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At least 343 records · Page 19

All sources NASA NTRS DOE OSTI

DOE OSTI2022

How to Build a Connected Community: Policies to Promote Grid-interactive Efficient Buildings and Demand Flexibility

Efficient, connected, grid-interactive, smart, and flexible buildings are key to decarbonizing the U.S. energy economy, optimizing energy use, reducing electric consumers’ bills, integrating variable renewable energy resources, and improving the reliability and performance of the nation’s electricity grids. Such grid-interactive efficient buildings have high levels of energy efficiency layered with other distributed energy resources (DERs) and intelligent controls to provide demand flexibility. Policy support is unfolding at the federal, state, and local levels to transform homes and workplaces into state-of-the-art energy-efficient buildings and community-level grid services. This paper starts by describing the potential benefits. Next, it highlights existing policies — with a focus on state-level actions — that support grid-interactive efficient building deployment and demand flexibility. Finally, it identifies current trends and gaps, policies and programs that promote grid-interactive efficient buildings, and aggregations of grid-interactive efficient buildings referred to as Virtual Power Plants.

Schwartz, Lisa C↗

DOE OSTI2022

Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery

We developed Distilled Graph Attention Policy Networks (DGAPNs), a curiosity-driven reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention Network (sGAT) that leverages self-attention over both node and edge attributes as well as encoding spatial structure -- this capability is of considerable interest in areas such as molecular and synthetic biology and drug discovery. An attentional policy network is then introduced to learn decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with enhanced stability. Exploration is efficiently encouraged by incorporating innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while increasing the diversity of proposed molecules and reducing the complexity of paths to chemical synthesis.

Wu, Yulun↗

DOE OSTI2023

Method and apparatus for constructing informative outcomes to guide multi-policy decision making

In Multi-Policy Decision-Making (MPDM), many computationally-expensive forward simulations are performed in order to predict the performance of a set of candidate policies. In risk-aware formulations of MPDM, only the worst outcomes affect the decision making process, and efficiently finding these influential outcomes becomes the core challenge. Recently, stochastic gradient optimization algorithms, using a heuristic function, were shown to be significantly superior to random sampling. In this disclosure, it was shown that accurate gradients can be computed-even through a complex forward simulation—using approaches similar to those in dep networks. The proposed approach finds influential outcomes more reliably, and is faster than earlier methods, allowing one to evaluate more policies while simultaneously eliminating the need to design an easily-differentiable heuristic function.

Olson, Edwin↗

DOE OSTI2022

SPATIAL GRAPH ATTENTION AND CURIOSITY-DRIVEN POLICY FOR ANTIVIRAL DRUG DISCOVERY

We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure - this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis.

Wu, Y↗

DOE OSTI2026

A Real2Sim Digital Twin Pipeline for Photorealistic Robot Simulation: Evaluating VLA Policy Deployment on a Bimanual Mobile Robot

Digital twins that are automatically constructed from robot sensor data offer a promising pathway for scalable Real2Sim and Sim2Real transfer. However, it remains an open question whether photorealistic reconstruction alone is sufficient to support reliable deployment of vision-language-action (VLA) policies. We present a generative-AI-assisted Real2Sim pipeline that generates simulation-ready digital twins from real-world RGB observations with minimal manual intervention. The pipeline uses prompted segmentation to isolate scene components and a generative 3D model to directly produce simulation assets, eliminating the need for traditional multi-view reconstruction or manual 3D modeling.\r\nTo evaluate simulation fidelity, we deploy and compare policies from two VLA models in both the real robot and the reconstructed\r\nsimulation under identical tasks and initial conditions. We compare joint-level action trajectories and analyze how divergence evolves over time in closed-loop execution. Although the reconstructed environments are visually accurate, we observe increasing trajectory divergence during closedloop operation. These results indicate that photorealistic reconstruction alone is insufficient to preserve closed-loop control behavior\r\nin VLA policies, particularly in contact-rich manipulation settings where small perceptual errors compound over time.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Financial Incentives to Enable Clean Energy Deployment: Policy Overview and Good Practices

Financial incentives have been widely implemented by governments around the world to support scaled up deployment of renewable energy and energy efficiency technologies and practices. As of 2015, at least 48 countries have adopted financial incentives to support renewable energy and energy efficiency deployment. Broader clean energy strategies and plans provide a crucial foundation for financial incentives that often complement regulatory policies such as renewable energy targets, standards, and other mandates. This policy brief provides a primer on key financial incentive design elements, lessons from different country experiences, and curated support resources for more detailed and country-specific financial incentive design information.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2016

Energy Policy Case Study - California: Renewables and Distributed Energy Resources

