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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Quantifying the challenge of reaching a 100% renewable energy power system for the United States

We simulate pathways for achieving up to 100% renewable energy (RE) electric power systems for the contiguous United States. Under base conditions, the least-cost buildout has RE penetration growing up to 57% in 2050. Relative to this base scenario, average CO2 abatement costs of achieving 80%, 90%, 95%, and 100% RE are $25, $33, $40, and $61/ton, respectively, with system costs growing from $30 to $36/MWh at 95% (achieved in 2040) and $39/MWh at 100%. Incremental abatement costs from 99% to 100% RE reach $930/ton, driven primarily by the need for firm RE capacity. In addition to the base conditions, we also examined 22 alternative conditions for a buildout of up to 100%. These sensitivities capture different technology trajectories, compliance requirements, requirement timing, electrification, and transmission availability. Nonlinear marginal costs for the last few percent approaching 100% RE were found for all sensitivities, which might motivate alternative nonelectric-sector abatement opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Geothermal Power Systems Analysis: Outcome of Industry Stakeholders Workshop

Geothermal cost and performance evaluation implemented via techno-economic assessment (TEA) modeling is critical for the U.S. Department of Energy (DOE) and other geothermal industry stakeholders in assessing the current state of geothermal technologies and to identify existing hurdles to commercially viable geothermal development. The Geothermal Electricity Technology Evaluation Model (GETEM) is a major TEA tool used in estimating the economic feasibility and levelized cost of energy (LCOE) of conventional hydrothermal systems and enhanced geothermal systems (EGS). Since 2021, GETEM has been transitioning from an intricate spreadsheet model to a user-friendly tool within the System Advisor Model (SAM) developed by the National Renewable Energy Laboratory (NREL). Apart from enabling an expanded visibility of the geothermal model among other renewable resources, having GETEM in SAM has the advantage of simulation automation, better usability, updates tracking, active user inputs/feedback, and extended financial modeling. GETEM is used in developing supply curves for NREL's Annual Technology Baseline (ATB), which provides inputs to the Renewable Energy Potential (reV) and the Regional Energy Deployment System (ReEDS) models. The geothermal module in NREL's reV model assesses the geothermal energy potential in the conterminous United States by defining the geospatial intersection of geothermal resources with existing grid infrastructure within the constraint of land use characteristics. The ReEDS model is a capacity expansion model used for simulating the long-term build-out and operation of the U.S. generation and transmission system based on current energy costs and policies. To ensure enhanced representation of current industry trends in our model transitions and development, we organized a two-day virtual workshop to elicit geothermal industry stakeholder input and recommendations on our current approaches and assumptions on techno-economic, resource assessment, and deployment scenarios modeling of geothermal technologies. Participants included developers, operators, investors, regulatory agencies, system modelers, national laboratory researchers, consultants, and other stakeholders. In this workshop, we gained stakeholder insights on current geothermal plant performance (i.e., capacity factors), updated drilling costs and learning curves, and next-generation technologies such as closed-loop and superhot rock geothermal. Other outcomes from this workshop and its impact on future geothermal development feasibility, resource availability, and capacity expansion studies are compiled and discussed.

annual technology baseline↗
DOE OSTI2024

Geothermal Power Systems Analysis: Outcome of Industry Stakeholders Workshop: Preprint

Geothermal cost and performance evaluation implemented via technoeconomic assessment (TEA) modeling is critical for the Department of Energy (DOE) and other geothermal industry stakeholders in assessing the current state of geothermal technologies and to identify existing hurdles to commercially viable geothermal development. The Geothermal Electricity Technology Evaluation Model (GETEM) is a major TEA tool used in estimating the economic feasibility and levelized cost of energy (LCOE) of conventional hydrothermal systems and enhanced geothermal systems (EGS). Since 2021, GETEM has been transitioning from an intricate spreadsheet model to a user-friendly tool within the System Advisor Model (SAM) developed by the National Renewable Energy Laboratory (NREL). Apart from enabling an expanded visibility of the geothermal model among other renewable resources, having GETEM in SAM has the advantage of simulation automation, better usability, updates tracking, active user inputs/feedback, and extended financial modeling. GETEM is used in developing supply curves for the Annual Technology Baseline (ATB). The ATB data are inputs to the Renewable Energy Potential (reV) and the Regional Energy Deployment System (ReEDS) models. The geothermal module in NREL’s reV model assesses the geothermal energy potential in the conterminous United States by defining the geospatial intersection of geothermal resources with existing grid infrastructure within the constraint of land use characteristics. The ReEDS model is a capacity expansion model used for simulating the long-term build-out and operation of the US generation and transmission system based on current energy costs and policies. To ensure enhanced representation of current industry trends in our model transitions and development, we organized a two-day virtual workshop to elicit geothermal industry stakeholder input and recommendations on our current approaches and assumptions on technoeconomic, resource assessment, and deployment scenarios modeling of geothermal technologies. Participants included developers, operators, investors, regulatory agencies, system modelers, national laboratory researchers, consultants, and other stakeholders. In this workshop, we gained stakeholder insights on current geothermal plant performance (i.e., capacity factors), updated drilling costs and learning curves, and next generation technologies such as closed loop and superhot rock geothermal. Other outcomes from this workshop and its impact on future geothermal development feasibility, resource availability, and capacity expansion studies are compiled and discussed.

