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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Computational framework for behind-the-meter DER techno-economic modeling and optimization: REopt Lite

The energy system is undergoing a major transformation with the global emphasis on decarbonization. Distributed generation is projected to play a significant role in the new energy system, and energy models are informing how distributed generation can be integrated reliably and economically. In this work, we present an end-to-end computational framework for distributed energy resource (DER) modeling, REopt Lite™, which captures the interface of technology, economics, and policy in the energy modeling process. We describe the problem space, the building blocks of the model, the scaling capabilities of the design, the optimization formulation, and the extensibility of the model. We present a framework for accelerating the techno-economic analysis of behind-the-meter distributed energy resources to enable rapid planning and decision-making, thereby enabling greater renewable energy deployment. This computation framework is open-sourced to facilitate transparency, flexibility, and wider collaboration opportunities within the worldwide energy modeling community.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

A data-driven operational model for traffic at the Dallas Fort Worth International Airport

Airports are on the front line of significant innovations, allowing the movement of more people and goods faster, cheaper, and with greater convenience. As air travel continues to grow, airports will face challenges in responding to increasing passenger vehicle traffic, which leads to lower operational efficiency, poor air quality, and security concerns. This paper evaluates methods for traffic demand forecasting combined with traffic microsimulation, which will allow airport operations staff to accurately predict traffic and congestion. Using two years of detailed data describing individual vehicle arrivals and departures, aircraft movements, and weather at Dallas-Fort Worth (DFW) International Airport, we evaluate multiple prediction methods including the Auto Regressive Integrated Moving Average (ARIMA) family of models, traditional machine learning models, and DeepAR, a modern recurrent neural network (RNN). We find that these algorithms are able to capture the diurnal trends in the surface traffic, and all do very well when predicting the next 30 minutes of demand. Longer forecast horizons are moderately effective, demonstrating the challenge of this problem and highlighting promising techniques as well as potential areas for improvement. Traffic demand is not the only factor that contributes to terminal congestion, because temporary changes to the road network, such as a lane closure, can make benign traffic demand highly congested. Combining a demand forecast with a traffic microsimulation framework provides a complete picture of traffic and its consequences. The result is an operational intelligence platform for exploring policy changes, as well as infrastructure expansion and disruption scenarios. To demonstrate the value of this approach, we present results from a case study at DFW Airport assessing the impact of a policy change for vehicle routing in high demand scenarios. This framework can assist airports like DFW as they tackle daily operational challenges, as well as explore the integration of emerging technology and expansion of their services into long term plans.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Reward-Based Participant Management for Crowdsourcing Rainfall Monitoring: An Agent-Based Model Simulation

Crowdsourcing incorporates common citizens as rich sources of data and is promising for environmental monitoring. Here, we propose and test the idea of incorporating incentives to crowdsourcing management for rainfall monitoring. In particular, we model the allocation of incentives (quantitatively measurable and limited rewards) among crowdsourcing participants for a theoretical rainfall monitoring case. For this purpose, we develop an integrated model comprising a reward allocation component to represent the decision-making process of a central manager, an agent-based model to simulate the interactions between the manager and participants, and a rainfall simulation model to evaluate the effectiveness of various reward allocation policies. We simulate six reward allocation policies of varying levels of administrative cost, and consideration of participant and rainfall spatial heterogeneities. The findings indicate the performance of each policy to improve with the reward budget and their spatial uniformity. Among the six policies tested, we discover that the participant density weighted maximum participation policy yields the most accurate estimation of rainfall intensity due to its more explicit consideration of the spatial distribution of participants; however, this policy associates with a high administrative cost. This highlights the trade-off between performance and cost in designing effective reward allocation policies. This paper provides a physical and behavior simulation modeling tool to study the feasibility and complexity of reward-based participant management for crowdsourcing rainfall monitoring. The proposed crowdsourcing method is beneficial for a wide range of applications that require rainfall data with fine resolution, such as storm water management and water availability and biomass assessment for food and energy crops.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

An approach to and web-based tool for infectious disease outbreak intervention analysis

