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At least 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Neural network approaches for parameterized optimal control

Here, we consider numerical approaches for deterministic, finite-dimensional optimal control problems whose dynamics depend on unknown or uncertain parameters. We seek to amortize the solution over a set of relevant parameters in an offline stage to enable rapid decision-making and be able to react to changes in the parameter in the online stage. To tackle the curse of dimensionality arising when the state and/or parameter are high-dimensional, we represent the policy using neural networks. We compare two training paradigms: First, our model-based approach leverages the dynamics and definition of the objective function to learn the value function of the parameterized optimal control problem and obtain the policy using a feedback form. Second, we use actor-critic reinforcement learning to approximate the policy in a data-driven way. Using an example involving a two-dimensional convection-diffusion equation, which features high-dimensional state and parameter spaces, we investigate the accuracy and efficiency of both training paradigms. While both paradigms lead to a reasonable approximation of the policy, the model-based approach is more accurate and considerably reduces the number of PDE solves.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Evolution of NASA’s Nuclear Flight Safety Program to Meet Changing Needs

Over the past 4 years, the United States (U.S.) Government has issued several new National policies that fundamentally change the approach to nuclear flight safety for aerospace applications, including the complete revision of the Federal policy for handling launch of spacecraft containing space nuclear systems. In response, the National Aeronautics and Space Administration (NASA) is updating its nuclear flight safety program while still maintaining consistency with other Federal policies, international conventions, and NASA’s own policies. To achieve this evolution, NASA is factoring in an objectives-driven and assurance case mindset to develop a risk-informed and performance-based program. NASA and others have successfully applied this mindset in other disciplines and contexts and it is being pursued here via broad cooperation within NASA and with external stakeholders. This paper will briefly describe how the NASA nuclear flight safety program is evolving to meet these changing needs.

NASA↗
NASA NTRSDate not supplied

Identifying Outside Influences as Latent Factors to Risk in Human Performance

During the Acquisition Life Cycle for a program, there are several opportunities for the system in design to be adjusted in accordance with its changing landscape as it is being shaped by evolving policy and organizational culture. Human Systems Integration is integral to identifying these opportunities of change as there are a set number of activities that may account for altered operational states, human performance deviations, and overall component engagement if HSI is enacted early enough in the life cycle. Some changes that occur in the operational environment may not be accounted for since the organizational culture and practices are not currently a part of the HSI focus. Likewise, policy changes themselves from a top-to-bottom analysis may not have the appearance of effecting human performance until having gone through a trail-and-error period. Workarounds to adjust for unforeseen policy affects become the system's solution that usually includes changes in the training and education of the operators, maintainers, and support personnel. A system full of workarounds and off-normal practices, that cause operators to disregard the purpose of the design, coupled with the false notion that these activities are proven for successful system operation, is the very definition of “an accident waiting to happen”. Unforeseen changes in policies and practices that cause new and unusual activities to successfully and keep the system running, should be considered latent factors that may cause a potential mishap, and not part of the resilience that humans provide to the successful operation of the system. This presentation will explore how latent factors may find their way into system operations and how they can be identified and addressed.

Human Systems Integration↗
DOE OSTI2015

Biomass Scenario Model: BETO Analysis Platform Peer Review; NREL (National Renewable Energy Laboratory)

The Biomass Scenario Model (BSM) is a unique, carefully validated, state-of-the-art fourth-generation model of the domestic bioenergy supply chain which explicitly focuses on policy issues and their potential side effects. It integrates resource availability, behavior, policy, and physical, technological, and economic constraints. The BSM uses system-dynamics simulation to model dynamic interactions across the supply chain; it tracks the deployment of biofuels given technological development and the reaction of the investment community to those technologies in the context of land availability, the competing oil market, consumer demand for biofuels, and government policies over time. It places a strong emphasis on the behavior and decision-making of various economic agents. The model treats the major infrastructure-compatible fuels. Scenario analysis based on the BSM shows that the biofuels industry tends not to rapidly thrive without significant external actions in the early years of its evolution. An initial focus for jumpstarting the industry typically has strongest results in the BSM in areas where effects of intervention have been identified to be multiplicative. In general, we find that policies which are coordinated across the whole supply chain have significant impact in fostering the growth of the biofuels industry and that the production of tens of billions of gallons of biofuels may occur under sufficiently favorable conditions.

