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At least 361 records · Page 20

Basic Research Needs Workshop on Synthesis Science for Energy Relevant Technology

The technology that lies at our fingertips becomes more powerful each day. Smartphones connect us instantly to family, friends, and co-workers around the globe; give us access to a limitless stream of information; control the heating in our homes; and serve as our cameras, calculators, flashlights, music players, boarding passes and, on occasion, our phones. Cars are ever more fuel-efficient, safer, semi-autonomous, and have more computing power than the systems that guided humankind to the moon. LED lighting and solar panels are becoming commonplace, replacing less efficient technologies and expanding the energy options available worldwide. Novel polymers and nanoparticles are playing a crucial role in enhanced oil recovery. None of these advances would have been possible without the discovery and development of, and ability to create, new materials and chemical processes. Now imagine what our world would be like if we could accelerate those discoveries a thousandfold. What if the only limit to synthesizing new forms of matter were the imagination? We could build complex assemblies of atoms and molecules with architectures and capabilities far exceeding those of materials found in nature—for example, develop catalysts that turn garbage into fuels, design solar cells to power our homes directly from sunlight, make batteries with the energy density of gasoline, and create one- and two-dimensional solids that transport charge hundreds of times faster than silicon or allow us to build quantum bits based on the spins of electrons or photons to realize the promise of “beyond Moore’s law” computing. Advances in synthesis science are required to bring about this future—we not only must know how to design new molecules and materials with desired functions and properties through theory and computational techniques; we also must be able to make the materials we envision. New approaches to discovering as yet unimagined matter require a sea change in the way we think about the science of synthesis. Chemical and materials sciences have traditionally focused on understanding structure–function relationships with the goal of predicting where the atoms should be placed to achieve a targeted property or process. Much less effort has been directed toward a predictive science of synthesis—understanding how to get the atoms where they need to go to achieve the desired structure. This report, which is the result of the Basic Energy Sciences Workshop on Basic Research Needs for Synthesis Science for Energy Technologies, lays out the scientific challenges and opportunities in synthesis science. The workshop was attended by more than 100 leading national and international scientific experts. Its five topical and two crosscutting panels identified four priority research directions (PRDs) for realizing the vision of predictive, science-directed synthesis.

99 GENERAL AND MISCELLANEOUS↗

Energy and mobility impacts of connected autonomous vehicles with co-optimization of speed and powertrain on mixed vehicle platoons

Intersections are known to be traffic bottlenecks where a significant amount of energy consumption could be caused due to deceleration/acceleration in the presence of red signals. With an increased level of connectivity and automation of intelligent transportation systems, connected autonomous vehicles (CAVs) are expected to be able to proactively adjust their driving strategies subject to constraints imposed by the predicted future traffic. As a result, many potential benefits can be achieved, such as improved energy efficiency, enhanced traffic safety, among many others. Notably, the way CAVs are controlled affects the following legacy vehicles (LVs) due to complex traffic dynamics. Here, we are particularly interested in studying the energy and mobility impact of CAVs with an improved traffic prediction method on mixed vehicle platoons at various market penetration rates. Leveraging traffic prediction, CAVs are controlled with co-optimization of their speed and gear position. Specifically, a traffic prediction framework in a rolling horizon fashion is employed based upon a modified Payne–Whitham (PW) model capable of handling mixed traffic consisting of CAVs and LVs. The prediction error of the modified PW model is reduced by 53.62% compared to that of the standard PW model under test scenarios. According to the predicted traffic conditions, speed and gear position of CAVs are co-optimized with the primary goal of minimizing energy consumption when driving on a signalized arterial. The energy benefits achieved by CAVs and the impact of CAVs on LVs behind are studied comprehensively for mixed vehicle platoons. The lead LV follows a real-world speed profile collected on TH-55 in Minnesota. Numerical results show that energy benefits achieved by the vehicle platoon range from 2% to 16%, and a 1% to 5% reduction in travel time for LVs behind CAVs is also observed, at different penetration rates of CAVs in various traffic scenarios. Furthermore, it is observed that CAVs using the proposed eco-driving approach appear to have a positive impact on the LVs behind in terms of energy consumption, regardless of the driving styles of the LVs ahead.

