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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 163 records · Page 9

A Dynamic Pricing Method to Manage the Impact of EV Charging on the Grid Using RL

This work addresses the challenge of managing electrical vehicle (EV) charging loads on distribution feeders with the increase in deployment of fast charging stations. To mitigate the adverse impacts on feeder health, a novel dynamic grid-informed pricing approach is proposed. This approach leverages reinforcement learning (RL) to determine hourly charging prices based on real-time grid conditions. A synthetic environment was developed to train the reinforcement learning agent. A model of an IEEE 34-bus distribution feeder with EV charging stations has been developed in OpenDSS utilizing Caldera for realistic EV charging profiles. Test cases demonstrate that the dynamic pricing strategy achieves higher energy delivery to the EV end user at a lower cost compared to constant pricing methods, while lowering voltage deviations and congestion. This approach offers more granular price adjustments, responding dynamically to feeder conditions and potentially improving grid stability and efficiency. The communication architecture to implement this dynamic pricing method is described. This research contributes to the development of smart grid-informed charging solutions that can reduce the cost of charging to the end user while also helping the grid.

EV charging, dynamic pricing, grid-informed chargi↗

A dynamic pricing method to manage the impact of EV charging on the grid using RL

This work addresses the challenge of managing electrical vehicle (EV) charging loads on distribution feeders with the increase in deployment of fast charging stations. To mitigate the adverse impacts on feeder health, a novel dynamic grid-informed pricing approach is proposed. This approach leverages reinforcement learning (RL) to determine hourly charging prices based on real-time grid conditions. A synthetic environment was developed to train the reinforcement learning agent. A model of an IEEE 34-bus distribution feeder with EV charging stations has been developed in OpenDSS utilizing Caldera for realistic EV charging profiles. Test cases demonstrate that the dynamic pricing strategy achieves higher energy delivery to the EV end user at a lower cost compared to constant pricing methods, while lowering voltage deviations and congestion. This approach offers more granular price adjustments, responding dynamically to feeder conditions and potentially improving grid stability and efficiency. The communication architecture to implement this dynamic pricing method is described. This research contributes to the development of smart grid-informed charging solutions that can reduce the cost of charging to the end user while also helping the grid.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

Development of a Reference Design for a Cyber-Physical System

The purpose of this thesis is to develop a reference design to assist in the selection of security practices in power electronics design. A prototype will be developed from this reference design for evaluation. This evaluation will include a brief cost/benefit analysis to gauge the efficacy of implementing each layer of security throughout the power electronics design process. This thesis will also describe the obstacles and effectiveness of integrating a Trusted Platform Module (TPM) into a cyber-hardened grid-connected device. The TPM device is a secured crypto processor that assists in generating, storing, and restricting the use of cryptographic keys. The emphasis of this research is to establish integrity, authenticity, and confidentiality within a system by providing a baseline of security concerns for segments of the system. This research considers communication, control, and hardware level securities. The scope of this thesis will review the necessary security methods as well as consider the effects these methods have on the embedded system, to assess the desired security to responsiveness trade off. Applying this approach to a design process will alleviate various unknowns of appending security to a power electronics design. This thesis describes the specific vulnerabilities introduced within this grid-edge environment, and how the liabilities within the system can be mitigated. Initially, common security techniques will be considered to establish a guideline to benchmark performance and resource costs of the system. The foundation will be a non-hardened power electronic system platform with industry standard communication protocols. Several security techniques and attack vectors will then be evaluated to contribute to the base level platform. Other fail-safe features take place to gauge progress of the selected approach, non-inclusive to the TPM. Collectively, this investigation will determine a valid experiment by appraising and categorizing resource allocation, performance overhead, and monetary cost analysis results into a reference design. The prototype will then demonstrate methods to relieve common threats that are purposefully implemented into the design.

Blair, Nicholas Paul↗

Parallel Simulation of Quantum Networks with Distributed Quantum State Management

Quantum network simulators offer the opportunity to cost-efficiently investigate potential avenues for building networks that scale with the number of users, communication distance, and application demands by simulating alternative hardware designs and control protocols. Several quantum network simulators have been recently developed with these goals in mind. As the size of the simulated networks increases, however, sequential execution becomes time-consuming. Parallel execution presents a suitable method for scalable simulations of large-scale quantum networks, but the unique attributes of quantum information create unexpected challenges. In this work, we identify requirements for parallel simulation of quantum networks and develop the first parallel discrete-event quantum network simulator by modifying the existing serial simulator SeQUeNCe. Our contributions include the design and development of a quantum state manager (QSM) that maintains shared quantum information distributed across multiple processes. We also optimize our parallel code by minimizing the overhead of the QSM and decreasing the amount of synchronization needed among processes. Using these techniques, we observe a speedup of 2 to 25 times when simulating a 1,024-node linear network topology using 2 to 128 processes. We also observe an efficiency greater than 0.5 for up to 32 processes in a linear network topology of the same size and with the same workload. We repeat this evaluation with a randomized workload on a caveman network. We also introduce several methods for partitioning networks by mapping them to different parallel simulation processes. We have released the parallel SeQUeNCe simulator as an open source tool alongside the existing sequential version.