09 BIOMASS FUELS↗
DOE OSTI2015

Remaking the US nuclear waste program: a window of opportunity for change? - 15352

John Kingdon's 1984 policy analytic approach is utilized to examine nuclear waste policy in the United States over the last three decades. Kingdon's premise concerning the merging of three process streams - the problem stream, the political stream, and the policy stream - to create a policy window, took place when major changes in U.S. management of high-level radioactive waste and spent nuclear fuel occurred in 1982 and 1987. These three streams have once again converged, with events beginning in 2006, creating an opportunity to move beyond the current Yucca Mountain stalemate by adopting the recommendations of the Blue Ribbon Commission (BRC) on America's Nuclear Future in 2012. Legislation introduced in the U.S. Senate in 2013 to redirect the waste program along the lines suggested by the BRC has been reintroduced as the Nuclear Waste Administration Act of 2015, S. 854. The authors examine key provisions of S. 854 and suggest amendments requiring written consent agreements with governors of all potential host states, including Nevada; the same requirements for commingled and separate defense waste facilities; and additional measures to enhance nuclear waste transportation safety, security, and public acceptance. The Nuclear Waste Informed Consent Act (S. 691, 2015; H.R. 1364, 2015) is also examined. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Reinforcement learning for real-time process control in high-temperature superconductor manufacturing

With high efficiency and low energy loss, high-temperature superconductors (HTS) have demonstrated their profound applications in various fields, such as medical imaging, transportation, accelerators, microwave devices, and power systems. The high-field applications of HTS tapes have raised the demand for producing cost-effective tapes with long lengths in superconductor manufacturing. However, achieving the uniform and enhanced performance of a long HTS tape is challenging due to the unstable growth conditions in the manufacturing process. Although it is confirmed that the process parameters during the advanced metal organic chemical vapor deposition (A-MOCVD) process influence the uniformity of the produced HTS tapes, the high-dimensional process parameter signals and their complicated interactions make it difficult to develop an effective control policy. In this paper, we propose a local measure for the uniformity of HTS tapes to provide instant feedback for our control policy. Then, we model the manufacturing of HTS tapes as a Markov decision process (MDP) with continuous state and action spaces to assess the instant reward in real time in our feedback control model. As our MDP involves continuous and high-dimensional state and action spaces, a neural fitted Q-iteration (NFQ) algorithm is adopted to solve the MDP with artificial neural network (ANN) function approximation. The collinearity of process parameters can restrict our capability of adjusting the process parameters, which is addressed by the principal component analysis (PCA) in our method. The control policy adjusts the PCA of process parameters using the NFQ algorithm. In conclusion, based on our case studies on real A-MOCVD dataset, the obtained control policy increases the average uniformity of tapes by 5.6% and performs especially well on sample HTS tapes with a low uniformity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Efficient learning of power grid voltage control strategies via model-based deep reinforcement learning

Here this article proposes a model-based deep reinforcement learning (DRL) method to design emergency control strategies for short-term voltage stability problems in power systems. Recent advances show promising results for model-free DRL-based methods in power systems control problems. But in power systems applications, these model-free methods have certain issues related to training time (clock time) and sample efficiency; both are critical for making state-of-the-art DRL algorithms practically applicable. DRL-agent learns an optimal policy via a trial-and-error method while interacting with the real-world environment. It is also desirable to minimize the direct interaction of the DRL agent with the real-world power grid due to its safety-critical nature. Additionally, the state-of-the-art DRL-based policies are mostly trained using a physics-based grid simulator where dynamic simulation is computationally intensive, lowering the training efficiency. We propose a novel model-based DRL framework where a deep neural network (DNN)-based dynamic surrogate model (SM), instead of a real-world power grid or physics-based simulation, is utilized within the policy learning framework, making the process faster and more sample efficient. However, having stable training in model-based DRL is challenging because of the complex system dynamics of large-scale power systems. We addressed these issues by incorporating imitation learning to have a warm start in policy learning, reward-shaping, and multi-step loss in surrogate model training. Finally, we achieved 97.5% reduction in samples and 87.7% reduction in training time for an application to the IEEE 300-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

The impact of development priorities on power system expansion planning in sub-Saharan Africa

Sub-Saharan Africa faces unique barriers to electricity development due to the large proportion of the population that is un-electrified and the prevalence of rural populations. Typically, power system expansion planning models assume all potential consumers can be immediately electrified. This assumption is unrealistic in sub-Saharan Africa, where electrification will likely be a gradual process over a number of years. Furthermore, since a large proportion of the population in sub-Saharan Africa is located in rural regions, the prioritization of these regions may impact how the grid develops. In this research, we develop a multi-period optimization model for power generation and transmission system expansion planning in sub-Saharan Africa. In contrast to existing models, which assume full electrification, we consider a variety of electrification policies and analyze the impact of varying the electrification rate and policy on the cost and resources selected for power system expansion. We test our model on a case study of Rwanda. We find that varying the year in which full electrification is reached has a larger impact on cost and generation capacity than varying the electrification policy does, although, when urban and rural regions are considered equitably, more rooftop solar is built. Varying the electrification policies has a larger impact on transmission expansion than on generation expansion and this impact is amplified when starting from zero initial system capacity rather than the original Rwanda system. Additionally, a sensitivity analysis shows that tightening the bounds on CO 2eq emissions has a large impact on the generation portfolio and cost.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Comprehensive assessment of deep reinforcement learning approaches for economic dispatch in nuclear-driven microgrids