Infectious diseases are a leading cause of death globally. Decisions surrounding how to control an infectious disease outbreak currently rely on a subjective process involving surveillance and expert opinion. However, there are many situations where neither may be available. Modeling can fill gaps in the decision making process by using available data to provide quantitative estimates of outbreak trajectories. Effective reduction of the spread of infectious diseases can be achieved through collaboration between the modeling community and public health policy community. However, such collaboration is rare, resulting in a lack of models that meet the needs of the public health community. Here we show a Susceptible-Infectious-Recovered (SIR) model modified to include control measures that allows parameter ranges, rather than parameter point estimates, and includes a web user interface for broad adoption. We apply the model to three diseases, measles, norovirus and influenza, to show the feasibility of its use and describe a research agenda to further promote interactions between decision makers and the modeling community.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Control strategy optimization of HVAC plants

In this paper we present a methodology to optimize the operating conditions of heating, ventilation and air conditioning (HVAC) plants to achieve a higher energy efficiency in use. Semi-empiric numerical models of the plant components are used to predict their performances as a function of their set-point and the environmental and occupied space conditions. The optimization is performed through a graph-based algorithm that finds the set-points of the system components that minimize energy consumption and/or energy costs, while matching the user energy demands. The resulting model can be used with systems of almost any complexity, featuring both HVAC components and energy systems, and is sufficiently fast to make it applicable to real-time setting.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

SEE Action Guide for States: Energy Efficiency as a Least-Cost Strategy to Reduce Greenhouse Gases and Air Pollution and Meet Energy Needs in the Power Sector

This guide is designed to provide information to state decision makers and staff on options to advance energy efficiency through strategies designed or implemented at the state and local levels of government and in the private sector.1 The information in this guide is intended to be useful to a wide variety of partners and stakeholders involved in energy-related discussions and decision-making at state and local levels. These energy efficiency options, or “pathways” as they are identified in this guide, can assist states in using energy efficiency to meet air pollution reduction and other policy objectives such as energy affordability and reliability. A pathway is a set of interdependent actions that results in measurable energy savings streams and associated avoided air emissions and other benefits over a period of time. These activities can include state, local, or private sector regulations, policies, programs and other activities. For each of five broad pathways that offer sizable cost-effective energy savings, the guide addresses likely questions policy makers and regulators face when screening for the best opportunities to advance energy efficiency in their state.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Variable Renewable Energy in Long-Term Planning Models: A Multi-Model Perspective

Long-term capacity expansion models of the U.S. electricity sector have long been used to inform electric sector stakeholders and decision-makers. With the recent surge in variable renewable energy (VRE) generators — primarily wind and solar photovoltaics — the need to appropriately represent VRE generators in these long-term models has increased. VRE generators are especially difficult to represent for a variety of reasons, including their variability, uncertainty, and spatial diversity. This report summarizes the analyses and model experiments that were conducted as part of two workshops on modeling VRE for national-scale capacity expansion models. It discusses the various methods for treating VRE among four modeling teams from the Electric Power Research Institute (EPRI), the U.S. Energy Information Administration (EIA), the U.S. Environmental Protection Agency (EPA), and the National Renewable Energy Laboratory (NREL). The report reviews the findings from the two workshops and emphasizes the areas where there is still need for additional research and development on analysis tools to incorporate VRE into long-term planning and decision-making. This research is intended to inform the energy modeling community on the modeling of variable renewable resources, and is not intended to advocate for or against any particular energy technologies, resources, or policies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Review of Energy Equity Metrics

A literature review of energy equity and energy justice metrics was performed to support efforts to develop an energy equity metrics framework. Pacific Northwest National Laboratory (PNNL) reviewed the available literature, surveyed work in progress on the topic, and solicited expert feedback to lay the groundwork for metrics development and provide reference material for equity research and development applications. The literature review identified three distinct equity metric types: target population identification, investment decision making, and program impact assessment.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Decarbonizing Medium- & Heavy-Duty On-Road Vehicles: Zero-Emission Vehicles Cost Analysis