97 MATHEMATICS AND COMPUTING↗

Lowering the computational barrier: Partially Bayesian neural networks for transparency in medical imaging AI

Deep Neural Networks (DNNs) can provide clinicians with fast and accurate predictions that are highly valuable for high-stakes medical decision-making, such as in brain tumor segmentation and treatment planning. However, these models largely lack transparency about the uncertainty in their predictions, potentially giving clinicians a false sense of reliability that may lead to grave consequences in patient care. Growing calls for Transparent and Responsible AI have promoted Uncertainty Quantification (UQ) to capture and communicate uncertainty in a systematic and principled manner. However, traditional Bayesian UQ methods remain prohibitively costly for large, million-dimensional tumor segmentation DNNs such as the U-Net. In this work, we discuss a computationally-efficient UQ approach via the partially Bayesian neural networks (pBNN). In pBNN, only a single layer, strategically selected based on gradient-based sensitivity analysis, is targeted for Bayesian inference. We illustrate the effectiveness of pBNN in capturing the full uncertainty for a 7.8-million parameter U-Net. We also demonstrate how practitioners and model developers can use the pBNN's predictions to better understand the model's capabilities and behavior.

97 MATHEMATICS AND COMPUTING↗

Scaling Optimal Allocation of Cloud Resources Using Lagrange Relaxation

The rapid growth of Cloud Computing (CC) has increased the variety of computing resources, storage, and communication services that pose significant new challenges for the efficient use of cloud resources. The cost-efficient allocation of cloud resources has become a decisive premise for the adoption of CC services. The cost-efficient selection and scheduling of these resources to meet the demands of a scientific workflow is a challenging problem that is exacerbated by the inclusion of multiple CC providers. In this paper, we present a novel strategy for the cost-efficient selection of CC resources using Lagrange relaxation. Our approach is based on preselection of resources and demand decomposition to create a series of smaller sub-problems, which allow the estimation of the best cost-structures and selection of CC service providers for a subset of the time period of the planning horizon. Decomposition of the demand is achieved through the boundary analysis of a continuous relaxation of the problem. Using the metrics defined in terms of the cost and time of completion, we demonstrate excellent performance in relation to optimal solutions. Our method reduced the computational time from hours to seconds for a representative 36-month problem and provided high-quality solutions (< 0.05% relative error). Given the importance of selecting resources and scheduling complex scientific workflows, we believe that this novel strategy will be beneficial for many researchers and users of cloud computing resources.

De La Torre, Luis↗

The Demand-Side Opportunity: The Roles of Distributed Solar and Building Energy Systems in a Decarbonized Grid

Building energy systems represent a significant and largely untapped demand-side grid resource. Buildings use about 75% of electricity in the United States, including around 80% of peak demand, meaning that changes in building energy use have significant implications for grid operations. Building energy technologies-including energy-efficient devices, flexible loads, and energy storage-can be coordinated and aggregated to perform similar functions as centralized grid assets. Recent advances in information and communication (ICT) technologies have significantly improved building energy technology automation, coordination, and aggregation capabilities. With declining costs and technological advances, building energy systems can increasingly compete with conventional grid assets such as natural gas plants. Building energy technologies enable the deployment of distributed solar photovoltaics (DPV). Energy-efficient buildings allow DPV to meet greater shares of building load while flexible loads and energy storage can reshape building load profiles to optimize the on-site use of DPV. Optimized on-site use of DPV increases the value proposition for DPV adoption and can help utilities cost-effectively integrate higher levels of DPV penetration. Together, DPV and building energy technologies could help grids achieve deep decarbonization more quickly and cost-effectively.