As the electrical grid integrates more variable renewable energy sources such as wind and solar, the demand for distributed and flexible systems to address this increased variability becomes critical. Nuclear-driven microgrids provide a promising solution by offering stable generation to complement intermittent renewables, ensuring grid reliability and operating efficiency. This paper proposes a recurrent deep reinforcement learning framework for optimal economic dispatch in a nuclear-powered microgrid integrating renewable energy sources, small modular reactors, battery storage systems, and balance-of-plant dynamics. A three-agent control architecture is developed, where demand and renewable energy agents act as forecasters, and a reinforcement learning-based dispatch agent performs real-time energy allocation. A nonlinear programming formulation is first used to generate an optimal baseline for benchmarking. The proposed dispatch controller, based on Proximal Policy Optimization enhanced with Long Short-Term Memory networks, exploits temporal correlations in system dynamics by taking advantage of the time series used as inputs to improve policy robustness under uncertainty. Comparative analysis against established deep reinforcement learning methods, including Proximal Policy Optimization with a feedforward architecture, Soft Actor-Critic, and Twin Delayed Deep Deterministic Policy Gradient, demonstrates superior performance. Numerical results indicate that the proposed controller achieves a 0.39% cost reduction relative to the nonlinear programming benchmark and outperforms other learning-based methods by generating additional revenue of up to 0.35%. All reinforcement learning controllers compute dispatch actions in less than 0.3 s, resulting in a computational speedup of more than three orders of magnitude over the nonlinear programming baseline. The findings of this paper highlight their applicability for real-time operation and control in nuclear-integrated microgrids under volatile operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Reinforcement learning building control approach harnessing imitation learning

Reinforcement learning (RL) has shown significant success in sequential decision making in fields like autonomous vehicles, robotics, marketing and gaming industries. This success has attracted the attention to the RL control approach for building energy systems which are becoming complicated due to the need to optimize for multiple, potentially conflicting, goals like occupant comfort, energy use and grid interactivity. However, for real world applications, RL has several drawbacks like requiring large training data and time, and unstable control behavior during the early exploration process making it infeasible for an application directly to building control tasks. To address these issues, an imitation learning approach is utilized herein where the RL agents starts with a policy transferred from accepted rule based policies and heuristic policies. This approach is successful in reducing the training time, preventing the unstable early exploration behavior and improving upon an accepted rule-based policy - all of these make RL a more practical control approach for real world applications in the domain of building controls.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Inverse reinforcement learning control for building energy management

Reinforcement learning (RL) based control is widely considered a promising approach in building automation and control as it has demonstrated the potential to deal with complex objectives in adjacent domains like robotics, autonomous vehicles, gaming applications, and advertisement recommendations. When applied to any environment, model-free RL learns to improve its control performance over time without requiring a control model, by receiving and then analyzing feedback from the building environment after each control action. Operational objectives are becoming increasingly complex through the simultaneous consideration of thermal comfort, carbon emissions, grid services, and indoor air quality. In this context, conventional rule-based control approaches are proving sub-optimal, mostly heuristic, and inadequate. The model-free and self learning nature of RL appears promising and attractive as it may address the scalability issues associated with advanced control approaches. However, it suffers from long training times and unstable control behavior during the early stages of its learning process, which makes it unsuitable to be applied directly to buildings. This paper addresses these issues using an inverse reinforcement learning approach (IRL), a technique utilized to learn the objective of a controller agent which is considered an expert in its respective domain. Here, we consider a rule-based control as the expert demonstrator. IRL is different from a direct imitation (i.e., direct mapping of states to actions) of control actions as it tries to find the underlying intent of an expert's policy, providing the controller with a better-generalized policy for unseen states or environments with slightly different dynamics. This approach propels the RL controller's policy to levels similar to or better than that of a rule-based policy before it starts learning by interacting with the building. This makes RL for building energy management applications more practical as it prevents the erratic and exploratory behavior in the initial training period, simultaneously speeding up the learning process when compared to applying an untrained RL agent directly to a building environment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Estimated attribution of the RFS program on soybean biodiesel in the U.S. using the bioenergy scenario model

