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At least 685 records · Page 38

Minimizing the Electromechanical Stresses in Poloidal Field Coils by Optimizing their Numbers and Locations using FREDA Framework

Poloidal field (PF) and central solenoid (CS) coils play a crucial role in sustaining the equilibrium and preserving the shape of highly confined tokamak plasmas. Ensuring that PF coil current and mechanical stress stay within superconducting and structural limitations is an important check in the design assessment. Minimizing the PF coil currents and mechanical stresses influences reliability, cost, and performance. A free-boundary MHD equilibrium code—FreeGS is employed within the fusion reactor design and assessment (FREDA) whole facility modeling (WFM) framework to construct the plasma equilibrium based on the configuration and currents in the PF coils. Here, we present the capability of the FreeGS code to minimize the currents, forces, and electromagnetic stresses on the PF coils by optimizing their number, sizes, structures, and locations while maintaining an MHD stable plasma configuration with a large confinement factor. The workflow is initialized with a configuration of plasma parameters and coils’ locations from the 0-D tokamak build systems code in the FREDA framework. Then, FreeGS is called to calculate the initial equilibrium at the minimum total current in PF coils. Thereafter, FreeGS’s internal optimizer minimizes the currents and hoop and central forces on the PF coils while maintaining the reference equilibrium. Finally, the input configuration is updated with the optimized parameters for equilibria over the ramp-up phase of a burning-plasma operation. FREDA’s whole facility optimization capability, which includes all magnetic field coil systems, blanket, vacuum vessel (VV), first wall, divertor, etc., is under development and out of the scope for this study.

Hassan, Ehab [ORNL] (ORCID:0000000181060301)↗

Prioritizing circular economy strategies for sustainable PV deployment at the TW scale

Global decarbonization requires an unprecedented scale-up of photovoltaic (PV) manufacturing and deployment. The material demand and eventual end of life management associated with multi-TW scale deployment poses many challenges. Circular Economy (CE) and it's associated R-Actions (Reduce, Reuse, Recycle) have been proposed to mitigate end of life management and material sourcing concerns. However, CE metrics typically focus on a single product and only consider mass, excluding energy flows. This work leverages the PV in Circular Economy (PV ICE) tool to quantify the deployment, mass, and energy impacts of R-Actions and proposed sustainable PV designs in the context of achieving energy transition deployment goals (75 TW in 2050). 13 module scenarios are established and evaluated across 6 capacity, mass and energy metrics to identify tradeoffs and priorities. We find that increasing module efficiency can reduce near-term material demands up to 30% and improve energy metrics by up to 9%. Material circularity (recycling) can minimize lifecycle wastes and reduce material demands at the cost of higher energy demands. Increasing module lifetime, including reliability improvements and reuse strategies, is effective at reducing both material (>10%) and energy demands (24%). Uniquely, lifetime improvements maximize benefits and minimize the harms across all six metrics while achieving multi-TW scale deployment.

Photovoltaics↗

Indirect Liftoff Mechanism for High‐Throughput, Single‐Source Laser Scribing for Perovskite Solar Modules

A high‐throughput, single‐source laser scribing method exploiting a transparent conducting oxide (TCO) indirect liftoff mechanism is developed to produce serially interconnected perovskite solar modules. The TCO‐based indirect liftoff mechanism relies solely on laser absorption in the front transparent electrode material reducing thermal damage to the overlying layers and allowing for fast scribing speeds with low‐cost μs‐pulse duration fiber laser systems. Minimal resistive power losses are observed with the method compared to conventional ablative laser scribes, maintaining the power conversion efficiencies of small‐area devices (≈0.2 cm 2 ) across significantly larger deposition areas (≈1 cm 2 ). Finally, demonstrating > 3 m s −1 processing speeds, TCO‐based liftoff provides the highest throughput laser scribing method for thin‐film photovoltaic devices produced on glass/TCO substrates, capable of processing large‐area perovskite solar modules at a manufacturing scale.

