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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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DOE OSTI2025

Hourly Load Profile Dataset for Electric Transit Bus Depots in the United States

Transit buses operate primarily in dense urban areas, where nearby populations face increased exposure to fine particulates, nitrogen oxides, and other harmful pollutants. Electrifying transit buses presents a clear opportunity to reduce greenhouse gas emissions and improve urban air quality. However, widespread adoption may pose significant energy and infrastructure challenges, which can be mitigated through proactive planning and investment. This report presents a robust modeling framework and an initial estimation of the hourly electricity demand at transit bus depots across the United States. The resulting depot-level dataset, available at data.nrel.gov/submissions/282, provides valuable insights for infrastructure planning and electricity demand forecasting, supporting the scalable electrification of transit bus fleets nationwide.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Hourly Load Profile Dataset for Electric Port Cargo Handling Equipment in the United States

Historically, ports have relied on fossil fuels, particularly diesel, as their primary energy source. Transitioning to electric cargo handling equipment (eCHE) offers a promising solution, as this relatively mature technology eliminates tailpipe emissions, reduces harmful airborne particulates, lowers noise pollution, and supports decarbonization. This study develops an initial estimation of hourly electricity demand for eCHE at the top 25 container ports (by tonnage) in the United States. These datasets, accessible at data.nrel.gov/submissions/281, provide valuable insights into the electricity demand patterns and potential grid impacts associated with widespread eCHE adoption, forming a foundation for future refinement based on stakeholder feedback.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Techno-Economic Analysis of a Thermally Integrated Solid Oxide Fuel Cell and Compressed Air Energy Storage Hybrid System

Natural-gas-fueled solid oxide fuel cell (SOFC) systems have the potential for high-efficiency conversion of carbon to power due to the underlying electrochemical conversion process while readily facilitating carbon capture through the separation of the fuel and oxidant sources. Compressed air energy storage (CAES) technology can potentially store significant quantities of energy for later use with a high round-trip efficiency and lower cost when compared with state-of-the-art battery technology. The base load generation capability of SOFC can be coupled with CAES technology to provide a potentially flexible, low-carbon solution to meet the fluctuating electricity demands imposed by the increasing share of intermittent variable renewable energy (VRE) production. SOFC and CAES can be hybridized through thermal integration to maximize power output during periods of high electrical demand and then store power when either demand is low or renewable generation reduces power prices. The techno-economics of a low-carbon hybrid SOFC and CAES system was developed and investigated in the present study. The proposed hybrid system was found to be cost-competitive with other power-generating base-load facilities when power availability was considered. The hybrid system shows increased resilience to changes in a high VRE grid market scenario.

25 ENERGY STORAGE↗
DOE OSTI2024

Data Analytics and Visualization of Energy Systems for Critical Infrastructure Insights

Modernization of energy systems including transportation facilities provides opportunities for increased efficiency, expansion of commerce and meeting industry and federal goals. A significant increase in electrical demand is projected to meet these needs, which concentrates at facilities such as airports. For example, Xcel Energy working with two airports in their service area recently published information projecting an up to fivefold increase in electricity demand in the next 25 years [1]. Concurrently, the US Government Accountability Office (GAO) recently surveyed 30 commercial service airports identifying more than 300 outages of more than 5 minutes between 2015 and 2022 [2]. Power, reliability, and resilience planning becomes more important to safely maintain operations and the flow of commerce with fewer energy carriers providing necessary energy to safely move passengers and goods. NREL proposes to develop methodologies to allow owners, utilities, and federal agencies to dynamically analyze, forecast, and manage energy loads at airports, focused upon maintaining the flow of commerce in an efficient, sustainable, and resilient way. To address these energy challenges, a suite of technologies and methodologies can be leveraged to validate concepts, inform design, de-risk solutions and optimize energy management during deployment. These technologies include digitalization of energy systems, microgrid methodologies, and related energy technologies for building and vehicle loads. [1] Electrifying Airport Ecosystems - https://www.enterprisemobility.com/content/dam/enterpriseholdings/marketing/innovation-in-mobility/vehicle-innovation/airport-electrification-study-full-report-2024.pdf [2] Airport Infrastructure: Selected Airport's Efforts to Enhance Electrical Resilience https://www.gao.gov/products/gao-23-105203.

