Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Policy”

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

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

At least 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Including operational aspects in the planning of power systems with large amounts of variable generation: A review of modeling approaches

In the past, power system planning was based on meeting the load duration curve at minimum cost. The increasing share of variable generation (VG) makes operational constraints more important in the planning problem, and there is more and more interest in considering aspects such as sufficient ramping capability, sufficient reserve procurement, power system stability, storage behavior, and the integration of other energy sectors often through demand response assets. In VG integration studies, several methods have been applied to combine the planning and operational timescales. We present a four-level categorization for the modeling methods, in order of increasing complexity: (1a) investment model only, (1b) operational model only, (2) unidirectionally soft-linked investment and operational models, (3a) bidirectionally soft-linked investment and operational models, (3b) operational model with an investment update algorithm, and (4) co-optimization of investments and operation. The review shows that using a low temporal resolution or only few representative days will not suffice in order to determine the optimal generation portfolio. In addition, considering operational effects proves to be important in order to get a more optimal generation portfolio and more realistic estimations of system costs. However, operational details appear to be less significant than the temporal representation. Furthermore, the benefits and impacts of more advanced modeling techniques on the resulting generation capacity mix significantly depend on the system properties. Thus, the choice of the model should depend on the purpose of the study as well as on system characteristics.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

A new scenario framework for climate change research. Background, process, and future directions

The scientific community is developing new integrated global, regional, and sectoral scenarios to facilitate interdisciplinary research and assessment to explore the range of possible future climates and related physical changes could pose to human and natural systems; how these could interact with social, economic, and environmental development pathways; the degree to which mitigation and adaptation policies can avoid and reduce those risks; the costs and benefits of various policy mixes; residual impacts under alternative pathways; and the relationship with sustainable development. This paper provides the background to, and process of, developing the conceptual framework for these scenarios, described in three other papers in this Special Issue (van Vuuren et al.; O'Neill et al.; Kriegler et al.). The paper also discusses research needs to further develop and apply this framework. The goal is to encourage climate change researchers from a broad range of perspectives and disciplines to work together to develop policy-relevant scenarios and explore the implications of different possible futures for the challenges and opportunities human and natural systems could face with increasing climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Non-Kyoto Radiative Forcing in Long-Run Greenhouse Gas Emissions and Climate Change Scenarios

Climate policies designed to achieve climate change objectives must consider radiative forcing from the Kyoto greenhouse gas, as well as other forcing constituents, such as aerosols and tropospheric ozone. Net positive forcing leads to global average temperature increases. Modeling of non-Kyoto forcing is a relatively new component of climate management scenarios. Five of the nineteen models in the EMF-27 Study model both Kyoto and non-Kyoto forcing. This paper describes and assesses current non-Kyoto radiative forcing modeling within these integrated assessment models. The study finds negative forcing from aerosols masking significant positive forcing in reference non-climate policy projections. There are however large differences across models in projected non-Kyoto emissions and forcing, with differences stemming from differences in relationships between Kyoto and non-Kyoto emissions and fundamental differences in modeling structure and assumptions. Air pollution and non-Kyoto forcing decline in the climate policy scenarios. However, non-Kyoto forcing appears to be influencing mitigation results, including allowable carbon dioxide emissions, and further evaluation is merited. Overall, there is substantial uncertainty related to non-Kyoto forcing that must be considered.

Rose, Steven K.↗
DOE OSTI2015

Overview of the Special Issue: A Multi-Model Framework to Achieve Consistent Evaluation of Climate Change Impacts in the United States

The Climate Change Impacts and Risk Analysis (CIRA) modeling exercise is a unique contribution to the scientific literature on climate change impacts, economic damages, and risk analysis that brings together multiple, national-scale models of impacts and damages in an integrated and consistent fashion to estimate climate change impacts, damages, and the benefits of greenhouse gas (GHG) mitigation actions in the United States. The CIRA project uses three consistent socioeconomic, emissions, and climate scenarios across all models to estimate the benefits of GHG mitigation policies: a Business As Usual (BAU) and two policy scenarios with radiative forcing (RF) stabilization targets of 4.5 W/m2 and 3.7 W/m2 in 2100. CIRA was also designed to specifically examine the sensitivity of results to uncertainties around climate sensitivity and differences in model structure. The goals of CIRA project are to 1) build a multi-model framework to produce estimates of multiple risks and impacts in the U.S., 2) determine to what degree risks and damages across sectors may be lowered from a BAU to policy scenarios, 3) evaluate key sources of uncertainty along the causal chain, and 4) provide information for multiple audiences and clearly communicate the risks and damages of climate change and the potential benefits of mitigation. This paper describes the motivations, goals, and design of the CIRA modeling exercise and introduces the subsequent papers in this special issue.