14 SOLAR ENERGY↗

Grand challenges and opportunities in next-generation batteries and technologies

The development of advanced Li-ion batteries and technologies generally addresses one of four objectives: 1) create a higher volumetric energy density and/or specific energy/power, 2) impart intrinsically safer chemistry, 3) produce speedier charging, and 4) utilize less expensive batteries but with competitive/near-competitive performances. Certainly, other factors can play a role as well, dependent on the type of market targeted and the availability of global supplies; however, for widespread adoption, the above points/criteria remain salient. Li-ion is commercially well entrenched in industry for communication and transportation (EV) applications. Nowadays, slight iterations, mostly electrolyte-defined, are incrementally improving safety, cost, and cycle or calendar life. The last point, calendar life, is one that is often overlooked for very high-energy dense Li-ion batteries, because of their reactivity at higher charge (OCV conditions) and elevated temperatures. While cycle life is debated with respect to capacity/energy performance decline, attempts to re-purpose the battery itself or recycle the internal chemical constituents at end of life have considerably grown in the field. Hopefully, energy-neutral processes are also considered in the recycling loop. Nevertheless, the energy storage arena is quite large, and this pursuit hinges on pushing the field in one of many directions, toward loftier objectives. The pursuit of next-generation batteries and technologies must thus delve deeper into new and novel chemistry and electrochemistry to create a world with a neutral, carbon-free environment, and one that is solely sufficient on energy-producing renewables such as the Sun and wind-derived means. The application of electricity and chemistry within our world is thus a 21st century opus.

25 ENERGY STORAGE↗

Cybersecurity of Networked Microgrids: Challenges Potential Solutions and Future Directions

Networked microgrids are clusters of geographically-close, islanded microgrids that can function as a single, aggregate island. This flexibility enables customer-level resilience and reliability improvements during extreme event outages and also reduces utility costs during normal grid operations. To achieve this cohesive operation, microgrid controllers and external connections (including advanced communication protocols, protocol translators, and/or internet connection) are needed. However, these advancements also increase the vulnerability landscape of networked microgrids, and significant consequences could arise during networked operation, increasing cascading impact. To address these issues, this report seeks to understand the unique components, functions, and communications within networked microgrids and what cybersecurity solutions can be implemented and what solutions need to be developed. A literature review of microgrid cybersecurity research is provided and a gap analysis of what is additionally needed for securing networked microgrids is performed. Relevant cyber hygiene and best practices to implement are provided, as well as ideas on how cybersecurity can be integrated into networked microgrid design. Lastly, future directions of networked microgrid cybersecurity R&D are provided to inform next steps.

24 POWER TRANSMISSION AND DISTRIBUTION↗

A Fully Decentralized Modulation Scheme for Modular Multilevel Converters

As the number of submodules rapidly increase, control architecture of Modular Multilevel Converters (MMCs) is transforming from centralized to distributed in order to address the challenges of tremendous computational burden and massive wiring. In this digest, we propose a modulation scheme for the MMCs with distributed controls, where each submodule is able to handle both controls and modulation in a decentralized manner. It enables the distributed controllers to synchronize in terms of fast-switching pulse width modulation (PWM). Aiming at this key technical challenge, this research develops a physics-informed PWM control strategy of mirroring the spontaneous synchronization feature of coupled oscillator’s behavior at switching frequency. Thanks to less reliance on wirings and communications, the proposed approach has the potential of i) improving reliability of MMC systems, and ii) reducing cost and space requirements for MMCs. The proposed approach has been preliminarily verified through a simulation tool for power electronics systems.

Lu, Minghui [BATTELLE (PACIFIC NW LAB)]↗

A framework and tool for designing cost-effective, resilient, and circular net-zero supply chains under uncertainty with an application to multilayer plastic films

While 55% of Fortune 500 companies have committed to achieving net-zero emissions and/or zero-waste operations by 2035, only 2% are currently on track, revealing a critical gap between ambition and action. Designing supply chains that reduce both emissions and waste is a complex non-intuitive, multi-objective challenge, compounded by the high costs of new technologies and the need for resilient, profitable solutions. This paper aims to address this challenge by presenting a generic framework and multi-objective optimization formulation for designing cost-effective, circular, and resilient supply chains under uncertainty, implemented through a user-friendly decision-support tool with intuitive data visualization capabilities, enabling communication of results to both technical and non-technical stakeholders. We demonstrate the application of this framework in the context of multilayer plastic films (barrier films), which are widely used in food packaging and composite materials. The model quantifies trade-offs across three objectives: minimizing global warming potential, maximizing circularity, and minimizing cost. A key contribution of this work is the explicit modeling of technological resilience, the ability of supply chains to maintain function under disruption. In the cost-minimization case, the resilience constraint makes the design approximately three times more expensive in the short-term metric, but shifts the system from relying on a single recovery pathway to a portfolio of four recovery pathways, improving the robustness of the optimization solution under uncertainty. Lastly, we introduce TranZero, a decision-support tool that integrates material flow analysis, hotspot identification, and optimization-based scenario planning to support net-zero and circularity decisions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Decentralized Carrier Phase Shifting for Optimal Harmonic Minimization in Asymmetric Parallel-Connected Inverters