critcal infrastructure↗
DOE OSTI2021

The Effects of Climate Change on Interregional Electricity Market Dynamics on the U.S. West Coast

The United States (U.S.) West Coast power system is strongly influenced by variability and extremes in air temperatures (which drive electricity demand) and streamflows (which constrain hydropower production). As hydroclimate changes across the West Coast, a combination of forces may work in tandem to make its bulk power system more vulnerable to physical reliability issues and market price shocks. In particular, a warmer climate is expected to increase summer cooling (electricity) demands and shift the average timing of peak streamflow (hydropower production) away from summer to the spring and winter, depriving power systems of hydropower when it is needed the most. Here, we investigate how climate change could alter interregional electricity market dynamics on the West Coast, including the potential for hydroclimatic changes in one region (e.g. Pacific Northwest (PNW)) to “spill over” and cause price and reliability risks in another (e.g. California). We find that the most salient hydroclimatic risks for the PNW power system are changes in streamflow, while risks for the California system are driven primarily by changes in summer air temperatures, especially extreme heat events that increase peak system demand. Altered timing and amounts of hydropower production in the PNW do alter summer power deliveries into California but show relatively modest potential to impact prices and reliability there. Instead, it is future climate conditions (extreme heat) in California that may exert a stronger significant influence on prices and reliability in the PNW, especially if California continues to rely on its northern neighbor for imported power to meet higher summer demands.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

IM3 Projected US Data Center Locations

IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory Protected Areas Database of the United States (PAD-US) areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall (ORCID:0000000328077088)↗
DOE OSTI2026

IM3 Projected US Data Center Locations

IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway or within an airport area boundary Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory USGS Protected Areas Database of the United States (PAD-US) GAP status 1, 2, or 3 areas US National Parks Wetlands USFWS critical habitats BIA land areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall (ORCID:0000000328077088)↗
DOE OSTI2021

Topical Report – Findings on Subtask 2.7 – Wet ESP and Aerosol Testing at Coal Creek Station