Annual Technology Baseline↗
DOE OSTI2022

Modeling Distributed Energy Resources for Analyzing Distribution Systems with High Renewable Penetration: Preprint

Increasing levels of inverter-based distributed energy resources (IBDERs) impact the legacy overcurrent distribution protection systems. The fault current injections of IBDERs are limited to between 1-2 p.u. of the rated current. The combination of the limited fault current and varying system load makes it increasingly difficult to reliably set the overcurrent protection. To address this challenge, researchers are proposing adaptive overcurrent protection mechanisms that can adapt to changing system conditions. Accurate modeling of IBDERs is required to develop reliable protection schemes. This paper presents a photovoltaics-based IBDER model in OpenDSS for adaptive overcurrent protection. The paper presents the validation of the proposed IBDER model against a detailed model in electromagnetic transient programs. Protection analysis of the Electric Power Research Institute J1 feeder with the proposed IBDER model is also presented.

adaptive↗
DOE OSTI2025

An analysis of physics limited dispatch of nuclear renewable integrated energy systems using deep reinforcement learning and dynamic modeling

Previous approaches to dispatching nuclear integrated energy systems (NIES) have focused on the profitability and flexibility of these systems to operate on energy grids with highly variable pricing. However, due to the complexity involved in modeling and designing these systems, there has been less emphasis on ensuring that these dispatch strategies are physically achievable. It is imperative to develop methods that allow the system to remain within the desired NIES operating conditions and perform this based on realistic limited forecasted information. This research employs next generation artificial intelligence, namely deep reinforcement learning (DRL), and a dynamic system model written in Modelica to find a safe and profitable dispatch strategy for a solar nuclear hybrid design. The DRL agent is shown to find a novel dispatch strategy that manages both power ramping and power levels while respecting operational limits. This DRL-based dispatch is compared to other dispatching strategies including an optimal design solution from mixed integer linear programming (MILP). It is found that incorporating the physics of such a tightly coupled NIES limits the profitability of the MILP-based dispatch strategy. As a result, the MILP solution overestimates the design’s generated revenue. In contrast, DRL significantly reduces the number of breaches of safe operational conditions during energy arbitrage while maintaining profitability. Furthermore, this work paves the way for a more detailed assessment of NIES profitability and could be used to aid operator decisions on future NIES projects.

14 - SOLAR ENERGY↗
DOE OSTI2023

Dispatch optimization of electric thermal energy storage within System Advisor Model

A stand-alone electric thermal energy storage (ETES) system converts low-value electricity into heat using resistance heating elements. During periods of high-value electricity, an ETES system uses a thermodynamic power cycle to convert stored thermal energy back to electricity. These dispatchable systems derive value from their ability to store energy when prices are low and generate electricity when prices are favorable, i.e., energy arbitrage. Consequently, dispatch optimization of system operations, through maximizing revenue subject to system constraints, is essential to evaluate the economic value of a particular system design. While stand-alone ETES systems offer potential advantages as dispatchable grid storage technologies, there is a lack of a neutral third-party, publicly available, open-source model to evaluate the performance, dispatch, and financial viability of these systems. To address this problem, we have developed a techno-economic model for stand-alone ETES systems, within National Renewable Energy Laboratory's (NREL's) System Advisor Model (SAM). We implement a mixed-integer linear program to determine an ETES optimal operating schedule that maximizes electricity sales less maintenance costs caused by operation and cycling given temporal-varying grid electricity prices. Our contributions include a mixed-integer linear program for energy arbitrage of an ETES system, an ETES performance model through a publicly-available software (i.e., SAM), and an exercise of our model through case studies that compare ETES operational strategies and annual financial metrics. With our dispatch optimization model, we were able to improve revenue by 20% compared to a myopic heuristic while reducing the operational cost of the ETES system through decreases in cycle starts, cycles per day, and heater starts.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