We simulate adoption and energy consumption of zero-emission vehicles (ZEVs) in the medium and heavy duty (MD/HD) sector. With continued improvement in ZEV technologies, total-cost-of-driving parity with conventional vehicles is achievable by 2035 for all MD/HD vehicle classes. Two technological solutions – battery electric vehicles (BEVs) and fuel cell electric vehicles (FCEVs) are viable in multiple market segments, offering alternative pathways for decarbonization. In this scenario, ZEV sales reach 99+% by 2045 and 42% by 2030. 80% of the MD/HD stock transitions to ZEVs by 2050, reducing CO2 emissions by 69% from 2019. Disparities in adoption rate and vehicle usage between vehicle classes and between short and long-haul applications highlight opportunities for targeted policies (to reduce emissions it is critical to understand what vehicles transition to ZEV, not just how many vehicles). Results are highly sensitive to assumed fuel prices, technology improvement trajectories, adoption decision-making, and assumptions about future freight demand and vehicle use.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

We Must Stop Fossil Fuel Emissions to Protect Permafrost Ecosystems

Climate change is an existential threat to the vast global permafrost domain. The diverse human cultures, ecological communities, and biogeochemical cycles of this tenth of the planet depend on the persistence of frozen conditions. The complexity, immensity, and remoteness of permafrost ecosystems make it difficult to grasp how quickly things are changing and what can be done about it. Here, we summarize terrestrial and marine changes in the permafrost domain with an eye toward global policy. While many questions remain, we know that continued fossil fuel burning is incompatible with the continued existence of the permafrost domain as we know it. If we fail to protect permafrost ecosystems, the consequences for human rights, biosphere integrity, and global climate will be severe. The policy implications are clear: the faster we reduce human emissions and draw down atmospheric CO 2 , the more of the permafrost domain we can save. Emissions reduction targets must be strengthened and accompanied by support for local peoples to protect intact ecological communities and natural carbon sinks within the permafrost domain. Some proposed geoengineering interventions such as solar shading, surface albedo modification, and vegetation manipulations are unproven and may exacerbate environmental injustice without providing lasting protection. Conversely, astounding advances in renewable energy have reopened viable pathways to halve human greenhouse gas emissions by 2030 and effectively stop them well before 2050. We call on leaders, corporations, researchers, and citizens everywhere to acknowledge the global importance of the permafrost domain and work towards climate restoration and empowerment of Indigenous and immigrant communities in these regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

The Impact of Utility Tariff Evolution on Behind-the-Meter PV Adoption

This analysis uses a new method to link the NREL Regional Energy Deployment System (ReEDS) capacity expansion model with the NREL distributed generation market demand model (dGen) to explore the impact that the evolution of retail electricity tariffs can have on the adoption of distributed photovoltaics (DPV). The evolution most notably takes the form of decreased mid-day electricity costs, as low-cost PV reduces the marginal cost of electricity during those hours and the changes are subsequently communicated to electricity consumers through tariffs. We find that even under the low PV prices of the new SunShot targets the financial performance of DPV under evolved tariffs still motivates behind-the-meter adoption, despite significant reduction in the costs of electricity during afternoon periods driven by deployment of cheap utility-scale PV. The amount of DPV in 2050 in these low-cost futures ranged from 206 GW to 263 GW, a 13-fold and 16-fold increase over 2016 adoption levels respectively. From a utility planner's perspective, the representation of tariff evolution has noteworthy impacts on forecasted DPV adoption in scenarios with widespread time-of-use tariffs. Scenarios that projected adoption under a portfolio of time-of-use tariffs, but did not represent the evolution of those tariffs, predicted up to 36 percent more DPV in 2050, compared to scenarios that did not represent that evolution. Lastly, we find that a reduction in DPV deployment resulting from evolved tariffs had a negligible impact on the total generation from PV - both utility-scale and distributed - in the scenarios we examined. Any reduction in DPV generation was replaced with utility-scale PV generation, to arrive at the quantity that makes up the least-cost portfolio.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Primal-Dual Differentiable Programming for Distribution System Critical Load Restoration: Preprint

Swift and reliable critical load restoration (CLR) can help make a distribution system resilient towards extreme events. To optimally achieve that, alongside practical concerns such as limiting online computational burden, some studies leverage model-free reinforcement learning (RL) to train control policies. Despite the advantages provided by RL algorithms, these approaches suffer from two issues: 1) the lack of a proper mechanism for constraint enforcement, and 2) poor sample efficiency. Therefore, in this paper, a primal-dual differentiable programming (PDDP) method is developed for guiding the training leading to a constraint-satisfying policy. Additionally, the model-based nature of the proposed method aims at improving sample efficiency. The experiment on a CLR problem demonstrates that PDDP can effectively train a control policy that both achieves desirable performance and satisfies required constraints.