Growing concerns over the impact of CO 2 emissions from combustion sources on global climate change have prompted numerous research and development projects aimed at developing cost-effective technologies for CO 2 capture. One family of technologies being demonstrated at pilot and full scale globally is postcombustion carbon capture (PCCC) systems that employ amine-based solvents. The captured CO 2 can be compressed and permanently stored underground or used for enhanced oil recovery. The proximity of North Dakota’s lignite-fired fleet of power plants to potential CO 2 storage options creates a unique atmosphere for PCCC within the state. However, the unique components present in lignite flue gas present a challenge for large-scale PCCC at North Dakota power plants by contributing to aerosol formation. Aerosols can negatively impact the long-term performance of amine-based solvents for CO 2 capture. Amine-based solvents are volatile, and flue gas particulate provides nucleation sites where amine vapors can condense as aerosols. Because aerosols cannot be easily captured at the column outlet using conventional technologies, the amine-laden aerosols escape the system and lead to accelerated solvent losses. Moreover, particulate components can chemically react with amines to form degradation products that can permanently deactivate the amine, cause fouling, and lead to hazardous emissions. Many of the elements that have been shown to catalyze solvent degradation are present in lignite coals and can exacerbate solvent replacement economics. Understanding this issue is critical to the implementation of solvent-based CO 2 capture systems as applied to lignite-fired generation systems. The Energy & Environmental Research Center (EERC) designed and carried out this project to fully characterize aerosol behavior with various control technologies installed to better optimize aerosol mitigation technology for CO 2 capture. To meet the goal of this project, the following objectives were identified: Determine the effectiveness of a wet electrostatic precipitator (WESP) on mitigating formation of problematic aerosols at Great River Energy’s Coal Creek Station, upstream of the PCCC system. Determine the effectiveness of the Mitsubishi Heavy Industries (MHI) proprietary amine emission reduction unit (AERU) as a postcapture solvent recovery system for reducing aerosol emissions and extending solvent life downstream of the PCCC system. Determine the impact of aerosols on the efficiency and degradation products of both commercial and advanced solvents within the PCCC system. Work was conducted at Coal Creek Station Unit 1 using a slipstream of flue gas from the outlet of the plant’s flue gas desulfurization (FGD) unit. Flue gas was routed through a pilot-scale FGD unit to remove SO 2 to very low levels (~1 ppm) and then through a direct contact cooler (DCC) to further cool the gas and to remove moisture. The gas exiting the DCC was then optionally routed through a WESP before passing to the CO 2 absorber columns. The MHI solvent was used to scrub CO 2 from the slipstream through a set of two absorber columns. The rich solvent was regenerated in a stripper column by heating to drive off captured CO 2 . Flue gas exiting the absorber column was routed to MHI’s proprietary AERU to recover entrained solvent. The system operated using a catch-and-release method where the CO 2 was separated to provide data on the process, but the captured CO 2 was released back into the host site stack. Particulate was measured, collected, and analyzed from multiple locations throughout the pilot-scale system. Unlike the performance observed in prior work, the inlet FGD and DCC did not remove significant particulate matter from the flue gas. This appears to be due to a difference in the nature of the particulate. The DCC seemed to increase particulate size and count, most likely owing to water condensing onto the surfaces of fly ash particles. When the WESP was operated, it achieved >95% particulate capture. Very little particulate matter or indications of solvent were detected at the AERU outlet. When operating with advanced KS 21 solvent, the particulate material at the AERU outlet was even further decreased. Solvent analysis showed that some species derived from flue gas and ash were slowly concentrating in the solvent over the duration of the test. The levels observed were reported to be within expected ranges and were not of concern to MHI. A high-level techno-economic assessment of installing CO 2 capture at Coal Creek Station suggested that, when using a standard monoethanolamine (MEA)-based solution with simple heat integration, the energy penalty to net generation would be 34%. The bulk of this was due to steam losses for regenerating solvent, followed by parasitic electrical demand for CO 2 compression and then by increased parasitic load for pushing flue gas through the absorber column. These demands could be decreased with a more advanced solvent that exhibits lower heat of regeneration and lower pressure drop than does a simple MEA solution. Further energy could be saved with more thorough heat integration to recover useful energy from the steam used for solvent regeneration. Installing a WESP was predicted to increase the cost of electricity by nearly $5/MWh. This would become cost-effective if solvent losses were roughly 10 times the baseline estimate when not using a WESP but could be returned to baseline by installing the WESP. Piping CO 2 for storage in more favorable geology could help with carbon capture and storage economics. Although storing off-site would necessitate construction of a CO 2 transport pipeline, the cost of this pipeline might be more than offset by reducing the number of wells required, the depths of the wells required, and the electrical demand for the CO 2 compressor. Additional factors that favor off-site storage costs include smaller expected CO 2 plume sizes, which translates to less monitoring and fewer landowner agreements. More detailed assessment of the specific geology in the region under and around Coal Creek Station would be needed to accurately assess the costs and benefits of different storage site options. This subtask was cofunded through the Energy & Environmental Research Center–U.S. Department of Energy Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by the North Dakota Industrial Commission.

Strege, Joshua↗
DOE OSTI2020

Scaling Up Energy Efficiency Investment in Emerging Markets - Private Sector Perspectives

Since 2000, electricity demand has flattened and decoupled from Gross Domestic Product (GDP) growth in the Organization for Economic Cooperation and Development (OECD) countries. This trend is anticipated to continue for the next several decades and is largely attributed to the implementation of energy efficiency measures. However, non-OECD countries (emerging markets) have experienced, and are projected to continue experiencing, increasing electricity demands. If the world is to meet the requirements of the Paris Agreement, annual investments in clean energy and energy efficiency need to increase by a factor of six by 2050, compared to 2015. Information presented in this paper is based on qualitative and quantitative data collection and analysis methods to provide an empirical understanding of barriers to private sector clean energy investment including microgrid development, energy efficiency, smart grid development, and utility-scale wind and solar in emerging markets. Through literature review, a survey, and a series of webinar dialogues, USAID and the U.S. Department of Energy National Renewable Energy Laboratory (NREL) solicited input from private sector actors, including developers, project financiers, manufacturers and technical assistance service providers, on the challenges they face to market entry in emerging markets, and their suggestions for improving market competitiveness.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Least-cost targets and avoided fossil fuel capacity in India’s pursuit of renewable energy