PyPVRPM: Photovoltaic Reliability and Performance Model in Python

The ability to perform accurate techno-economic analysis of solar photovoltaic (PV) systems is essential for bankability and investment purposes. Most energy yield models assume an almost flawless operation (i.e., no failures); however, realistically, components fail and get repaired stochastically. This package, PyPVRPM, is a Python translation and improvement of the Language Kit (LK) based PhotoVoltaic Reliability Performance Model (PVRPM), which was first developed at Sandia National Laboratories in Goldsim software (Granata et al., 2011) (Miller et al., 2012). PyPVRPM allows the user to define a PV system at a specific location and incorporate failure, repair, and detection rates and distributions to calculate energy yield and other financial metrics such as the levelized cost of energy and net present value (Klise, Lavrova, et al., 2017). Our package is a simulation tool that uses NREL’s Python interface for System Advisor Model (SAM) (National Renewable Energy Laboratory, 2020b) (National Renewable Energy Laboratory, 2020a) to evaluate the performance of a PV plant throughout its lifetime by considering component reliability metrics. Besides the numerous benefits from migrating to Python (e.g., speed, libraries, batch analyses), it also expands on the failure and repair processes from the LK version by including the ability to vary monitoring strategies. These failures, repairs, and monitoring processes are based on user-defined distributions and values, enabling a more accurate and realistic representation of cost and availability throughout a PV system’s lifetime.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Modeling of Microgrid for Critical Data Center Applications

As part of continuing efforts to develop support, understanding, and infrastructure of data center integration onto the electric grid, our work aims to model and analyze the behavior of data center loads in a microgrid power system. Our model consists of renewable energy sources, batteries, and a nuclear reactor-steam Rankine cycle to power data center loads. The simulation studies investigate the electrical behavior of the microgrid system to assess its ability in supporting large data center electrical demands.

14 - SOLAR ENERGY↗
DOE OSTI2023

The value of concentrating solar power in ancillary services markets

Ancillary services, such as spinning reserves, can provide grid reliability and contribute to profitability of an energy resource. We exercise an existing dispatch optimization model to estimate the profitability of a concentrating solar power plant by incorporating the sale of spinning reserves in the ancillary service market using the National Renewable Energy Laboratory's System Advisor Model to simulate operations within a 72-h rolling horizon framework. Assuming a price-taker approach with day-ahead energy and spinning reserve prices from both the California Independent System Operator and the Electricity Reliability Council of Texas, we find that selling spinning reserves in addition to electric energy increases plant profitability by up to 7% with perfect knowledge of day-ahead pricing and solar resource availability. Here, this finding suggests that spinning reserve markets provide significant value streams to concentrating solar power plants that can leverage thermal energy storage to offer reliable production in the short-to-medium term.

14 SOLAR ENERGY↗
DOE OSTI2025

Customized open source renewable energy models validated through PHIL lab experiments

Energy models for power systems require ongoing updates to reflect advancements in equipment technology and the increasing complexity of power electronic devices. This study utilizes a Power Hardware-in-the-Loop (PHIL) experimental setup to validate custom photovoltaic (PV) inverter models, aiming to enhance and expedite the development of advanced renewable energy models. The research compares the performance of a physical inverter with generic Renewable Energy Source (RES) models recommended by the Western Electricity Coordinating Council (WECC). As inverter-based renewable energy sources become more prevalent in modern electrical grids, it is crucial that dynamic models accurately represent their real-world behavior. Accurate models improve our understanding of these energy resources and their interactions with the grid. The proposed model enhancements are designed to better reflect real inverter performance, based on insights from PHIL experiments. These models are developed using the open source Modelica language and the OpenIPSL Modelica Library, allowing integration across various simulation tools without re-implementation. The paper concludes with a thorough assessment, comparing the enhanced models with PHIL experiments on a real PV inverter in a controlled laboratory setting. As a result, the study provides the enhanced WECC RES models and validation data as open source resources, facilitating further research and development.