differentiable programming↗
NASA NTRS2012

Lessons Learned in Decadal Planning in Space Science

This summary has been prepared by the workshop rapporteurs as a factual summary of what occurred at the workshop. The planning committee's role was limited to planning and convening the workshop. The views contained in the report are those of individual workshop participants and do not necessarily represent the views of all workshop participants, the planning committee, or the National Research Council. This summary was written using video recordings of the entire workshop proceedings. Speakers attributed in this summary were not asked to review the workshop summary before its release. This workshop summary has been reviewed in draft form by individuals chosen for their diverse perspectives and technical expertise, in accordance with procedures approved by the NRC's Report Review Committee. The purpose of this independent review is to provide candid and critical comments that will assist the institution in making its published report as sound as possible and to ensure that the summary meets institutional standards for objectivity, evidence, and responsiveness to the study charge. The review comments and draft manuscript remain confidential to protect the integrity of the deliberative process. We wish to thank the following individuals for their review of this summary: George Paulikas, The Aerospace Corporation (retired), Marcia Rieke, University of Arizona, Kunio Sayanagi, Hampton University, Marcia Smith, Space and Technology Policy Group, LLC, and Warren Washington, National Center for Atmospheric Research. Although the reviewers listed above have provided many constructive comments and suggestions, they did not see the final draft of the workshop summary before its release. The review of this report was overseen by Kenneth Kellermann, National Radio Astronomy Observatory. Appointed by the NRC, he was responsible for making certain that an independent examination of this report was carried out in accordance with institutional procedures and that all review comments were carefully considered. Responsibility for the final content of this report rests entirely with the authors and the institution.

Groswald, Lewis↗
DOE OSTI2024

Decision-making based on Markov decision process in integrated artificial reasoning framework—Part I: Theory

This paper presents a decision-making framework based on an integrated artificial reasoning framework and Markov decision process (MDP). The integrated artificial reasoning framework provides a physics-based approach that converts system information into state transition models, and the analysis result will be represented by the transition probabilities that can be used with an MDP to find a traceable and explainable optimal pathway. A dynamic Bayesian network (DBN) is well suited for representing the structure of an MDP. The causality information among process variables (or among subsystems) is mathematically represented in a DBN by the conditional probabilities of the node’s states provided different probabilities of the parent node’s states. To define node states in a physically understandable manner, we used multilevel flow modeling (MFM). An MFM follows the fundamental energy and mass conservation laws and supports the selection of process variables that represent the system of interest so that causal relations among process variables are properly captured. An MFM-based DBN supports developing state transition models in an MDP to capture the effect of process variables of system having physical relations. The operators of the target system can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from component degradation or random failures. We analyzed a simplified exemplary system to illustrate an optimal operational policy using the suggested approach.

Markov decision process↗
DOE OSTI2013

Estimating Policy-Driven Greenhouse Gas Emissions Trajectories in California: The California Greenhouse Gas Inventory Spreadsheet (GHGIS) Model

A California Greenhouse Gas Inventory Spreadsheet (GHGIS) model was developed to explore the impact of combinations of state policies on state greenhouse gas (GHG) and regional criteria pollutant emissions. The model included representations of all GHG- emitting sectors of the California economy (including those outside the energy sector, such as high global warming potential gases, waste treatment, agriculture and forestry) in varying degrees of detail, and was carefully calibrated using available data and projections from multiple state agencies and other sources. Starting from basic drivers such as population, numbers of households, gross state product, numbers of vehicles, etc., the model calculated energy demands by type (various types of liquid and gaseous hydrocarbon fuels, electricity and hydrogen), and finally calculated emissions of GHGs and three criteria pollutants: reactive organic gases (ROG), nitrogen oxides (NOx), and fine (2.5 ?m) particulate matter (PM2.5). Calculations were generally statewide, but in some sectors, criteria pollutants were also calculated for two regional air basins: the South Coast Air Basin (SCAB) and the San Joaquin Valley (SJV). Three scenarios were developed that attempt to model: (1) all committed policies, (2) additional, uncommitted policy targets and (3) potential technology and market futures. Each scenario received extensive input from state energy planning agencies, in particular the California Air Resources Board. Results indicate that all three scenarios are able to meet the 2020 statewide GHG targets, and by 2030, statewide GHG emissions range from between 208 and 396 MtCO2/yr. However, none of the scenarios are able to meet the 2050 GHG target of 85 MtCO2/yr, with emissions ranging from 188 to 444 MtCO2/yr, so additional policies will need to be developed for California to meet this stringent future target. A full sensitivity study of major scenario assumptions was also performed. In terms of criteria pollutants, targets were less well-defined, but while all three scenarios were able to make significant reductions in ROG, NOx and PM2.5 both statewide and in the two regional air basins, they may nonetheless fall short of what will be required by future federal standards. Specifically, in Scenario 1, regional NOx emissions are approximately three times the estimated targets for both 2023 and 2032, and in Scenarios 2 and 3, NOx emissions are approximately twice the estimated targets. Further work is required in this area, including detailed regional air quality modeling, in order to determine likely pathways for attaining these stringent targets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Self-consumption for energy communities in Spain: A regional analysis under the new legal framework