Climate Change Impacts↗
DOE OSTI2015

Accounting for radiative forcing from albedo change in future global land-use scenarios

We demonstrate the effectiveness of a new method for quantifying radiative forcing from land use and land cover change (LULCC) within an integrated assessment model, the Global Change Assessment Model (GCAM). The method relies on geographically differentiated estimates of radiative forcing from albedo change associated with major land cover transitions derived from the Community Earth System Model. We find that conversion of 1 km² of woody vegetation (forest and shrublands) to non-woody vegetation (crops and grassland) yields between 0 and –0.71 nW/m² of globally averaged radiative forcing determined by the vegetation characteristics, snow dynamics, and atmospheric radiation environment characteristic within each of 151 regions we consider globally. Across a set of scenarios designed to span a range of potential future LULCC, we find LULCC forcing ranging from –0.06 to –0.29 W/m² by 2070 depending on assumptions regarding future crop yield growth and whether climate policy favors afforestation or bioenergy crops. Inclusion of this previously uncounted forcing in the policy targets driving future climate mitigation efforts leads to changes in fossil fuel emissions on the order of 1.5 PgC/yr by 2070 for a climate forcing limit of 4.5 Wm –2 , corresponding to a 12–67 % change in fossil fuel emissions depending on the scenario. Scenarios with significant afforestation must compensate for albedo-induced warming through additional emissions reductions, and scenarios with significant deforestation need not mitigate as aggressively due to albedo-induced cooling. In all scenarios considered, inclusion of albedo forcing in policy targets increases forest and shrub cover globally.

Jones, Andrew D.↗
DOE OSTI2021

Observations of greenhouse gases as climate indicators

Humans have significantly altered the energy balance of the Earth’s climate system mainly not only by extracting and burning fossil fuels but also by altering the biosphere and using halocarbons. The 3rd US National Climate Assessment pointed to a need for a system of indicators of climate and global change based on long-term data that could be used to support assessments and this led to the development of the National Climate Indicators System (NCIS). Here we identify a representative set of key atmospheric indicators of changes in atmospheric radiative forcing due to greenhouse gases (GHGs), and we evaluate atmospheric composition measurements, including non-CO<:sub>2 GHGs for use as climate change indicators in support of the US National Climate Assessment. GHG abundances and their changes over time can provide valuable information on the success of climate mitigation policies, as well as insights into possible carbon-climate feedback processes that may ultimately affect the success of those policies. To ensure that reliable information for assessing GHG emission changes can be provided on policy-relevant scales, expanded observational efforts are needed. Furthermore, the ability to detect trends resulting from changing emissions requires a commitment to supporting long-term observations. Long-term measurements of greenhouse gases, aerosols, and clouds and related climate indicators used with a dimming/brightening index could provide a foundation for quantifying forcing and its attribution and reducing error in existing indicators that do not account for complicated cloud processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Transferring biodiversity-ecosystem function research to the management of ‘real-world’ ecosystems

Biodiversity-ecosystem functioning (BEF) research rose quickly following concerns that biodiversity loss would negatively affect ecosystem functions and the ecosystem services they underpin. However, despite evidence that biodiversity strongly affects ecosystem functioning, the influence of BEF research upon policy and the management of ‘real-world’ ecosystems, i.e., semi-natural habitats and agroecosystems, has been limited. In this work, we address this concern by classifying BEF research into three clusters based on the degree of human control over species composition and the spatial scale, in terms of grain, of the study, and discussing how the research of each cluster is best suited to inform particular fields of ecosystem management. Research in the first cluster, small-grain highly controlled studies, is best able to provide general insights into mechanisms and to inform the management of species-poor and highly managed systems such as croplands, plantations, and the restoration of heavily degraded ecosystems. Research from the second cluster, small-grain observational studies, and species removal and addition studies, may allow for direct predictions of the impacts of species loss in specific semi-natural ecosystems. Research in the third cluster, large-grain uncontrolled studies, may best inform landscape-scale management and national-scale policy. We discuss barriers to transfer within each cluster and suggest how new research and knowledge exchange mechanisms may overcome these challenges. To meet the potential for BEF research to address global challenges, we recommend transdisciplinary research that goes beyond these current clusters and considers the social-ecological context of the ecosystems in which BEF knowledge is generated. This requires recognizing the social and economic value of biodiversity for ecosystem services at scales, and in units, that matter to land managers and policy makers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Tables of E2 transition probabilities from the first 2 + states in even–even nuclei