33 ADVANCED PROPULSION SYSTEMS↗

Integrated Optimization of Powertrain Energy Management and Vehicle Motion Control for Autonomous Hybrid Electric Vehicles

Hybrid Electric Vehicles (HEVs) and autonomous vehicles have been widely studied recently for on-road transportation. In the study of autonomous HEVs, the control of the vehicle's external dynamics and powertrain dynamics are often treated separately. Optimizing these two problems together can significantly improve fuel economy. In this paper, an autonomous HEV following a leader is considered. First, the augmented model to integrate the abovementioned dynamics is presented. Second, the optimization problem is defined to find the optimum fuel consumption of the follower in pursuit of a leader in a drive cycle. A customized control strategy based on Approximate Dynamic Programming (ADP) is then explored in which the optimal cost-to-go at each time step is approximated using neural networks. Also, the accuracy of the optimization solution is enhanced by applying the concept of the reachable sets. At last, three case studies show that the examined integrated control strategy outperforms the one with the separated optimization method by an additional 7.4%, 4.6%, and 11.8% improvement in fuel consumption, respectively.

33 ADVANCED PROPULSION SYSTEMS↗

Active Suppression of Drilling System Vibrations For Deep Drilling

The dynamic stability of deep drillstrings is challenged by an inability to impart controllability with ever-changing conditions introduced by geology, depth, structural dynamic properties and operating conditions. A multi-organizational LDRD project team at Sandia National Laboratories successfully demonstrated advanced technologies for mitigating drillstring vibrations to improve the reliability of drilling systems used for construction of deep, high-value wells. Using computational modeling and dynamic substructuring techniques, the benefit of controllable actuators at discrete locations in the drillstring is determined. Prototype downhole tools were developed and evaluated in laboratory test fixtures simulating the structural dynamic response of a deep drillstring. A laboratory-based drilling applicability demonstration was conducted to demonstrate the benefit available from deployment of an autonomous, downhole tool with self-actuation capabilities in response to the dynamic response of the host drillstring. A concept is presented for a prototype drilling tool based upon the technical advances. The technology described herein is the subject of U.S. Patent Application No. 62219481, entitled "DRILLING SYSTEM VIBRATION SUPPRESSION SYSTEMS AND METHODS", filed September 16, 2015.

Raymond, David W.↗

Autonomous Intelligent Assembly Systems LDRD 105746 (Final Report)

This report documents a three-year to develop technology that enables mobile robots to perform autonomous assembly tasks in unstructured outdoor environments. This is a multi-tier problem that requires an integration of a large number of different software technologies including: command and control, estimation and localization, distributed communications, object recognition, pose estimation, real-time scanning, and scene interpretation. Although ultimately unsuccessful in achieving a target brick stacking task autonomously, numerous important component technologies were nevertheless developed. Such technologies include: a patent-pending polygon snake algorithm for robust feature tracking, a color grid algorithm for uniquely identification and calibration, a command and control framework for abstracting robot commands, a scanning capability that utilizes a compact robot portable scanner, and more. This report describes this project and these developed technologies.

Anderson, Robert J.↗

Application of DLT cybersecurity stack to TES applications for a scalable, cybersecure, and interoperable future

Transactive Energy Systems (TES) are expected to improve upon existing grid operations and capabilities by enabling the integration of traditional grid resources with distributed energy resources (DER). Distributed Ledger Technology or DLT (e.g., blockchain) presents itself as a viable instrument to support decentralized, autonomous, and tamper-evident applications, which can be leveraged within TES's ecosystem. DLTs can provide pertinent security controls including access controls, data immutability, and traceability in addition to other well-known advantages such as decentralization and scalability. This work demonstrates the DLT Cybersecurity stack and its applicability to TES-based use-cases/applications. The seven-layer DLT cybersecurity stack is a DLT-agnostic framework that can quickly be used to classify and group the individual needs of an application into the different processing and cybersecurity layers offered by a DLT using a common taxonomy and an architectural mapping framework. This enables application engineers to demystify and strengthen the overall security aspects of their systems while maintaining an open perspective towards features and drawbacks that may hinder their performance in real-world scenarios. The paper leverages the work performed by the IEEE P2418.5 - Blockchain for Energy Standards working group.