India has set aggressive targets to install more than 400 GW of wind and solar electricity generation by 2030, with more than two-thirds of that capacity coming from solar. This paper examines the electricity and carbon mitigation costs to reliably operate India’s grid in 2030 for a variety of wind and solar targets (200 GW to 600 GW) and the most promising options for reducing these costs. We find that systems where solar photovoltaic comprises only 25 to 50% of the total renewable target have the lowest carbon mitigation costs in most scenarios. This result invites a reexamination of India’s proposed solar-majority targets. We also find that, compared to other regions and contrary to prevailing assumptions, meeting high renewable targets will avoid building very few new fossil fuel (coal and natural gas) power plants because of India’s specific weather patterns and need to meet peak electricity demand. However, building 600 GW of renewable capacity, with the majority being wind plants, reduces how often fossil fuel power plants run, and this amount of capacity can hold India’s 2030 emissions below 2018 levels for less than the social cost of carbon. With likely wind and solar cost declines and increases in coal energy costs, balanced or wind-majority high renewable energy systems (600 GW or ≈ 45% share by energy) could result in electricity costs similar to a fossil fuel-dominated system. As an alternative strategy for meeting peak electricity demand, battery storage can avert the need for new fossil fuel capacity but is cost effective only at low capital costs (≈ USD 150 per kWh).

14 SOLAR ENERGY↗
DOE OSTI2020

The Effects of Wind Veer During the Morning and Evening Transitions

Direction and speed wind shear modify turbine performance by changing inflow conditions on turbine blades. Using observations from the 2013 CWEX campaign, we found the daily atmospheric boundary layer transitions (morning and evening) match periods of high electricity demand for a wind farm in central Iowa. Power production during these periods was undermined for large direction shear and low speed shear scenarios. The morning transition displayed larger direction shear over the rotor layer for most wind speeds compared to the evening period. This resulted in lower turbine performance for the morning compared to the evening and whole day. This study shows that the combined effect of direction and speed shear is affecting turbine operation during high electricity demand times of day, predominantly during the morning at this wind farm.

17 WIND ENERGY↗
DOE OSTI2023

Spatiotemporal Super-Resolution with Generative Machine Learning for Creating Renewable Energy Resource Data Under Climate Change Scenarios

As we plan for a future with higher penetrations of renewables and increasing electrification, it becomes more important to understand how the electricity grid will operate under a variety of weather events. We must also consider that the weather our future grid will experience will be different and possibly more extreme than the historical weather that we have extensive data for. We can use data from global climate models (GCMs) to help understand how our climate may change over the next several decades, but there is often a significant gap between the low-resolution GCM data and the high-resolution weather data required to study power systems under specific weather events. Therefore, our objective in this work is to develop tools that can bridge this gap by using low-resolution GCM data to create realistic high-resolution weather datasets that can be used to study renewable energy generation and electricity demand. To accomplish this objective, we have developed a set of generative machine learning models that can rapidly downscale GCM daily average output data at an approximate grid resolution of 100km to hourly data at an approximate 4 km grid resolution. The models can be used to create high resolution data from nearly any GCM included in the Coupled Model Intercomparison Project (CMIP) Phase 5 or 6. Our methods include all datasets regularly used to study the integration of wind and solar power plants as well as changes in electricity demand due to heating and cooling loads. These models and datasets enable power systems modelers to study climate change-influenced weather events and their impact on the grid. We have downscaled and validated wind, solar, temperature, and humidity data with very promising results. The generative machine learning methods are computationally efficient and produce data that has similar statistical characteristics to current state-of-the-art historical datasets. We have trained initial generative models and produced an initial dataset collectively referred to as Sup3rCC: Super-Resolved Renewable Energy Resource Data with Climate Change Impacts. The data covers a (mostly) historical period from 2015-2025 and a future period from 2050-2059. We have also taken hypothetical high-electrification load data and scaled the heating and cooling loads with respect to the 2050-2059 high-resolution Sup3rCC meteorology. The results show how future levels of renewable energy generation and electrified load may be impacted by climate change, setting the stage for capacity expansion models to consider a dynamic climate through model years.