This paper presents a carrier phase shifting technique for minimizing the aggregate harmonics in networks of asymmetric parallel-connected inverters for distributed power generation system applications. The proposed technique is: 1) implemented in a decentralized manner, relying only on local voltage and current measurements, and 2) optimal in the sense that it minimizes a cost function representing the carrier-frequency current harmonics. The analysis indicates that the proposed optimal carrier phase shifting technique can enable order-of-magnitude reductions in harmonic power, and also universal improvements compared to symmetric carrier interleaving for asymmetric inverter networks. Moreover, compared to existing methods that require either centralized communication or information exchange between inverters to coordinate carriers, the proposed technique is completely decentralized, which provides important practical benefits for implementation, including improved robustness and reduced cost. The technique is experimentally validated on a network of three single-phase 2-kW inverters and demonstrates a 36.5% reduction in the weighted total harmonic distortion factor of the aggregate inverter current, and the ability to converge to the optimal carrier phase spacing dynamically in less than one line frequency cycle (16.7 ms) in steady state and transient operating conditions.

42 ENGINEERING↗

Photonic circuits for laser stabilization with integrated ultra-high Q and Brillouin laser resonators

The integration of stabilized lasers, sources that generate spectrally pure light, will provide compact, low-cost solutions for applications including quantum information sciences, precision navigation and timing, metrology, and high-capacity fiber communications. We report a significant advancement in this field, demonstrating stabilization of an integrated waveguide Brillouin laser to an integrated waveguide reference cavity, where both resonators are fabricated using the same CMOS-compatible integration platform. We demonstrate reduction of the free running Brillouin laser linewidth to a 292 Hz integral linewidth and carrier stabilization to a 4.9 × 10 –13 fractional frequency at 8 ms reaching the cavity-intrinsic thermorefractive noise limit for frequencies down to 80 Hz. We achieve this level of performance using a pair of 56.4 x 10 6 quality factor Si 3 N 4 waveguide ring-resonators that reduce the high-frequency noise by the nonlinear Brillouin process and the low-frequency noise by Pound–Drever–Hall locking to the ultra-low loss resonator. These results represent an important step toward integrated stabilized lasers with reduced sensitivity to environmental disturbances for atomic, molecular, and optical physics (AMO), quantum information processing and sensing, and other precision scientific, sensing, and communications applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

IceNet for FireBox - A Berkeley Warehouse-Scale Computer

Berkeley’s FireBox is a next-generation warehouse-scale computer (WSC) that utilizes the energy-efficiency and bandwidth density of integrated silicon-photonic interconnects to enable a new high-bandwidth and low-latency network fabric connecting thousands of compute nodes to petabytes of DRAM and Flash storage. The high bandwidth, low latency and high connectivity of FireBox’s WSC network fabric (IceNet) will enable dramatic improvements in the overall system energy efficiency enabling fine-grain power control on system resources (processors, links and memory/storage components). IceNet is a special 3-stage photonic Clos network architected to achieve ultra-low-latency connectivity between processor nodes and memory, drastically cutting down on the energy wasted in resource idling (processors and memory stalled due to pending network requests). This is achieved by integration of the first and last switch stages into processor/memory hub clients and by heavy over-provisioning of the high-radix middle switches (FlareSwitches). A key hardware component developed in this program is an active laser power management photonic integrated circuits called LightSpark. It interacts with the FlareSwitch and provides laser power to a subset of occupied switch ports, increasing the utilization of laser light in the photonic network by an order of magnitude. In addition to guiding the laser power where it is needed, the laser-power management module enables both wavelength and laser redundancy, significantly increasing the robustness of the system. The goal of the IceNet fabric is to enable communication between 1000s of processor nodes and PBs of memory/storage with <100ns latency, <10pJ/b wall-plug energy cost at multiple Pb/s of available connectivity bandwidth. These metrics represent two-orders of magnitude improvement with respect to the status of current data-center technology.

