shown to capture realistic solar variability at multiple timescales. We also propose a new estimation scheme based on Cholesky factors of empirical autocovariance matrices, bypassing difficult and inaccessible likelihood-based approaches. The model is demonstrated for a case study of three locations that are located in diverse climates through the United States. The model is compared against competitors from the literature and is shown to provide better uncertainty and variability quantification on testing data.

Cholesky factor↗

DOE OSTI2024

Cryptate binding energies towards high throughput chelator design: metadynamics ensembles with cluster–continuum solvation

A tiered forcefield/semiempirical/meta-GGA pipeline together with a thermodynamic scheme designed with error cancellation in mind was developed to calculate binding energies of [2.2.2] cryptate complexes of mono- and divalent cations. Stable complexes of Na, K, Rb, Ca, Zn and Pb were generated, revealing consistent cation–N lengths but highly variable cation–O lengths and an amine stacking mechanism potentially augmenting the cation size selectivity. Metadynamics, used for searching the high-dimensional potential energy surface, together with a cluster–continuum model for affordable – yet accurate – solvation modeling, enabled the discovery of more stable geometries than those previously reported. Similar solvation energy curve shapes for lone vs. coordinated ions enabled rapid solvation convergence via the cancellation of errors stemming from finite cluster sizes. In conclusion, an R 2 of 0.850 vs. experimental aqueous binding energies was obtained, validating this scheme as the backbone of a high-throughput workflow for chelator design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

A database of battery materials auto-generated using ChemDataExtractor

A database of battery materials is presented which comprises a total of 292,313 data records, with 214,617 unique chemical-property data relations between 17,354 unique chemicals and up to five material properties: capacity, voltage, conductivity, Coulombic efficiency and energy. 117,403 data are multivariate on a property where it is the dependent variable in part of a data series. The database was auto-generated by mining text from 229,061 academic papers using the chemistry-aware natural language processing toolkit, ChemDataExtractor version 1.5, which was modified for the specific domain of batteries. The collected data can be used as a representative overview of battery material information that is contained within text of scientific papers. Public availability of these data will also enable battery materials design and prediction via data-science methods. To the best of our knowledge, this is the first auto-generated database of battery materials extracted from a relatively large number of scientific papers. We also provide a Graphical User Interface (GUI) to aid the use of this database.

25 ENERGY STORAGE↗

DOE OSTI2026

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - NLR Historical Solar PV

The U.S. Department of Energy and National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis from variable sources, hydrogen compression and storage, and hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) research platform. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence data centers and other variable loads. This dataset entry describes the behavior of a 1.25-MW proton exchange membrane MC250 electrolyzer system, manufactured by Nel Hydrogen , [1] when fed historical data generated by the 430-kW, fixed-axis solar photovoltaic (PV) array located at NLR’s Flatirons Campus. (While the electrolyzer balance of plant supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack.) Solar PV power output data for the 2020 calendar year were categorized on a daily basis by total energy generation and standard deviation. Each day was then ranked by these metrics, and the 25th, 50th, and 100th percentiles were selected. The 75th percentile day did not exhibit sufficient variability to make for a valuable experiment. A similar process was used for the related historical wind dataset . [2] The historical days in 2020 that represented these percentiles are Dec. 19, March 29, and May 4, respectively. The entire solar day’s power profile was then fed through the MC250 electrolyzer. Due to its length, the 100th percentile day experiment was split into two parts, and the final 3 hours of the solar day were not captured. These final 3 hours contained no spikes or dips of interest and simply represented a slow decay of input solar power. Also, a single timestamp (13:13:47 on Jan. 14, 2026) was lost in the hydrogen system supervisory control and data acquisition. Finally, during the 25th percentile experiment (solar day Dec. 19, 2020) data recording was lost from 11:00:13 to 11:14:45. The roughly 15 minutes of the solar profile were rerun at the end of the experiment and spliced into this time slot during post-processing. The electrolysis system controls hydrogen production by varying direct current applied to the stack, from a maximum of 3,000 A to a minimum safe operation of 300 A, or 10%. Because the current–voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The historical solar profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz frequency. For more details on the statistical analysis process, see the slide deck “Public Reference Data for Megawatt-Scale Hydrogen Electrolysis: NLR Historical Solar PV Analysis and Profile Generation” accessible with this data entry. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single solar PV electrolysis experiment and is formatted as: {technology}_{percentile}_{scaling factor} For instance, “solarPV-430kW_25_2x.zip” reports the experiment using the 25th percentile solar data from the historical 2020 solar PV dataset, scaled to 200%. Scaling factors were applied to the generated solar PV power output files to more closely match the 1.25-MW capacity of the electrolyzer. Each .zip folder contains the following files: A .csv file containing raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production, electrolysis power consumption, and solar power input. A PDF file detailing the historical solar data statistical analysis used to generate the solar profile. An experiment labeled “characterization_200.zip” demonstrates the MC250 electrolyzer steady-state response with 30-minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all experiments combined into one dataset labeled "combined_solarPV_experiments.csv". [1] nelhydrogen.com/product/mc-series-electrolyser . [2] data.nlr.gov/submissions/316 .