14 SOLAR ENERGY↗

Impact of model predictive control-enabled home energy management on large-scale distribution systems with photovoltaics

Residential customers use more than one-quarter of the electricity in the world. Optimally managing home energy consumption is an effective way of easing the operational challenges facing the electric grid with increasing solar photovoltaics (PV). This paper studies the impact of the future proliferation of home energy management systems (HEMS) in the presence of PV on large-scale distribution systems. First, we present a stochastic HEMS model that minimizes residential customers' thermal discomfort and energy costs under uncertainty. The HEMS model schedules the optimal operations of residential appliances in the presence of PV within a mixed-integer linear programming-based model predictive control framework that links the proposed HEMS to a quasi-steady-state time-series simulation tool. Additionally, extensive simulations are conducted for a stand-alone residential home using two tariff structures and for 1977 homes on an 8,500-node distribution feeder. Simulation results quantify the impact of the future proliferation of HEMS on the large-scale distribution system with PV.

14 SOLAR ENERGY↗

PCAfold 2.0—Novel tools and algorithms for low-dimensional manifold assessment and optimization

We describe an update to our open-source Python package, PCAfold, designed to help researchers generate, analyze and improve low-dimensional data manifolds. In the current version, PCAfold 2.0, we introduce novel tools and algorithms for assessing and optimizing low-dimensional manifolds. This includes a method that generates a “map” of local feature sizes that can help pinpoint researchers to problematic regions on a manifold. We introduce a novel cost function that characterizes the quality of a manifold topology with a single number. We develop two algorithms for feature selection based on principal component analysis (PCA) that use the cost function as an objective function to minimize. We introduce a quantity of interest (QoI)-aware dimensionality reduction strategy where data projections are computed using an artificial neural network and are directly optimized towards representing various projection-independent and projection-dependent QoIs. We also introduce an implementation of partition of unity networks (POUnets) for efficient reconstruction of QoIs from low-dimensional manifolds based on combining neural network classification with localized polynomial regression. Our software can be broadly applicable in all domains of science and engineering that aim to reduce data dimensionality, as well as in the fundamental research on representation learning.

97 MATHEMATICS AND COMPUTING↗

Cyber-Physical Reconfiguration for Disaster Resilience of Power Distribution Systems

Cyber-physical distribution systems (CPDS) have emerged from the integration of information technology into distribution systems. While offering substantial benefits, this integration also introduces vulnerabilities. The interaction between cyber networks and distribution systems renders CPDS susceptible to disasters. To ensure critical load supply and system resilience, rapid post-disaster load restoration is required. The paper proposes a critical load restoration (CLR) framework in CPDS using a network reconfiguration approach that exploits the existing post-disaster resources to restore critical loads within the shortest possible time. Using graph theory, the cyber network and distribution system are integrated into a single digraph, minimizing the CLR complexity in CPDS. A cost metric is also defined to satisfy network-specific objectives and constraints. A heuristic is proposed to guide the load restoration process using the cost metric within the integrated digraph. Simulation results confirm the framework's superiority over existing literature, which either overlooks cyber components or prolongs restoration with additional resource deployment.

cyber-physical system↗

Deep Reinforcement Scheduling of Energy Storage Systems for Real-time Voltage Regulation in Unbalanced LV Networks with High PV Penetration

The ever-growing higher penetration of distributed energy resources (DERs) in low-voltage (LV) distribution systems brings both opportunities and challenges to voltage support and regulation. This paper proposes a deep reinforcement learning (DRL)-based scheduling scheme of energy storage systems (ESSs) to mitigate system voltage deviations in unbalanced LV distribution networks. The ESS-based voltage regulation problem is formulated as a multi-stage quadratic stochastic program, with the objective of minimizing the expected total daily voltage regulation cost while satisfying operational constraints. While existing voltage regulation methods are mostly focused on onetime- step control, this paper explores a day-horizon systemwide voltage regulation problem. In other words, the size of action and state spaces are extremely high-dimensional and need to be delicately handled. Furthermore, in order to overcome the difficulty of modeling uncertainties and develop a realtime solution, a learn-to-schedule feedback control framework is proposed by adapting the problem to a model-free DRL setting. The proposed algorithm is tested on a customized 6-bus system and a modified IEEE 34-bus system. Simulation results validate the effectiveness and near-optimality of voltage regulation by ESS in comparison with a deterministic quadratic program solution.