Highlights: • Compilation of experimental transition probabilities or B(E2) values for the known first 2{sup +} states in even–even nuclei. • Evaluation of B(E2) values, lifetimes and deformation parameters for 447 even–even nuclei. • Extensive discussion on data evaluation policies and procedures. • Nuclear shell model calculation of the first 2{sup +} states energies and B(E2) values in even–even nuclei. Experimental results of E2 transition probabilities or B(E2) values for the known first 2{sup +} states in 447 even–even nuclei have been compiled and evaluated. The evaluation policies for the analysis of experimental data have been described and new results are discussed. The recommended B(E2) values have been compared with comprehensive shell model calculations for a selected set of nuclei, where such theoretical procedures are amenable. The present work was motivated by a rapid increase in the number of new B(E2) measurements for the first 2{sup +} states since the previous evaluation of such data by S. Raman et al. published in 2001. Future plans to investigate the systematics of B(E2)↑ values, and intercomparison of different experimental techniques to obtain these data are outlined.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

A phase model approach for thermostatically controlled load demand response

A significant portion of electricity consumed worldwide is used to power thermostatically controlled loads (TCLs) such as air conditioners, refrigerators, and water heaters. Because the short-term timing of operation of such systems is inconsequential as long as their long-run average power consumption is maintained, they are increasingly used in demand response (DR) programs to balance supply and demand on the power grid. Here, we present an ab initio phase model for general TCLs, and use the concept to develop a continuous oscillator model of a TCL and compute its phase response to changes in temperature and applied power. This yields a simple control system model that can be used to evaluate control policies for modulating the power consumption of aggregated loads with parameter heterogeneity and stochastic drift. We demonstrate this concept by comparing simulations of ensembles of heterogeneous loads using the continuous state model and an established hybrid state model. The developed phase model approach is a novel means of evaluating DR provision using TCLs, and is instrumental in estimating the capacity of ancillary services or DR on different time scales. We further propose a novel phase response based open-loop control policy that effectively modulates the aggregate power of a heterogeneous TCL population while maintaining load diversity and minimizing power overshoots. This is demonstrated by low-error tracking of a regulation signal by filtering it into frequency bands and using TCL sub-ensembles with duty cycles in corresponding ranges. As a result, control policies that can maintain a uniform distribution of power consumption by aggregated heterogeneous loads will enable distribution system management (DSM) approaches that maintain stability as well as power quality, and further allow more integration of renewable energy sources.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Reinforcement learning based automated history matching for improved hydrocarbon production forecast

History matching aims to find a numerical reservoir model that can be used to predict the reservoir performance. An engineer and model calibration (data inversion) method are required to adjust various parameters/properties of the numerical model in order to match the reservoir production history. In this study, we develop deep neural networks within the reinforcement learning framework to achieve automated history matching that will reduce engineers’ efforts, human bias, automatically and intelligently explore the parameter space, and remove the need of large set of labeled training data. To that end, a fast-marching-based reservoir simulator is encapsulated as an environment for the proposed reinforcement learning. The deep neural-network-based learning agent interacts with the reservoir simulator within reinforcement learning framework to achieve the automated history matching. Reinforcement learning techniques, such as discrete Deep Q Network and continuous Deep Deterministic Policy Gradients, are used toth, used to train the learning agents. The continuous actions enable the Deep Deterministic Policy Gradients to explore more states at each iteration in a a learning episode; consequently, a better history matching is achieved using this algorithm as compared to Deep Q Network. For simplified dual-target composite reservoir models, the best history-matching performances of the discrete and continuous learning methods in terms of normalized root mean square errors are 0.0447 and 0.0038, respectively. Furthermore, our study shows that continuous action space achieved by the deep deterministic policy gradient drastically outperforms deep Q network.