The purpose of this document is to present a case study of energy policies in California related to power system transformation and renewable and distributed energy resources (DERs). Distributed energy resources represent a broad range of technologies that can significantly impact how much, and when, electricity is demanded from the grid. Key policies and proceedings related to power system transformation and DERs are grouped into the following categories: 1.Policies that support achieving environmental and climate goals 2.Policies that promote deployment of DERs 3.Policies that support reliability and integration of DERs 4.Policies that promote market animation and support customer choice. Major challenges going forward are forecasting and modeling DERs, regulatory and utility business model issues, reliability, valuation and pricing, and data management and sharing.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Community Solar Policy Screening Workbook for Multifamily Affordable Housing Building Portfolios

This workbook provides a framework to help Multifamily Affordable Housing (MFAH) housing providers evaluate state-level policy landscapes as they relate to MFAH community solar development. By exploring which programs and policies aid in the technical and economic feasibility of MFAH solar projects, building owners can gain an understanding of where in their portfolios to begin developing solar. This workbook also walks through the steps necessary for MFAH building owners to assess their portfolios using this framework. It was developed by the National Renewable Energy Laboratory for the National Community Solar Partnership (NCSP) Multifamily Affordable Housing Collaborative. Learn more about the NCSP and the Collaborative at https://www.energy.gov/communitysolar/community-solar.

14 SOLAR ENERGY↗

DOE OSTI2023

DEPRECATED - State Policies and Programs for Community Solar (2022)

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nlr.gov/submissions/249 The purpose of this dataset is to summarize current community solar policies and low-income stipulations by the state in the United States as of December 2022. The "State_Program" sheet summarizes the key policy details for each state. This dataset is associated with the National Renewable Energy Laboratory (NREL) publication "Expanding Solar Access: State Community Solar Landscape (2022)." https://www.nlr.gov/docs/fy23osti/84247.pdf This list has been reviewed, but errors may exist, and the list may not be comprehensive. NREL invites input to update or add to the database. Please submit updates, additions, or corrections please find contact information on the current data set page linked above.

14 SOLAR ENERGY↗

DOE OSTI2024

State Policies and Programs for Community Solar

The purpose of this dataset is to summarize current community solar policies and related stipulations by state in the United States. The dataset is updated multiple times per year. The current version is the first file located below. Previous versions of the dataset published before August of 2024 can be found in the dataset below labeled “ARCHIVE_State Policies and Programs for Community Solar_Before 08.24.“ This list has been reviewed, but errors may exist, and the list may not be comprehensive. NLR invites input to update or add to the database. Please submit updates, additions, and corrections to Jenna Harmon (Jenna.Harmon@nlr.gov) and Sonja Berdahl (sonja.berdahl@nlr.gov).

14 SOLAR ENERGY↗

DOE OSTI2014

Community Shared Solar: Policy and Regulatory Considerations (Brochure)

This brochure explores the ways in which the shared solar business model interacts with existing policy and regulations, including net metering, tax credits, and securities regulation. It presents some of the barriers that shared solar projects may face, and provides options for creating a supportive policy environment.

14 SOLAR ENERGY↗

DOE OSTI2014

Dynamic Line Rating: Research and Policy Evaluation

Dynamic Line Rating (DLR) is a smart grid technology that allows the rating of electrical conductors to be increased based on local weather conditions. Overhead lines are conventionally given a conservative rating based on worst case scenarios. We demonstrate that observing the conditions in real time leads to additional capacity and safer operation. This paper provides a report of a pioneering scheme in the United States of America in which DLR has been applied. Thereby, we demonstrate that observing the local weather conditions in real time leads to additional capacity and safer operation. Secondly, we discuss limitations involved. In doing so, we arrive at novel insights which will inform and improve future DLR projects. Third, we provide a policy background and discussion to clarify the technology’s potential and identifies barriers to the imminent adoption of dynamic line rating systems. We provide suggestions for regulatory bodies about possible improvements in policy to encourage adoption of this beneficial technology.

17 WIND ENERGY↗

DOE OSTI2017

Energizing Government Decision-Makers with the Facts on Solar Technology, Policy, and Integration

The Solar Technical Assistance Team (STAT) is a network of solar technology and implementation experts who provide timely, unbiased expertise to assist policymakers and regulators in making informed decisions about solar programs and policies. Government officials can submit requests directly to the STAT for technical assistance. STAT then partners with experts in solar policy, regulation, finance, technology, and other areas to deliver accurate, up-to-date information to state and local decision makers. The STAT responds to requests on a wide range of issues -- including, but not limited to, feed-in tariffs, renewable portfolio standards, rate design, program design, workforce and economic impacts of solar on jurisdictions, and project financing.