Modelica↗
DOE OSTI2020

Off-design performance of molten salt-driven Rankine cycles and its impact on the optimal dispatch of concentrating solar power systems

This paper presents a model for improving off-design performance predictions for molten salt-driven Rankine power cycles, such as in concentrating solar power tower applications. The model predicts cycle off-design performance under various boundary conditions, including molten salt inlet temperature, mass flow rate, and ambient temperature. The model is validated using industry performance data and benchmarked with results from the literature. A complete concentrating solar power plant, inclusive of solar heliostat field and receiver, is then considered, by implementing the Rankine cycle off-design performance results into the National Renewable Energy Laboratory’s System Advisor Model software, which includes a tool that determines optimal power production schedules. The work improves upon the current System Advisor Model by updating off-design performance characteristics. A case study demonstrates the impact of cycle off-design behavior on annual performance for a stand-alone concentrating solar power system and a concentrating solar power-photovoltaic hybrid system. In addition, we demonstrate how cycle off-design performance influences optimal operator dispatch decisions and, thereby, overall system design and economics. We conclude that off-design cycle performance impacts “optimal” sub-system sizing, especially for a concentrating solar power-photovoltaic hybrid configuration in which concentrating solar power must dispatch in conjunction with photovoltaic generation.

14 SOLAR ENERGY↗
DOE OSTI2025

Energy, economic, and environmental tradeoffs at run-of-river hydropower facilities

Hydropower’s ability to quickly adapt to variability from wind and solar generation by fluctuation flow rates can allow the electricity grid to integrate more renewable capacity. However, these rapid flow fluctuations, required to meet variability needs, can negatively impact aquatic ecosystems. In this study, we quantified energy-economic-environment tradeoffs at five conventional hydropower facilities (i.e. hydropower produced ad a dam on a river channel) across the United States to identify a mix of operational regimes that can provide flexibility to support variable renewable energy integration and environmental protections. Model results show a range of ability to meet demand from 4.7% to 97.8% depending on which case study is considered. Additionally, when modeling the case study facilities on a range of RoR conditions, allowing a % of inflow as discharge, we found the range of 140–200% of inflow allowed as discharge lead to lowest environmental impact while meeting the highest amount of demand. Our sensitivity analysis results demonstrated the Richard-Baker Flashiness Index, used to measure flowrate changes, and Revenue, were negatively correlated with the percent of hydropower generation within the defined Regional Energy Deployment System balancing area (i.e. region in which energy demand and energy supply is balanced based on the Regional Energy Deployment System model) yet positively correlated to the variable renewable energy generation percentage in the defined balancing area. In conclusion, our results suggest hydropower operations can aid in increasing renewable energy generation while limiting environmental impacts when considering a holistic analysis of energy-economic-environment tradeoffs.

Economic impact↗
DOE OSTI2025

Impact Analysis of Utility-Scale Energy Storage on the ERCOT Grid in Reducing Renewable Generation Curtailments and Emissions

This paper explores the solutions for minimizing renewable energy (RE) curtailment in the Texas Electric Reliability Council of Texas (ERCOT) grid. By utilizing current and future planning data from ERCOT and the System Advisor Model from the National Renewable Energy Laboratory, we examine how future renewable energy (RE) initiatives, combined with utility-scale energy storage, can reduce CO2 emissions while reshaping Texas’s energy mix. The study projects the energy landscape from 2023 to 2033, considering the planned phase-out of fossil fuel plants and the integration of new wind/solar projects. By comparing emissions under different load scenarios, with and without storage, we demonstrate storage’s role in optimizing RE utilization. The findings of this paper provide actionable guidance for energy stakeholders, underscoring the need to expand wind and solar projects with strategic storage solutions to maximize Texas's RE capacity and substantially reduce CO2 emissions.

14 SOLAR ENERGY↗
DOE OSTI2022

A Review of Modeling Approaches for Understanding and Monitoring the Environmental Effects of Marine Renewable Energy

Understanding the environmental effects of marine energy (ME) devices is fundamental for their sustainable development and efficient regulation. However, measuring effects is difficult given the limited number of operational devices currently deployed. Numerical modeling is a powerful tool for estimating environmental effects and quantifying risks. It is most effective when informed by empirical data and coordinated with the development and implementation of monitoring protocols. We reviewed modeling techniques and information needs for six environmental stressor–receptor interactions related to ME: changes in oceanographic systems, underwater noise, electromagnetic fields (EMFs), changes in habitat, collision risk, and displacement of marine animals. This review considers the effects of tidal, wave, and ocean current energy converters. We summarized the availability and maturity of models for each stressor–receptor interaction and provide examples involving ME devices when available and analogous examples otherwise. Models for oceanographic systems and underwater noise were widely available and sometimes applied to ME, but need validation in real-world settings. Many methods are available for modeling habitat change and displacement of marine animals, but few examples related to ME exist. Models of collision risk and species response to EMFs are still in stages of theory development and need more observational data, particularly about species behavior near devices, to be effective. We conclude by synthesizing model status, commonalities between models, and overlapping monitoring needs that can be exploited to develop a coordinated and efficient set of protocols for predicting and monitoring the environmental effects of ME.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Application of Ocean Thermal Energy Conversion (OTEC) Systems for Powering Safety Monitoring Systems of Offshore Oil and Gas Operations. OESI 2.0 M-1 T-1-P1.1 – Objective 1 Interim Report: Summary of Offshore Oil and Gas Well Monitoring Systems and Process Power Requirements