42 ENGINEERING↗
DOE OSTI2013

New Directions: GEIA’s 2020 Vision for Better Air Emissions Information

We are witnessing a crucial change in how we quantify and understand emissions of greenhouse gases and air pollutants, with an increasing demand for science-based transparent emissions information produced by robust community efforts. Today’s scientific capabilities, with near-real-time in-situ and remote sensing observations combined with forward and inverse models and a better understanding of the controlling processes, are contributing to this transformation and providing new approaches to derive, verify, and forecast emissions (Tong et al., 2011; Frost et al., 2012) and to quantify their impacts on the environment (e.g., Bond et al., 2013). At the same time, the needs for emissions information and the demands for their accuracy and consistency have grown. Changing economies, demographics, agricultural practices, and energy sources, along with mandates to evaluate emissions mitigation efforts, demonstrate compliance with legislation, and verify treaties, are leading to new challenges in emissions understanding. To quote NOAA Senior Technical Scientist David Fahey, "We are in the Century of Accountability. Emissions information is critical not only for environmental science and decision-making, but also as an instrument of foreign policy and international diplomacy." Emissions quantification represents a key step in explaining observed variability and trends in atmospheric composition and in attributing these observed changes to their causes. Accurate emissions data are necessary to identify feasible controls that reduce adverse impacts associated with air quality and climate and to track the success of implemented policies. To progress further, the international community must improve the understanding of drivers and contributing factors to emissions, and it must strengthen connections among and within different scientific disciplines that characterize our environment and entities that protect the environment and influence further emissions. The Global Emissions InitiAtive, GEIA (http://www.geiacenter.org/), is a center for emissions information exchange and competence building created in 1990 in response to the need for high quality global emissions data (Graedel et al., 1993). While the past two decades have seen considerable progress in developing, improving and assessing emission estimates, emissions continue to be a major contributor to overall uncertainty in atmospheric model simulations. Moving forward, GEIA aims to help build emissions knowledge in a rapidly evolving society by: 1) enhancing understanding, quantification, and analysis of emissions processes; 2) improving access to emissions information; and 3) strengthening the community of emissions groups involved in research, assessment, operations, regulation and policy.

Frost, G. J.↗
DOE OSTI2021

Deep reinforcement learning control of hydraulic fracturing

Hydraulic fracturing is a technique to extract oil and gas from shale formations, and obtaining a uniform proppant concentration along the fracture is key to its productivity. Recently, various model predictive control schemes have been proposed to achieve this objective. But such controllers require an accurate and computationally efficient model which is difficult to obtain given the complexity of the process and uncertainties in the rock formation properties. In this article, we design a model-free data-based reinforcement learning controller which learns an optimal control policy through interactions with the process. Deep reinforcement learning (DRL) controller is based on the Deep Deterministic Policy Gradient algorithm that combines Deep-Q-network with actor-critic framework. In addition, we utilize dimensionality reduction and transfer learning to quicken the learning process. We show that the controller learns an optimal policy to obtain uniform proppant concentration despite the complex nature of the process while satisfying various input constraints.

42 ENGINEERING↗
DOE OSTI2013

A Dynamic Simulation of the Indirect Land Use Implications of Recent Biofuel Production and Use in the United States.

The global indirect land use change (ILUC) implications of biofuel use in the United States of America (USA) from 2001 to 2010 are evaluated with a dynamic general equilibrium model. The effects of biofuels production on agricultural land area vary by year; from a net expansion of 0.17 ha per 1000 gallons produced (2002) to a net contraction of -0.13 ha per 1000 gallons (2018) in Case 1 of our simulation. In accordance with the general narrative about the implications of biofuel policy, agricultural land area increased in many regions of the world. However, oil-export dependent economies experienced agricultural land contraction because of reductions in their revenues. Reducing crude oil imports is a major goal of biofuel policy, but the land use change implications have received little attention in the literature. Simulations evaluating the effects of doubling supply elasticities for land and fossil resources show that these parameters can significantly influence the land use change estimates. Therefore, research that provides empirically-based and spatially-detailed agricultural land-supply curves and capability to project future fossil energy prices is critical for improving estimates of the effects of biofuel policy on land use.

Oladosu, Gbadebo A.↗
DOE OSTI2015

China's transportation energy consumption and CO2 emissions from a global perspective

ABSTRACT Rapidly growing energy demand from China's transportation sector in the last two decades have raised concerns over national energy security, local air pollution, and carbon dioxide (CO2) emissions, and there is broad consensus that China's transportation sector will continue to grow in the coming decades. This paper explores the future development of China's transportation sector in terms of service demands, final energy consumption, and CO2 emissions, and their interactions with global climate policy. This study develops a detailed China transportation energy model that is nested in an integrated assessment model—Global Change Assessment Model (GCAM)—to evaluate the long-term energy consumption and CO2 emissions of China's transportation sector from a global perspective. The analysis suggests that, without major policy intervention, future transportation energy consumption and CO2 emissions will continue to rapidly increase and the transportation sector will remain heavily reliant on fossil fuels. Although carbon price policies may significantly reduce the sector's energy consumption and CO2 emissions, the associated changes in service demands and modal split will be modest, particularly in the passenger transport sector. The analysis also suggests that it is more difficult to decarbonize the transportation sector than other sectors of the economy, primarily owing to its heavy reliance on petroleum products.