Blockchain, blockchain interoperability, Cybersecu↗

Robot for Investigations and Assessments of Nuclear Areas

RIANA is a remote controlled Robot dedicated for Investigations and Assessments of Nuclear Areas. The development of RIANA is motivated by the need to have at disposal a proven robot, tested in hot cells; a robot capable of remotely investigate and characterise the inside of nuclear facilities in order to collect efficiently all the required data in the shortest possible time. It is based on a wireless medium sized remote carrier that may carry a wide variety of interchangeable modules, sensors and tools. It is easily customised to match specific requirements and quickly configured depending on the mission and the operator's preferences. RIANA integrates localisation and navigation systems. The robot will be able to generate / update a 2D map of its surrounding and exploring areas. The position of the robot is given accurately on the map. Furthermore, the robot will be able to autonomously calculate, define and follow a trajectory between 2 points taking into account its environment and obstacles. The robot is configurable to manage obstacles and restrict access to forbidden areas. RIANA allows an advanced control of modules, sensors and tools; all collected data (radiological and measured data) are displayed in real time in different format (chart, on the generated map...) and stored in a single place so that may be exported in a convenient format for data processing. This modular design gives RIANA the flexibility to perform multiple investigation missions where humans cannot work such as: visual inspections, dynamic localization and 2D mapping, characterizations and nuclear measurements of floor and walls, non destructive testing, samples collection: solid and liquid. The benefits of using RIANA are: - reducing the personnel exposures by limiting the manual intervention time, - minimizing the time and reducing the cost of investigation operations, - providing critical inputs to set up and optimize cleanup and dismantling operations. (authors)

42 ENGINEERING↗

Advanced Inverter Voltage Controls: Simulation and Field Pilot Findings

This report describes work performed by the Hawaiian Electric Companies and the National Renewable Energy Laboratory (NREL) to model and simulate advanced inverter grid-support functions and to validate and expand on those simulations through a field pilot study. This work builds on earlier research, referred to as the Voltage Regulation Operational Strategies study. The objective of both is to investigate functionalities available in most photovoltaic (PV) inverters to modulate active and reactive power autonomously based on local voltage measurements for the purpose of mitigating off-nominal grid voltage conditions. Specifically of interest are volt/volt-ampere reactive (VAR) control and volt/watt control, the effect of those functions on quasi-steady-state feeder voltages, and the impact of the functions on PV energy production. Because volt/VAR and volt/watt control autonomously adjust inverter output based on local conditions without requiring communication with any other devices, they are good candidates to increase PV hosting capacity when it is limited by voltage constraints in a feeder with very large numbers of PV systems.

14 SOLAR ENERGY↗

Efficient Automated Driving Strategies Leveraging Anticipation and Optimal Control

Automated vehicles and advanced driver assistance systems bring computation, sensing, and communication technologies that exceed human abilities in some ways. For example, automated vehicles may sense a panorama all at once, do not suffer from human impairments and distractions, and could wirelessly communicate precise data with neighboring vehicles. Prototype and commercial deployments have demonstrated the capability to relieve human operators of some driving tasks up to and including fully autonomous taxi rides in some areas. The ultimate impact of this technology’s large-scale market penetration on energy efficiency remains unclear, with potential negative factors like road use by empty vehicles competing with positive ones like automatic eco-driving. Fundamentally enabled by historic and look-ahead data, this dissertation addresses the use of automated driving and driver assistance to optimize vehicle motion for energy efficiency. Facets of this problem include car following, co-optimized acceleration and lane change planning, and collaborative multi-agent guidance. Optimal control, especially model predictive control, is used extensively to improve energy efficiency while maintaining safe and timely driving via constraints. Techniques including chance constraints and mixed integer programming help overcome uncertainty and non-convexity challenges. Extensions of these techniques to tractor trailers on sloping roads are provided by making use of linear parameter-varying models. To approach the wheel-input energy eco-driving problem over generally shaped sloping roads with the computational potential for closed-loop implementation, a linear programming formulation is constructed. Distributed and collaborative techniques that enable connected and automated vehicles to accommodate their neighbors in traffic are also explored and compared to centralized control. Using simulations and vehicle-in-the-loop car following experiments, the proposed algorithms are benchmarked against others that do not make use of look-ahead information.