Biofuels are supported by various governmental policies in the U.S. and globally as an alternative transportation fuel for environmental, geopolitical, and economic reasons. Much debate surrounds the effectiveness of these policies as well as the overall net environmental effect of increased biofuels use. In the U.S., recent studies have shown that the Renewable Fuels Standard (RFS) Program, overall, may not have been the leading driver of the ethanol industry from 2005 to 2020, contrary to common perception. Similar scrutiny has not been applied to biodiesel. Here, this study uses the Bioenergy Scenario Model, a well-vetted system dynamics model, to retrospectively apportion historical biodiesel production between the RFS Program and other potentially influential drivers, such as the economics of biodiesel vs. diesel, the Biodiesel Tax Credit (BTC), California's Low Carbon Fuel Standard, and other factors. From 2002 to 2020 about 36% of biodiesel production can be attributed to the RFS Program, 35% to the BTC, and the rest to other factors. Thus, the overall effect of the RFS Program appears much larger on biodiesel than on corn ethanol. The finding that the same policy may have very disparate effects on different biofuels helps inform the design of future policies on biofuels.

09 BIOMASS FUELS↗
DOE OSTI2017

The role of bioenergy in a climate-changing world

Bioenergy has been under intense scrutiny over the last ten years with significant research efforts in many countries taking place to define and measure sustainable practices. We describe here the main challenges and policy issues and provide policy recommendations for scaling up sustainable bioenergy approaches globally. The 2016 Intended Nationally Determined Contributions (INDCs defined under the UN Framework Convention on Climate Change) (UNFCCC) Conference of the Parties (COP21) will not reach global Greenhouse Gas (GHG) emission targets of 2 °C. Sustainable biomass production can make a significant contribution. Substantive evidence exists that many bioenergy cropping systems can bring multiple benefits and off-set environmental problems associated with fossil fuels usage as well as intensive food production and urbanization. We provide evidence that there are many approaches to land use for bioenergy expansion that do not lead to competition for food or other needs. We should focus on how to manage these approaches on a synergistic basis and how to reduce tradeoffs at landscape scales. Priorities include successful synergies between bioenergy and food security (integrated resource management designed to improve both food security and access to bioenergy), investments in technology, rural extension, and innovations that build capacity and infrastructure, promotion of stable prices to incentivize local production and use of double cropping and flex crops (plants grown for both food and non-food markets) that provide food and energy as well as other services. The sustainable production of biomass requires appropriate policies to secure long-term support to improve crop productivity and also to ensure environmental as well as economic and social benefits of bioenergy cropping systems. Continuous support for cropping, infrastructure, agricultural management and related policies is needed to foster positive synergies between food crops and bioenergy production. In comparison to fossil fuels, biofuels have many positive environmental benefits. Potential negative effects caused by land-use change and agriculture intensification can be mitigated by agroecological zoning, best management practices, the use of eco-hydrology and biodiversity-friendly concepts at field, watershed and landscape scales. Global climate and environmental changes related to the use of fossil fuels and inequitable development make it unethical not to pursue more equitable energy development that includes bioenergy. Here, to achieve sustainable development, competitiveness and costs of bioenergy production need to be addressed in a manner that considers not only economic gains but also development of local knowledge and social and environmental benefits.

09 BIOMASS FUELS↗
DOE OSTI2017

The impact of climate change and emissions control on future ozone levels: Implications for human health

Overwhelming evidence has shown that, from the Industrial Revolution to the present, human activities influence ground-level ozone (O 3 ) concentrations. Past studies demonstrate links between O 3 exposure and health. However, knowledge gaps remain in our understanding concerning the impacts of climate change mitigation policies on O 3 concentrations and health. Using a hybrid downscaling approach, we evaluated the separate impact of climate change and emission control policies on O 3 levels and associated excess mortality in the US in the 2050s under two Representative Concentration Pathways (RCPs). We show that, by the 2050s, under RCP4.5, increased O 3 levels due to combined climate change and emission control policies, could contribute to an increase of approximately 50 premature deaths annually nationwide in the US. The biggest impact, however, is seen under RCP8.5, where rises in O 3 concentrations are expected to result in over 2,200 additional premature deaths annually. Here, the largest increases in O 3 are seen in RCP8.5 in the Northeast, the Southeast, the Central, and the West regions of the US. Additionally, when O 3 increases are examined by climate change and emissions contributions separately, the benefits of emissions mitigation efforts may significantly outweigh the effects of climate change mitigation policies on O 3 -related mortality.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Transition in source contributions of PM2.5 exposure and associated premature mortality in China during 2005–2015