climate change↗
DOE OSTI2020

Performance analysis of hybrid power configurations: Impact on primary energy intensity, carbon dioxide emissions, and life cycle costs

In this work, utilization of Natural gas and Hydrogen to support current and future building energy needs to offset the total electric demand while improving the grid resiliency and energy efficiency was investigated. Demand side energy management will play an important role in efficiently managing the available energy resources. Performance assessment of different power generation and energy management configurations is presented in this paper. Development of solutions in addressing grid resiliency by providing the ability to design suitable configurations for meeting individual building energy needs is discussed. Primary movers (PM) such as internal combustion engines (ICE) and fuel cells (FC) along with small scale auxiliary renewable energy source such as photovoltaics (PV) were considered. Key attributes of total carbon foot-print, life cycle costs including capital and operational expenditure, electric grid offset or peak shaving capability, thermal energy availability and its further potential to offset total electric demand, and primary energy intensity are analyzed and discussed in detail.

08 HYDROGEN↗
DOE OSTI2022

ASP (Adaptive Splines for Prediction) [SWR-22-66]

This software package provides functionality to fit piecewise cubic splines to model the relationship between a univariate X (independent variable) and a univariate Y (dependent variable). The output is akin to linear regression but has higher "capacity" in that it can learn more complicated relationships despite leveraging only a single predictor. It thus can learn very reasonable relationships between X and Y with minimal effort. The canonical intended use case is for predicting electric demand (Y) from temperature (X). There can be complicated, non-linear relationships between these two variables, but these can be reasonably approximated by the sum of: 1) an "aggregate" relationship between daily average temperature and daily average demand, and 2) intra-day patterns represented as hourly offsets from the daily average load. The spline functionality is used for learning a robust "aggregate" relationship without the inconvenience of manual feature engineering while still ensuring robust results. The model fitting workhorse is R's built-in smooth.spline. This package provides a suite of options for controlling how smooth.spline fits a spline to the data. In particular, it is designed to return a model with a single critical point (i.e., a single location along the domain/support where the first derivative is zero). This is to ensure that predicted changes in electric demand are always positive as temperatures become more extreme. This helps to prevent overfitting, particularly when the input data has few observations or has significant uncertainty from other sources.

Murphy, Sinnott↗
DOE OSTI2021

How structural differences influence cross-model consistency: An electric sector case study

Multiple models are often employed to describe a range of possible outcomes for one or more scenarios, yielding insights into causal relationships and their uncertainties. Electric sector capacity expansion scenarios are a common topic of such efforts due to the economic influence of the electric sector, but model results typically span a broad solution space despite efforts to harmonize input assumptions, making decision implications difficult to discern. This study investigates the relationship between input harmonization and cross-model scenario consistency under disparate electric sector scenarios. We compare cross-model consistency between two state-of-the-art electric sector capacity expansion models (GCAM-USA and ReEDS) for six electric sector scenarios comprising alternate assumptions about fossil fuel resource availability, technology innovation, and long-term economy-wide transitions under four harmonization configurations varying model representations of electricity demand, fuel prices, renewable resources, and capacity retirements. These comparisons reveal that cross-model consistency can vary across scenarios under a given harmonization configuration, suggesting that harmonization efforts must often be scenario-specific if comparable cross-model consistency is desired. Model structural differences can hinder consistency, and the impact of these differences can depend on the scenario. Ultimately, thorough harmonization can reveal insights into cross-model consistency, which can be used to tighten uncertainty bounds and improve the decision-making implications of multi-model activities.