08 HYDROGEN↗

DOE OSTI2023

Incorporation of market signals for the optimal design of post combustion carbon capture systems

Recent studies have shown that fossil generators equipped with post-combustion carbon capture (PCC) systems are needed to reduce the cost of deep decarbonization. Such generators need to be flexible and responsive to grid conditions, particularly in a high variable renewable energy (VRE) environment. In this work, we evaluate the net present value (NPV) of retrofitting an existing natural gas combined cycle (NGCC) unit with a flexible PCC system while incorporating market signals from a high VRE grid. We use our industrial partner’s NGCC configuration as representative of existing NGCC units and Svante’s rapid-temperature swing adsorption (TSA) for PCC. Because of its ability to rapidly startup/shutdown and ramp-up/ramp-down, the chosen capture technology is very attractive for load-following operations. For a given set of market signals, we formulate a two-stage stochastic multi-period optimization problem, under the price-taker assumption, to simultaneously optimize the design of the capture system and operation of the entire plant. Rigorous models for the NGCC unit, PCC system, and compression system are developed using commercial process simulators and validated with either plant or vendor data. For computational tractability, we develop surrogate/reduced-order models for use in the optimization problem. The surrogate model for the NGCC plant is constructed by linearizing the rigorous dynamic model at 75% load, while data-driven nonlinear surrogate models for the capture and compression systems are constructed using simulation data from the rigorous models. The optimization problem, formulated as a mixed integer bilinear program, is implemented in the IDAES® integrated platform and solved to global optimality using Gurobi 9.5. Using this formulation, we determine the profitability of retrofitting an existing NGCC unit with the chosen capture system for multiple regions in the U.S. under two scenarios with different carbon prices. Importantly, the results show that the optimal decision strongly depends on the region and on the carbon price, thereby demonstrating the importance of the inclusion of market signals in the design process.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Variable renewable energy deployment in low-emission scenarios: The role of technology cost and value

While rapid deployment of variable renewable energy (VRE) technologies, namely wind and solar PV, is often projected in 2C pathways generated by integrated assessment models, there is a wide range in projected VRE deployment by mid-century. Such differences could be the result of differences in assumptions about future technology costs and/or differences in model approaches for capturing other aspects of technology competitiveness. Here we introduce a consistent competitiveness metric, profitability-adjusted levelized cost of electricity (or PLCOE), to an integrated assessment model (EPPA) to evaluate the representation of technology competition, including VRE, in low-emission scenarios. We show that representing the value of technology (alongside cost) may significantly impact VRE deployment relative to scenarios without such an adjustment. In addition, we show that varying VRE costs by about 35% in 2050 results in differences in VRE deployment that span much of the range in outcomes (over the same period) observed in likely 2C scenarios assessed by the IPCC, suggesting that both cost and value are key drivers of VRE deployment in such scenarios. Given the central role that VRE technologies play in the electricity mix across most scenarios, we also find that alternative cost assumptions for VRE technologies can lead to changes in electricity prices, the associated demand for electricity, and total final and primary energy consumption. However, the demand for fuels other than electricity is relatively insensitive to VRE assumptions in the 2C scenarios considered here.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Effects of Deep Reductions in Energy Storage Costs on Highly Reliable Wind and Solar Electricity Systems.