European climate polices acknowledge the role that energy communities can play in the energy transition. Self-consumption installations shared among those living in the same building are a good example of such energy communities. In this work, a regional analysis of optimal self-consumption installations under the new legal framework recently passed in Spain is performed. Results show that the optimal sizing of the installation leads to economic savings for self-consumers in all the territory, for both options with and without remuneration for energy surplus. A sensitivity analysis on technology costs revealed that batteries still require noticeably cost reductions to be cost-effective in a behind the meter self-consumption environment. In addition, solar compensation mechanisms make batteries less attractive in a scenario of low PV costs, since feeding PV surplus into the grid, yet less efficient, becomes more cost-effective. Furthermore, an improvement for the energy surplus remuneration policy in the context of the current legislation was proposed and analysed. It consists in the inclusion of the economic value of the avoided power losses in the remuneration.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Regulatory Treatment of Non-Core Sources of Radioactivity for Advanced Reactor Designs

The recent resurgence in advance (non-light water) reactor development has been paralleled by the development of risk-informed performance-based (RIPB) licensing pathways. Specifically, the creation of the RIPB Licensing Modernization Project (LMP) approach and subsequent endorsement by the U.S. Nuclear Regulatory Commission (NRC) now provides advanced reactor vendors with a defined RIPB method to develop an affirmative safety case for licensing. In addition, the Technology Inclusive Content of Applications Project (TICAP) has published guidance on developing a license application based on the LMP approach. To support the utilization of risk information as part of advanced reactor design and licensing efforts, the American Society of Mechanical Engineers (ASME)/American Nuclear Society (ANS) Joint Committee on Nuclear Risk Management (JCNRM) has developed a probabilistic risk assessment (PRA) standard for advanced reactors. The standard, which was formerly approved by the American National Standards Institute (ANSI) in 2021 and recently endorsed by the NRC in trial use Regulatory Guide (RG) 1.247, is an integral standard, covering from initiating events to offsite consequence. A major feature of the standard is that it permits the inclusion of any source of radioactivity material at the site within the plant PRA. Therefore, non-core sources of radioactivity, such as fuel storage, fuel processing, and purification systems, can be included within a single comprehensive plant PRA. For those advanced reactor vendors utilizing a RIPB licensing approach, there is an opportunity to include the non-core sources of radioactivity within the RIPB framework for licensing decision-making, such as the categorization of events, classification of structures, systems, and components (SSCs), and evaluation of the adequacy of defense-in-depth (DID). For advanced reactor designs that contain multiple non-core sources of radioactivity, or for monolithic plant sites that include associated fuel facilities, this approach could potentially simplify licensing applications through the use of a single, uniform, and consistent decision-making framework across all radioactive sources at the site. In addition, a RIPB approach could provide additional insights regarding plant behavior, flexibility regarding licensing decision-making, and potentially allow the use of risk information as part of the plant oversight process. Risk-informing these aspects of advanced reactor licensing would also be consistent with the NRC’s risk policy statement. However, there is diverse regulation and guidance regarding the licensing of non-core sources of radioactivity and generally limited experience using RIPB approaches for the evaluation as part of licensing.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