42 ENGINEERING↗

A Cybersecurity Threat Profile for a Connected Lighting System

In anticipation of improved energy performance and cost savings, cities and building owners are increasingly considering “smart lighting initiatives” that aim to convert their collection of simple luminaires (i.e., lighting fixtures) into an intelligent connected lighting system (CLS) capable of remotely monitoring energy consumption and fault conditions, and possibly implementing adaptive lighting schemes. The U.S. Department of Energy (DOE) has set an national goal of tripling the energy efficiency and demand flexibility of the buildings sector by 2030, relative to 2020 levels 1. It is forecast that connected lighting systems can contribute to that goal by delivering 125 TWh of annual energy savings by 2035 2, equivalent to the annual output of 50 typical (500 MW) power plants. However, these energy savings and the DOE goal are put at significant risk if connected technologies are not adopted due to real or perceived cybersecurity concerns. Connected IoT devices such as these have historically been rife with vulnerabilities which sometimes put security considerations secondary to functionality and operability. What are the cybersecurity threats that will impact these systems, as formerly banal luminaires transition into intelligent connected devices that collect information about themselves, their surrounding environment, and possibly us? In this paper we analyze a threat profile performed on a fault-detection use case for streetlights. A threat profile establishes security requirements, justifies security measures, yields actionable controls, and effectively communicates risk to stakeholders. This effort provides critical information for making threat-based decisions to increase security at a reasonable cost, and can effectively be used by development teams, software architects, and managers to make cybersecurity a part of their ongoing culture of awareness, training, and prevention. This leads to more secure systems and better-understood security. On-premise, cloud, and hybrid architectures with different authentication mechanisms were modeled and later categorized using the Microsoft STRIDE framework. An analysis of the recommended controls for each threat was performed to determine which controls could and should be put in place by manufacturers or third-party suppliers, and which controls need to be left up the end-user to implement. Fifty-seven threats were identified. Among our key findings: (1) 65% (37/57) of the threats did not involve the luminaires, but rather the other components needed to communicate with and manage them; (2) 63% (36/57) of the threats could have been mitigated through manufacturer-implemented defensive techniques or “controls”; and (3) 23% (13/57) of the threats were dependent on the network configuration. Recommendations based on the results of this work are made to key stakeholder groups. Notably, lighting technology developers are advised to address all threats that can be reasonably controlled with baked-in technology solutions (e.g., encryption or authentication controls), and employ some form of secure supply chain management and tracking where other parts (e.g., sensors, microprocessors) of a luminaire must also be built and manufactured with the proper security controls in place. Developers should also review threats involving assets not developed in-house to understand how connectivity with other devices will affect their product during system operation and determine if a compensating control for a defense-in-depth strategy will be needed. Finally, those interested in deploying CLS should compare the differences between cloud and on-premise models to determine which is more suitable for their needs and the abilities of their security team.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Multi-task deep reinforcement learning for intelligent multi-zone residential HVAC control

In this short communication, a data-driven deep reinforcement learning (deep RL) method is applied to minimize HVAC users’ energy consumption costs while maintaining users’ comfort. The applied deep RL method's efficiency is enhanced by conducting multi-task learning that can achieve an economic control strategy for a multi-zone residential HVAC system in both cooling and heating scenarios. The applied multi-task deep RL method is compared with a rule-based benchmark case and a single-task deep deterministic policy gradient algorithm to verify its effective and generalized application in optimizing HVAC operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

The effect of monitoring complexity on stakeholder acceptance of CO2 geological storage projects in the US gulf coast region

Environmental monitoring at geologic CO 2 storage sites is required by regulations for the purposes of environmental protection and emissions accounting in the case of leakage to surface. However, another very important goal of environmental monitoring is to assure stakeholders that the project is monitored for safety and effectiveness. With current efforts to optimize monitoring for cost-effectiveness, the question remains: will optimization of monitoring approaches degrade stakeholder assurance, or do heavily-instrumented sites communicate higher risk to a stakeholder? We report the results of a stakeholder survey in Gulf Coast states of the US where carbon capture and storage (CCS) is developing quickly. We rely on a 2 by 2 factorial experiment in which we manipulate message complexity (complex v. simple) and social norm (support from scientists v. support from community members). Subjects were randomly assigned to one of four conditions: 1) complex message with scientist support; 2) complex message with community member support; 3) simple message with scientist support; or 4) simple message with community member support. In addition to the experimental stimuli, subjects were also asked about their need for cognition, attitudes toward science and scientists, attitudes about climate change and support for carbon capture and storage (CCS). Our sample is drawn from residents in states bordering the western Gulf of Mexico (Texas, Louisiana, Florida) where CO 2 geologic storage is being planned both onshore and offshore. The results offer important implications for public outreach efforts to key stakeholders.

54 ENVIRONMENTAL SCIENCES↗