We use 36 years (1980-2015) of hourly weather data over the contiguous United States (CONUS) to assess the impact of low-cost energy storage on highly reliable electricity systems that use only variable renewable energy (VRE; wind and solar photovoltaics). Even assuming perfect transmission of wind and solar generation aggregated over CONUS, energy storage costs would need to decrease several hundred-fold from current costs (to ~$1/kWh) in fully VRE electricity systems to yield highly reliable electricity without extensive curtailment of VRE generation. The role of energy storage changes from high-cost storage competing with curtailment to fill short-term gaps between VRE generation and hourly demand to near-free storage serving as seasonal storage for VRE resources. Energy storage faces "double penalties" in VRE/storage systems: with increasing capacity, (1) the additional storage is used less frequently and (2) hourly electricity costs would become less volatile, thus reducing price arbitrage opportunities for the additional storage.

14 SOLAR ENERGY↗

DOE OSTI2023

Synchrotron X-ray diffraction analysis of constituent phases in transition joint between nickel alloy 738LC and a MnFeCoNiCu alloy

Synchrotron x-ray diffraction (XRD) analysis was performed on transition joints between a single-phase MnFeCoNiCu alloy and Ni-base Alloy 738LC to efficiently identify the constituent phases across the interface, with different levels of material intermixing generated by laser-welding with variable power. Here, the intermixing extent was quantified by postmortem energy dispersive x-ray spectroscopy mapping. Diffraction-based analyses on complex composition spaces with limited prior data present significant challenges because elemental substitution in both disordered and ordered phases is more extensive than in conventional alloy spaces, which may effectuate relatively large shifts in the observed lattice parameters that convolute the analysis. Therefore, thermodynamic simulations and crystallographic literature data were employed to construct a system-specific diffraction library of twelve prospective phases for the composition space investigated. Subsequently, for predicted disordered cubic phases, statistical hard-sphere models were established to estimate the lattice parameters and predict diffraction peak positions for inclusion in the library. The library was then employed to analyze diffraction profiles measured from the variably intermixed transition joints, with focus on accounting for both high and low-intensity peaks. 99.0 % of diffraction peaks with relative intensity greater than 0.001 were assigned to phases from the system-specific library, exemplifying rigorous peak accounting and indicating that no unexpected phases were present. Up to six of the twelve library phases were experimentally found in the transition joints. The lattice parameters predicted by the statistical hard-sphere model based upon thermodynamic simulations agree reasonably well with the measured values for the disordered FCC matrix phase.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Energy storage solutions to decarbonize electricity through enhanced capacity expansion modelling

To meet ambitious global decarbonization goals, electricity system planning and operations will change fundamentally. With increasing reliance on variable renewable energy resources, energy storage is likely to play a critical accompanying role to help balance generation and consumption patterns. As grid planners, non-profit organizations, non-governmental organizations, policy makers, regulators and other key stakeholders commonly use capacity expansion modelling to inform energy policy and investment decisions, it is crucial that these processes capture the value of energy storage in energy-system decarbonization. Here we conduct an extensive review of literature on the representation of energy storage in capacity expansion modelling. We identify challenges related to enhancing modelling capabilities to inform decarbonization policies and electricity system investments, and to improve societal outcomes throughout the clean energy transition. Additionally, we further identify corresponding research activities that can help overcome these challenges and conclude by highlighting tangible real-world outcomes that will result from pursuing these research activities. Capacity expansion modelling (CEM) approaches need to account for the value of energy storage in energy-system decarbonization. A new Review considers the representation of energy storage in the CEM literature and identifies approaches to overcome the challenges such approaches face when it comes to better informing policy and investment decisions.