The serious fine particle (PM2.5) pollution in China causes millions of premature deaths. Driven by swift eco-nomic growth and stringent control policies, air pollutant emissions in China have changed signi?cantly in the last decade, but the change in the source contribution of PM2.5-related health impacts remains unclear. In this study, we develop a multi-pollutant emission inventory in China for 2005–2015, and combine chemical transport modeling, ambient/household exposure evaluation and health impact assessment to quantify the contribution of eight emission sectors to PM2.5 exposure and associated health risk. From 2005 to 2015, the mortality due to PM2.5 from ambient air pollution (AAP) decreases from 1.04 (95% con?dence interval, 0.84–1.25) million to 0.87 (0.70–1.04) million. The agricultural sector contributes 25% and 32% to ambient PM2.5-attributed mor-tality in 2005 and 2015, respectively, representing the largest contributor during this period. The contribution of power plants drops monotonously from 13% to 6%. The percentage contribution of industrial process drops signi?cantly while the contribution of industrial combustion stays the same level. The overall contribution of industry is still as large as 26% in 2015 in spite of strict control measures. For transportation, despite strict emission standards, its contribution increases remarkably due to the rapid growth of vehicle population. When both ambient and household PM2.5 exposures are taken into account, the mortality due to integrated population-weighted exposure to PM2.5 (IPWE) drops from 1.78 (1.46–2.09) million in 2005 to 1.28 (1.05–1.52) million in 2015. Most of the IPWE reduction comes from domestic combustion as a result of urbanization and improved income, whereas this sector remains the largest contributor (58%) to IPWE-related health risk in 2015. Our results suggest that the government should dynamically adjust the air pollution control strategy according to the change in source contributions. Domestic combustion and agriculture should be prioritized considering their predominant contributions to mortality and the lack of e?ective control policies. More stringent control mea-sures for industry and transportation are necessary since the existing policies have not adequately reduced their health impacts. Electricity production is no longer the top priority of air pollution control policies given its lower health impact compared with that of other sources.

Zheng, Haotian↗
DOE OSTI2022

A Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flow

With the increasing penetration of distributed energy resources, distributed optimization algorithms have attracted significant attention for power systems applications due to their potential for superior scalability, privacy, and robustness to a single point-of-failure. The Alternating Direction Method of Multipliers (ADMM) is a popular distributed optimization algorithm; however, its convergence performance is highly dependent on the selection of penalty parameters, which are usually chosen heuristically. In this work, we use reinforcement learning (RL) to develop an adaptive penalty parameter selection policy for alternating current optimal power flow (ACOPF) problem solved via ADMM with the goal of minimizing the number of iterations until convergence. We train our RL policy using deep Q-learning and show that this policy can result in significantly accelerated convergence (up to a 59% reduction in the number of iterations compared to existing, curvatureinformed penalty parameter selection methods). Furthermore, we show that our RL policy demonstrates promise for generalizability, performing well under unseen loading schemes as well as under unseen losses of lines and generators (up to a 50% reduction in iterations). This work thus provides a proof-of-concept for using RL for parameter selection in ADMM for power systems applications.

alternating current optimal power flow↗
DOE OSTI2014

The contribution of future agricultural trends in the US Midwest to global climate change mitigation

Land use change is a complex response to changing environmental and socioeconomic systems. Historical drivers of land use change include changes in the natural resource availability of a region, changes in economic conditions for production of certain products and changing policies. Most recently, introduction of policy incentives for biofuel production have influenced land use change in the US Midwest, leading to concerns that bioenergy production systems may compete with food production and land conservation. Here we explore how land use may be impacted by future climate mitigation measures by nesting a high resolution agricultural model (EPIC – Environmental Policy Indicator Climate) for the US Midwest within a global integrated assessment model (GCAM – Global Change Assessment Model). This approach is designed to provide greater spatial resolution and detailed agricultural practice information by focusing on the climate mitigation potential of agriculture and land use in a specific region, while retaining the global economic context necessary to understand the far ranging effects of climate mitigation targets. We find that until the simulated carbon prices are very high, the US Midwest has a comparative advantage in producing traditional food and feed crops over bioenergy crops. Overall, the model responds to multiple pressures by adopting a mix of future responses. We also find that the GCAM model is capable of simulations at multiple spatial scales and agricultural technology resolution, which provides the capability to examine regional response to global policy and economic conditions in the context of climate mitigation.

Thomson, Allison M.↗