Cohen, Stuart↗
DOE OSTI2020

Early retirement of power plants in climate mitigation scenarios

International efforts to avoid dangerous climate change aim for large and rapid reductions of fossil fuel CO 2 emissions worldwide, including nearly complete decarbonization of the electric power sector. However, achieving such rapid reductions may depend on early retirement of coal- and natural gas-fired power plants. Here, we analyze future fossil fuel electricity demand in 171 energy-emissions scenarios from Integrated Assessment Models (IAMs), evaluating the implicit retirements and/or reduced operation of generating infrastructure. Although IAMs calculate retirements endogenously, the structure and methods of each model differ; we use a standard approach to infer retirements in outputs from all six major IAMs and—unlike the IAMs themselves—we begin with the age distribution and region-specific operating capacities of the existing power fleet. We find that coal-fired power plants in scenarios consistent with international climate targets (i.e. keeping global warming well-below 2 °C or 1.5 °C) retire one to three decades earlier than historically has been the case. If plants are built to meet projected fossil electricity demand and instead allowed to operate at the level and over the lifetimes they have historically, the roughly 200 Gt CO 2 of additional emissions this century would be incompatible with keeping global warming well-below 2 °C. Thus, ambitious climate mitigation scenarios entail drastic, and perhaps un-appreciated, changes in the operating and/or retirement schedules of power infrastructure.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2020

The role of nuclear in China’s energy future: insights from integrated assessment

China, the world’s largest electricity consumer, has strong motivations to deploy nuclear power due to increasing electricity demand and environmental concerns. However, there are social, institutional, and technical barriers to future nuclear expansion. This paper provides an updated assessment of nuclear energy potential in China with analyses at both national and provincial levels and compares results with studies previously conducted. Using an integrated assessment model, this paper considers the interactions among different socioeconomic development pathways, technology options, climate policies, and social/political concerns on inland siting. Among all factors explored, climate policies have the most significant impact on accelerating the deployment of nuclear technologies. Inland siting of nuclear power plants increases total capacity, but the impact is limited without climate policy. The amount of annual nuclear generation across scenarios and studies is unlikely to rise above 11% by 2030 and 30% by 2050 of total Chinese electricity generation. A dramatic increase in total Chinese nuclear energy generating capacity – around 3900 TWh of nuclear generation by 2050 – is only possible in scenarios where China sees electricity demand increase substantially, sticks to and tightens its current Paris Accord commitments, achieves low costs of nuclear technologies, and allows inland siting of nuclear power plants.

China, integrated assesment, Nuclear Power Plants↗
DOE OSTI2021

Urban microclimate and its impact on building performance: A case study of San Francisco

Urban microclimate exerts an increasing influence on urban buildings, energy, and sustainability. This study uses 10-year measured hourly weather data at 27 sites in San Francisco, California, to (1) analyze and visualize the urban microclimate patterns and urban heat island effect; (2) simulate annual energy use and peak electricity demand of typical large office buildings and large hotels to investigate the influence of urban microclimate on building performance; (3) simulate indoor air temperature of a single-family house without air-conditioning during the record three-day heatwave of 2017, to quantify the divergence of climate resilience due to urban microclimate effect. Results show significant microclimate effects in San Francisco with up to 11 °C outdoor air temperature difference between the coastal and downtown areas on September 1, 2017, during the record three-day heatwave. The simulated energy results of the prototype large office and large hotel buildings using the 2017 weather data show over 100% difference in annual heating energy use and 65% difference in annual cooling energy use across different stations; as well as up to 30% difference in peak cooling electricity demand. The impacts on annual site or source energy use are minimal (less than 5%) as cooling and heating in a mild climate are a relatively small portion of overall building energy use in San Francisco. Results also show the microclimate effects influence indoor air temperature of unconditioned homes by up to 5 °C. Newer buildings and homes are much less affected by microclimate effects due to more stringent performance requirements of the building envelope and energy systems. Furthermore, these findings inform that San Francisco microclimate variations should be considered in urban energy planning, building energy codes and standards, as well as heat resilience policymaking.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