25 ENERGY STORAGE↗

DOE OSTI2020

A Critical Exploration of the Efficiency Impacts of Demand Response From HVAC in Commercial Buildings

Increasing quantities of renewable energy generation has yielded a need for greater energy storage capacity in power systems. Thermal storage in variable air volume (VAV) heating, ventilation, and air conditioning (HVAC) in commercial buildings has been identified as an inexpensive source of grid storage, but the true costs are not known. Recent literature explores the inefficiency associated with providing grid services from these HVAC-based demand response (DR) resources by employing a battery analogy to calculate round-trip efficiency (RTE). Results vary significantly across studies and in some cases reported efficiencies are strikingly low. This article has three objectives to address these prior results. First, we synthesize and expand on insights into existing literature by systematically exploring the potential causes for the discrepancies in results. We reinforce previous work indicating baseline modeling may drive differences across studies and deduce that control accuracy plays a role in the major differences between experiments and simulation. Second, we discuss why the RTE metric is problematic for DR applications, discuss another proposed metric, additional energy consumption (AEC), and propose an extension, which we call uninstructed energy consumption (UEC), to evaluate DR performance. Finally, we explore the merits of different metrics using experimental data and highlight UEC's reduced sensitivity to the characteristics of the DR signal than previously proposed metrics.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Least-Cost Pathways for India's Electric Power Sector

The Government of India has a target of deploying 175 GW from renewable energy by 2022 and 40% of electricity capacity from renewable energy by 2030 and has indicated that ambitions for 2030 could be higher. Rapid changes in technology costs and performance could drive further deployment of wind and solar capacity beyond these policy targets. Increased deployment of variable renewable energy (VRE) raises new questions for power system planning regarding the optimal siting of generation capacity, trade-offs between generation and transmission infrastructure, and system flexibility needs. This study aims to evaluate least-cost pathways for India's electric power system over the period 2017-2047. Uniquely, this work considers an expanded planning horizon and range of scenarios not previously analyzed in national planning studies in India. The data collection and model design processes undertaken for this study provides a framework for recurring planning studies. This study finds anticipated changes in electricity demand and component costs can drive a significant shift in India's future electricity supply and how this system will be operated. In the Base scenario, the share of generation from VRE reaches 54% by 2047. Reducing the capital cost of wind has a larger impact on VRE penetration than reducing the capital cost of solar PV or battery storage. In the lowest wind cost scenario (40% capital cost decline by 2047 relative to the Base scenario), the penetration of VRE in the generation mix reaches 722%, exceeding the penetration levels achieved when the cost of battery storage or solar PV are reduced by an even greater 50%. In a future system with high penetrations of RE, capacity additions are driven by the coincidence of demand and RE generation rather than peak demand alone. This study finds the system could have surplus capacity during the peak demand months of July–September because this period corresponds to periods with high wind speeds and more wind generation available to meet peak demand. By contrast, new capacity is needed to meet demand during moderate demand months of October–November when output from wind plants falls more than 75% nationally compared to the previous two months. Finally, the success for gas for electricity production may depend on cost competitiveness rather than fuel availability. Increasing the amount of gas available for electricity production had no significant impact on the capacity or generation mix by 2047, as determined from a scenario that significantly increases fuel availability throughout the planning horizon. In fact, over 80% of new gas fuel available for the power sector remains unused. This suggests the high cost of gas plant operations relative to other technologies may constrain the expansion of gas generation in India more than fuel availability.