This report is part of a comprehensive research initiative aimed at evaluating the technoeconomic feasibility of deploying greenhouse gas (GHG) emissions monitoring systems powered by small-scale renewable energy sources on the U.S. Outer Continental Shelf (OCS). The study has three primary objectives: summarizing available GHG monitoring systems for the OCS, modeling a renewable marine energy source (specifically ocean thermal energy conversion, or OTEC), and modeling the collocation of OTEC power sources with offshore oil and gas activities to offset power demand.

02 PETROLEUM↗
DOE OSTI2021

NREL Price Series Developed for the ARPA-E FLECCS Program

The price data for four regions (CAISO, ERCOT, MISO-W, and PJM-W) are developed using the ReEDS to PLEXOS conversion as described in (Gagnon et al. 2020). The reference ReEDS case chosen is based on the 2020 Standard Scenario Mid-case, which uses the 2020 ReEDS model version (Cole et al. 2020; Ho et al. 2021). All ReEDS model inputs use 2020 Standard Scenarios Mid-case assumptions except for CO2 prices, which are implemented as linearly increasing CO2 price trajectories beginning at $0/tCO2 in 2020 and ending at either $100/tCO2 or $150/tCO2 in 2035 to dive capacity expansion towards a low-carbon system that could support CCS deployment. However, these scenarios also prohibit CCS deployment in this time frame so that resulting price data are not influenced by the deployment and operation of CCS itself. Implementing the ReEDS to PLEXOS conversion tool, PLEXOS is then simulated using the 2035 ReEDS infrastructure for both CO2 price scenarios, with the following model version and setup: PLEXOS Version: 8.2 Solver: Xpress-MP 35.01.01 Mixed integer optimization relative gap 1% System configuration: • Total number of nodes: 134 (consistent with ReEDS balancing areas) • Line losses enforced using piecewise linear approximation • Energy dump was enabled Price data is aggregated to the ISO/RTO level using load-weighted averages. References: Cole, Wesley, Sean Corcoran, Nathaniel Gates, Daniel Mai, Trieu, and Paritosh Das. 2020. “2020 Standard Scenarios Report: A U.S. Electricity Sector Outlook.” NREL/TP-6A20-77442. Golden, CO: National Renewable Energy Laboratory. https://www.nrel.gov/docs/fy21osti/77442.pdf. Gagnon, Pieter, Will Frazier, Elaine Hale, and Wesley Cole. 2020. “Cambium Documentation: Version 2020.” NREL/TP-6A20-78239. National Renewable Energy Lab. (NREL), Golden, CO (United States). https://doi.org/10.2172/1734551. Ho, Jonathan, Jonathon Becker, Maxwell Brown, Patrick Brown, Ilya (ORCID:0000000284917814) Chernyakhovskiy, Stuart Cohen, Wesley (ORCID:000000029194065X) Cole, et al. 2021. “Regional Energy Deployment System (ReEDS) Model Documentation: Version 2020.” NREL/TP-6A20-78195. Golden, CO: National Renewable Energy Laboratory. https://doi.org/10.2172/1788425.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Development of typical solar years and typical wind years for efficient assessment of renewable energy systems across the U.S.

Weather data plays a critical role in renewable energy analysis. Compared to using multiple Actual Meteorological Years, simulations using a single typical year require significantly fewer computational resources. Previous efforts to create typical weather datasets for renewable energy analysis either lack justified or optimized strategies for selecting and weighting different weather parameters or are limited to a few specific locations. Here, in this study, we developed a dataset comprising Typical Solar Years (TSYs) and Typical Wind Years (TWYs) for over 2000 locations across the U.S., based on data from NASA's POWER project. The strategies for creating TSYs and TWYs were optimized based on the simulated outputs of various PV systems and wind turbines in 16 representative cities. This dataset provides an efficient means for the rapid evaluation and optimization of renewable energy systems throughout the entire U.S. Additionally, the optimal strategies identified in this study can be directly applied to create near-optimal TSYs and TWYs for most locations worldwide. However, readers can also employ the optimization approach presented in this work to develop optimal strategies tailored for particular regions.

NASA POWER↗