Dollar, Robert Austin↗

Segmented Energy Routing for a Modular AC/DC Hybrid System

This article presents a modular ac/dc system with both distributed and centralized power ports for energy router (ER) applications. In each module of the described system, photovoltaic (PV) power generation units, battery-type energy storage (ES) units, and critical loads are connected to the cascaded H-bridge (CHB)-organized medium-voltage (MV) dc links, with fully distributed low-voltage (LV) dc power ports. Copies of modules share the centralized load bus and interact with an MV ac grid in parallel. Hybrid power port (HPO) assigns flexibility to the system but makes energy routings a necessity for stable operation. In this article, a segmented energy management strategy for the HPO-ER is proposed. In terms of the grid-side power transferring, the system ratings are intentionally designed to match significant power imbalance. Focally, a segmented energy management strategy is proposed to realize fully autonomous energy routing involving MV ac grid, distributed PV generations, distributed battery storages, and LV load. The system is proven to be stable using the derived impedance-based model considering the interaction between power blocks. The feasibility of the topology and control strategy is also verified through software simulation, laboratorial hardware prototype experiment, and hardware-in-the-loop (HIL) emulation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Autonomously powered inductively coupled time domain reflectometer sensor device

Devices, systems and methods for use of an autonomously powered inductive time domain reflectometer sensor device are provided. A sensor device for a power line can include a first induction coil and a second induction coil coupled to the power line and a control device. The first induction coil can be configured to inject a first signal on a power line by inducing a first current on the power line. The second induction coil can be configured to inject a second signal on the power line by inducing a second current on the power line. The control device can be configured to control the second induction coil to inject the second signal to cancel a portion of the first signal. As a result of the second signal cancelling a portion of the first signal, the first signal can be configured to propagate in a single direction on the power line.

Folsom, Matthew D.↗

Real-time and Autonomous Water Quality Monitoring System Based on Remotely Operated Vehicle

Existing water quality monitoring systems near hydropower facilities are limited by the lack of mobility of the sensors’ carrier platform. Most systems use a buoy, a mounting fixture attached to a solid structure, or a human worker, which significantly limits the selection of the sampling sites and poses safety risks during data collection and equipment maintenance. To improve on this technology, we developed an autonomous water quality monitoring system that can operate in dangerous water environments near hydropower facilities for water sampling at multiple locations. The goal is to enable safe, timely, and comprehensive water-quality data collection; maximize power generation revenue with improved operational control; and reduce Federal Energy Regulatory Commission and state water quality monitoring costs for compliance. The system incorporates a remotely operated vehicle as the mobile monitoring platform, a dissolved oxygen sensor for monitoring water quality, a tether management system for automatically winding the tether, a solar mobile docking platform for suppling power to the ROV, and a web-based graphical user interface for data post-processing and visualization. In addition, preliminary field research are presented to demonstrate the system capabilities.

Salalila, Aljon L.↗

Hierarchical distributed voltage regulation

Embodiments of the disclosure provide for hierarchically distributed voltage regulation and power flow optimization in a power distribution network. According to one embodiment, a regional coordinator system may determine power flow for an autonomous grid (AG) representing a portion of the distribution network based on a set of voltage parameters from nodes in the AG. The set of voltage parameters my include a current voltage magnitude. The regional coordinator system may provide, to a central controller system of the distribution network, one or more power flow parameters based on the determined power flow for the AG.

Zhou, Xinyang↗

The Future of Renewable Energy Transmission: An Autonomous Energy Grid

The drastic price reduction in variable renewable energy, such as wind and solar, coupled with the ease of use of smart technologies at the consumer level, is driving dramatic changes to the power system that will significantly transform how power is made, delivered, and used. Distributed energy resources (DERs)—which can include solar photovoltaic (PV), fuel cells, microturbines, gensets, distributed energy storage (e.g., batteries, ice storage), and new loads (e.g., electric vehicles (EVs), light-emitting diode (LED) lighting, smart appliances, and electric heat pumps)—are being added to electric grids and causing bidirectional power flows and voltage fluctuations that can impact optimal control and system operation. Residential solar installations, customer battery systems, and EVs are all seeing rapid increases in deployments. With DER seeing such increased use, it is not unreasonable to imagine a residential electricity customer having at least five controllable DERs. In future electric grids, as more DERs are integrated, the number of active control points will be too much for current control approaches to effectively manage.