14 SOLAR ENERGY↗

DOE OSTI2019

Quasi-Stochastic Approximation and Off-Policy Reinforcement Learning: Preprint

The Robbins-Monro stochastic approximation algorithm is a foundation of many algorithmic frameworks for reinforcement learning and often an efficient approach to solving (or approximating the solution to) complex optimal control problems. In many cases, however, practitioners are unable to apply these techniques because of an inherent high variance. This paper aims to provide a general foundation for 'quasi-stochastic approximation,' in which all of the processes under consideration are deterministic, much like quasi-Monte Carlo for variance reduction in simulation. The variance reduction can be substantial, subject to tuning of pertinent parameters in the algorithm. This paper introduces a new coupling argument to establish the optimal rate of convergence provided the gain is sufficiently large. These results are established for linear models and tested also in nonideal settings. A major application of these general results is a new class of reinforcement learning algorithms for deterministic state space models. In this setting, the main contribution is a class of algorithms for approximating the value function for a given policy, using a different policy designed to introduce exploration.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

U.S. Federal Policy Considerations for the Management of Retired Large-Format Batteries

The global demand for large-format batteries used in electric vehicles (EV) and battery energy storage is expected to continue as governments call for zero emission policies. Total installed large-scale stationary battery energy storage is expected to increase almost 20-fold in the coming years - from 17 GW installed globally in 2020 to 358 GW projected in 2030. Similarly, light duty EVs sales globally are expected to increase more than 8-fold from 2020 to 2030 - from 3 million units to 25 million units. The expected demand for large-format batteries brings supply chain concerns, and economic opportunities. Domestic reuse and recycling is one potential circular economy solution for large-format batteries. This presentation discusses current U.S. law and regulatory landscape for the reuse and recycling of large-format batteries, and how certain policy frameworks impact reuse and end-of-life management decisions for large-format battery materials.

battery↗

DOE OSTI2023

Integrated path planning and control through proximal policy optimization for a marine current turbine

This paper presents an integrated path planning and tracking control framework for a marine current turbine (MCT), where the MCT is treated as an energy-harvesting autonomous underwater vehicle (AUV). Considering the ocean (space of action) is continuous, the proposed framework employs two modules to address path planning and path tracking enabled by the proximal policy optimization (PPO) algorithm, which is a policy gradient deep reinforcement learning (RL) method. Further, to enable fully autonomous operation in a stochastic oceanic environment, the proposed path planning seeks a primary objective of maximizing the harvested energy; then, the path tracking module is designed to minimize the tracking error and avoid collisions with static and dynamic obstacles. Using field-collected acoustic Doppler current profiler (ADCP) data, the performance of the proposed framework is evaluated. Comparative studies with baseline algorithms in three different scenarios of path planning, path tracking without an obstacle, and path tracking with collision avoidance verify the effectiveness of our proposed approach.

16 TIDAL AND WAVE POWER↗

DOE OSTI2014

Improving energy efficiency via smart building energy management systems. A comparison with policy measures

In this study, to foster the transition to more sustainable energy systems, policymakers have been approving measures to improve energy efficiency as well as promoting smart grids. In this setting, building managers are encouraged to adapt their energy operations to real-time market and weather conditions. Yet, most fail to do so as they rely on conventional building energy management systems (BEMS) that have static temperature set points for heating and cooling equipment. In this paper, we investigate how effective policy measures are at improving building-level energy efficiency compared to a smart BEMS with dynamic temperature set points. To this end, we present an integrated optimisation model mimicking the smart BEMS that combines decisions on heating and cooling systems operations with decisions on energy sourcing. Using data from an Austrian and a Spanish building, we find that the smart BEMS results in greater reduction in energy consumption than a conventional BEMS with policy measures.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2017

Sensitivity of natural gas deployment in the US power sector to future carbon policy expectations

One option for reducing carbon emissions in the power sector is replacement of coal-fired generation with less carbon-intensive natural gas combined cycle (NGCC) generation. In the United States, where there is abundant, low-cost natural gas supply, increased NGCC deployment could be a cost-effective emissions abatement opportunity at relatively modest carbon prices. However, under scenarios in which carbon prices rise and deeper emissions reductions are achieved, other technologies may be more cost-effective than NGCC in the future. In this analysis, using a US energy system model with foresight (a version of the National Energy Modeling System or “NEMS” model), we find that varying expectations about carbon prices after 2030 does not materially affect NGCC deployment prior to 2030, all else equal. An important implication of this result is that, under the set of natural gas and carbon price trajectories explored here, myopic behavior or other imperfect expectations about potential future carbon policy do not change the natural gas deployment path or lead to stranded natural gas generation infrastructure. Lastly, we explain these results in terms of the underlying economic competition between available generation technologies and discuss the broader relevance to US climate change mitigation policy.

03 NATURAL GAS↗