14 SOLAR ENERGY↗

DOE OSTI2023

Potential Availability of and Supply Curves for Low-Cost, Dispatch-Constrained Electricity

Among other changes, variable renewable energy (VRE) - primarily wind and solar photovoltaics - is achieving a growing share of total generation. High VRE penetrations may result in an increased level of curtailment and thus suppress the value of additional VRE. This VRE, that either would not be built due to price suppression or would be curtailed, can be considered a resource that we define as low-cost, dispatch-constrained electricity (LDE). LDE could be used for various applications which value low-cost electricity and can operate at reduced capacity factors. Examples include electrolytic hydrogen production and carbon capture. This report provides initial estimates of the quantity and availability of the potential LDE resource in the U.S. under scenarios with high VRE penetrations. It also provides supply curves that can be used in subsequent analysis of the opportunity to use the LDE. We modeled several scenarios using capacity expansion and production cost models for LDE prices ranging from $\$ $0/MWh to $\$ $30/MWh. These LDE prices, coupled with low renewable energy cost assumptions, resulted in VRE penetrations ranging from 48-66% in 2050. The resulting LDE supplies range from 100 - 300 TWh/yr at a price of $\$ $0/MWh to 3,500 - 4,200 TWh/yr at $\$ $30/MWh. Increasing LDE prices increases wind and photovoltaics deployment; however, other generation technology capacities do not decrease equivalently in our models. Thus, additional generation is available but the capacity of traditional dispatchable generation is only reduced slightly. The available LDE is concentrated in the central and southwest U.S. regions because of their high wind and solar resources.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1979

Control strategy for a variable-speed wind energy conversion system

A control concept for a variable-speed wind energy conversion system is proposed, for which a self-exited asynchronous cage generator is used along with a system of thyristor converters. The control loops are the following: (1) regulation of the entrainment speed as function of available mechanical energy by acting on the resistance couple of the asynchronous generator; (2) control of electric power delivered to the asynchronous machine, functioning as a motor, for start-up of the vertical axis wind converter; and (3) limitation of the slip value, and by consequence, of the induction currents in the presence of sudden variations of input parameters.

Jacob, A.↗

NASA NTRS1984

A kinetic energy analysis of the meso beta-scale severe storm environment

Analyses are performed of the meso beta-scale (20-200 km wavelengths and several hours to one-day periods) severe storm kinetic energy balance on the fifth day of the AVE SESAME campaign of May 1979. A 24-hr interval covering the antecedent, active and post-convective outbreak activity over Oklahoma are considered. Use is made of the kinetic energy budget equation (KEBE) for a finite volume in an isobaric coordinate system. Rawindsonde data with 75 km resolution were treated. The KEBE model covered changes in kinetic energy due to the cross contour flows, horizontal and vertical components of flux divergence, and volumic mass changes on synoptic and subsynoptic scales. The greatest variability was concentrated above 400 mb height and over the most intense storm activity. Energy was generated at the highest rates in divergence and decreased the most in convection. The meso beta-scale lacked sufficient resolution for analyzing mesoscale activity.

Fuelberg, H. E.↗

NASA NTRS2019

Radiative Heating Rates Computed With Clouds Derived From Satellite‐Based Passive and Active Sensors and their Effects on Generation of Available Potential Energy

Radiative heating rates computed with cloud properties derived from passive and active sensors are investigated. Zonal monthly radiative heating rate anomalies computed using both active and passive sensors show that larger variability in longwave cooling exists near the tropical tropopause and near the top of the boundary layer between ~50°N to ~50°S. Aerosol variability contributes to increases in shortwave heating rate variability. When zonal monthly mean cloud effects on the radiative heating rate computed with both active and passive sensors and those computed with passive sensor only are compared, the latter shows cooling and heating peaks corresponding to cloud top and base height ranges used for separating cloud types. The difference of these two sets of cloud radiative effect on heating rates in the middle to upper troposphere is larger than the radiative heating rate uncertainty estimated based on the difference of two active sensor radiative heating rate profile data products. In addition, radiative heating rate contribution to generation of eddy available potential energy is also investigated. Although radiation contribution to generation of eddy available potential energy averaged over a year and the entire globe is small, radiation increases the eddy available potential energy in the northern hemisphere during summer. Two key elements that longwave radiation contribute to the generation of eddy potential energy are (1) longitudinal temperature gradient in the atmosphere associated with land and ocean surface temperatures contrasts and absorption of longwave radiation emitted by the surface and (2) cooling near the cloud top of stratocumulus clouds.

Kato, Seiji↗