30 DIRECT ENERGY CONVERSION↗

Stability and Performance of an Undersea Kite Operating in a Turbulent Flow Field

In this paper, we examine the effects of flow disturbances resulting from turbulence on the dynamic behavior of an underwater energy-harvesting kite system that executes periodic figure-8 flight. Due to the periodic nature of the kite’s operation, we begin by assessing orbital stability using the Floquet analysis and stroboscopic intersection analysis of a Poincaré section, with the former analysis performed on a simplified “unifoil” model and the latter performed on a six-degree-of-freedom (6-DOF)/flexible tether model. With periodic stability established, a frequency-domain analysis based on a linearization about the kite’s path is used to predict the quality of flight path tracking as a function of the turbulence frequency. To validate the accuracy of these simulation-based predictions under flow disturbances, we compare the predictions of the kite’s behavior against the results of small-scale tow testing experiments performed in a controlled pool environment.

13 HYDRO ENERGY↗

Development of an Energy Efficient and Cost Effective Autonomous Vehicle Research Platform

Commercialization of autonomous vehicle technology is a major goal of the automotive industry, thus research in this space is rapidly expanding across the world. However, despite this high level of research activity, literature detailing a straightforward and cost-effective approach to the development of an AV research platform is sparse. To address this need, we present the methodology and results regarding the AV instrumentation and controls of a 2019 Kia Niro which was developed for a local AV pilot program. This platform includes a drive-by-wire actuation kit, Aptiv electronically scanning radar, stereo camera, MobilEye computer vision system, LiDAR, inertial measurement unit, two global positioning system receivers to provide heading information, and an in-vehicle computer for driving environment perception and path planning. Robotic Operating System software is used as the system middleware between the instruments and the autonomous application algorithms. After selection, installation, and integration of these components, our results show successful utilization of all sensors, drive-by-wire functionality, a total additional power* consumption of 242.8 Watts (*Typical), and an overall cost of $118,189 USD, which is a significant saving compared to other commercially available systems with similar functionality. This vehicle continues to serve as our primary AV research and development platform.

33 ADVANCED PROPULSION SYSTEMS↗

Interconnection and Interoperability Requirements of Hydrogen Assets to Enable Grid Integration

Traditionally, the inverters used for distributed energy resources (DERs) including solar, energy storage and hydrogen fuel cells are grid following assets. Grid following assets track real power and reactive power setpoints decided by either a local autonomous controller or a remote controller like a microgrid controller. In the past five years, multiple challenges have increased the need for grid forming inverters that can interface to a wide variety of energy resources. These grid forming inverters are becoming critical assets for power systems to enable the replacement of existing rotational machines and provide grid services such as grid resiliency services and black-start. Hydrogen assets (fuel cells) have the potential to successfully replace rotating machines and act as voltage and frequency master by leveraging the long-duration and seasonal energy storage capabilities of hydrogen technologies. This poster presents the progress made in budget period 1 of the proposed work. We will leverage an existing ARIES platform experimental setup to run power-hardware-in-the-loop and controller-hardware-in-the-loop experiments with grid forming fuel cell inverters to develop and document the standardization requirements.

electrolyzer↗

Machine Intelligence-Centered System for Automated Characterization of Functional Materials and Interfaces

Classic design of experiment relies on a time-intensive workflow that requires planning, data interpretation, and hypothesis building by experienced researchers. Here, in this paper, we describe an integrated, machine-intelligent experimental system which enables simultaneous dynamic tests of electrical, optical, gravimetric, and viscoelastic properties of materials under a programmable dynamic environment. Specially designed software controls the experiment and performs on-the-fly extensive data analysis and dynamic modeling, real-time iterative feedback for dynamic control of experimental conditions, and rapid visualization of experimental results. The system operates with minimal human intervention and enables time-efficient characterization of complex dynamic multifunctional environmental responses of materials with simultaneous data processing and analytics. The system provides a viable platform for artificial intelligence (AI)-centered material characterization, which, when coupled with an AI-controlled synthesis system, could lead to accelerated discovery of multifunctional materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