Yin, Xiang↗
DOE OSTI2015

Electricity market design for generator revenue sufficiency with increased variable generation

Here, we present a computationally efficient mixed-integer program (MIP) that determines optimal generator expansion decisions, and hourly unit commitment and dispatch in a power system. The impact of increasing wind power capacity on the optimal generation mix and generator profitability is analyzed for a test case that approximates the electricity market in Texas (ERCOT). We analyze three market policies that may support resource adequacy: Operating Reserve Demand Curves (ORDC), Fixed Reserve Scarcity Prices (FRSP) and fixed capacity payments (CP). Optimal expansion plans are comparable between the ORDC and FRSP implementations, while capacity payments may result in additional new capacity. The FRSP policy leads to frequent reserves scarcity events and corresponding price spikes, while the ORDC implementation results in more continuous energy prices. Average energy prices decrease with increasing wind penetration under all policies, as do revenues for baseload and wind generators. Intermediate and peak load plants benefit from higher reserve prices and are less exposed to reduced energy prices. All else equal, an ORDC approach may be preferred to FRSP as it results in similar expansion and revenues with less extreme energy prices. A fixed CP leads to additional new flexible NGCT units, but lower profits for other technologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
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 OSTI2014

Perception, attitude and behavior in relation to climate change: A survey among CDC health professionals in Shanxi province, China

Background: A better understanding of public perceptions, attitude and behavior in relation to climate change will provide an important foundation for government's policy-making, service provider's guideline development and the engagement of local communities. The purpose of this study was to assess the perception towards climate change, behavior change, mitigation and adaptation measures issued by the central government among the health professionals in the Centres for Disease Control and Prevention (CDC) in China. Methods: In 2013, a cross-sectional questionnaire survey was undertaken among 314 CDC health professionals in various levels of CDC in Shanxi Province, China. Descriptive analyses were performed. Results: More than two thirds of the respondents believed that climate change has happened at both global and local levels, and climate change would lead to adverse impacts to human beings. Most respondents (74.8%) indicated the emission of greenhouse gases was the cause of climate change, however there was a lack of knowledge about greenhouse gases and their sources. Media was the main source from which respondents obtained the information about climate change. A majority of respondents showed that they were willing to change behavior, but their actions were limited. In terms of mitigation and adaptation measures issued by the Chinese Government, respondents' perception showed inconsistency between strategies and relevant actions. Moreover, although the majority of respondents believed some strategies and measures were extremely important to address climate change, they were still concerned about economic development, energy security, and local environmental protection. Conclusion: There are gaps between perceptions and actions towards climate change among these health professionals. Further efforts need to be made to raise the awareness of climate change among health professionals, and to promote relevant actions to address climate change in the context of the proposed policies with local sustainable development. - Highlights: • Global climate change has significant impacts on human wellbeing and health. • Health professionals play a significant role in improving the health of local citizens in China. • Perceptions of CDC staff on climate change are useful for policy making. • There are gaps between perceptions and actions among these health professionals in China. • Further efforts need to raise awareness of climate change and promote relevant actions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

On the origins of the linear no-threshold (LNT) dogma by means of untruths, artful dodges and blind faith

This paper is an historical assessment of how prominent radiation geneticists in the United States during the 1940s and 1950s successfully worked to build acceptance for the linear no-threshold (LNT) dose–response model in risk assessment, significantly impacting environmental, occupational and medical exposure standards and practices to the present time. Detailed documentation indicates that actions taken in support of this policy revolution were ideologically driven and deliberately and deceptively misleading; that scientific records were artfully misrepresented; and that people and organizations in positions of public trust failed to perform the duties expected of them. Key activities are described and the roles of specific individuals are documented. These actions culminated in a 1956 report by a Genetics Panel of the U.S. National Academy of Sciences (NAS) on Biological Effects of Atomic Radiation (BEAR). In this report the Genetics Panel recommended that a linear dose response model be adopted for the purpose of risk assessment, a recommendation that was rapidly and widely promulgated. The paper argues that current international cancer risk assessment policies are based on fraudulent actions of the U.S. NAS BEAR I Committee, Genetics Panel and on the uncritical, unquestioning and blind-faith acceptance by regulatory agencies and the scientific community. - Highlights: • The 1956 recommendation of the US NAS to use the LNT for risk assessment was adopted worldwide. • This recommendation is based on a falsification of the research record and represents scientific misconduct. • The record misrepresented the magnitude of panelist disagreement of genetic risk from radiation. • These actions enhanced public acceptance of their risk assessment policy recommendations.

54 ENVIRONMENTAL SCIENCES↗