Wang, Shengyi↗

Local-Scale Framework for Techno-Economic Analysis of Subsurface Hydrogen Storage

The energy sector is evolving toward increased reliance on renewable energy technologies to meet state, national, and organization decarbonization goals. This trend is creating challenges and opportunities with meeting current and future energy demand under the variable supply conditions that most renewables provide. Hydrogen (H 2 ) is a promising energy carrier that may meet the need for both on-demand and long-duration storage to maintain energy security and resilience. Underground hydrogen storage (UHS) is a method of storing H 2 in subsurface geological systems, such as depleted hydrocarbon reservoirs, salt caverns, saline aquifers, hard rock, and other engineered systems. UHS has the potential to store large quantities of H 2 over time, providing a reliable source of energy while minimizing surface footprints at a lower investment cost compared to surface storage. Earlier work estimated that, if converted and retrofitted, existing underground natural gas storage (UGS) facilities in the U.S. can store approximately 327 TWh, or 9.8 million metric tons, of pure H 2 . However, a shift to pure H 2 would decrease the collective working-gas energy of the UGS facilities by approximately 75% due to physical and chemical differences between natural gas and hydrogen. The same work also suggests that almost 75% of the existing UGS facilities in the U.S. could maintain current energy demand buffering using a blend of only 20% H 2 to 80% natural gas, by volume at surface conditions. If we can take advantage of the suite of mature technologies of existing UGS facilities and natural gas utility systems to accelerate the transition to a hydrogen economy in the U.S., a 20% H 2 blend could lead to a 6-7% reduction of greenhouse gas emissions for energy delivered through natural gas utility systems.

03 NATURAL GAS↗

Station Impact Analysis 2025

As part of the U.S. DOE EVs@Scale consortium, the NextGen Profiles (NGP) project presents analysis and results from the study of High Power Charging Electric Vehicles and Battery Charging Infrastructure. High Power Charging equipment is capable of recharging electric vehicle traction batteries at power levels of 200KW and above. The intent of the project is to further understand the most recent technological capabilities of the electric mobility industry related to charging performance. The project aims to develop EV, EVSE, and Fleet characterization testing practices and comprehensive analysis with inputs from key industry stakeholders. The results published in this NextGen Profiles project report provide data and insight for use by numerous entities including modeling and simulation organizations, policy makers, fleet planners, industry stakeholders and the general public involved with the development, deployment and operation of electrified transportation technologies. The factors influencing Electric Vehicle (EV) Direct Current Fast Charging (DCFC), including EV battery specifications, temperature effects on lithium-ion battery and power electronics performance, lithium-ion battery SOC bounding and charging station design considerations are specifically investigated to analyze their impacts on charging station operation and recommendations are made to minimize charge station dwell time, reduce charging costs and mitigate electric grid and charge station congestion. Additional high-power charging results are anticipated in future publications in support of the U.S. DOE EVs@Scale consortium NextGen Profiles project.

33 ADVANCED PROPULSION SYSTEMS↗

Comparison of Ion and Neutron Irradiations to 3 dpa at 500C in Ferritic-Martensitic Alloys

The growing global demand for energy will increasingly call upon advanced nuclear fission reactors to supply safe and reliable electricity. The structural and fuel cladding components of these reactors will be subject to extreme conditions of irradiation damage up to several hundred displacements per atom (dpa) at temperatures as high as 700°C. Ferritic-martensitic (F-M) steels are leading candidates for these challenging conditions due to their strength and dimensional stability under irradiation. In order to accelerate the process for evaluating F-M alloys, charged particles are increasingly being used to emulate neutron irradiations. Charged particle irradiations allow the possibility of conducting irradiation experiments within a shorter time period (i.e. at a rate up to 4 orders of magnitude faster) and with minimal radioactivation of the material, enabling lower cost and faster turnaround of post irradiation examination and analysis. However, the irradiation dose rate, damage cascade morphologies, and irradiation damage depth profiles all differ widely between protons, heavier ions, and neutrons. Currently, there is limited understanding of the significance of these physical differences and how they manifest in the irradiated microstructure and mechanical properties of F-M steels. The objective of this study is to evaluate charged particles as a surrogate for neutron irradiations in F-M alloys by assessing common irradiation conditions using Fe++ ions, protons, and neutrons. Keeping the temperature and dose consistent enables isolation of the effects of each irradiating particle and their respective dose rates and cascade morphologies.

Swenson, M.J.↗

Expedited desalination permitting enables adaptive planning and water system cost reduction

Seawater desalination is a drought-proof water supply for coastal cities, but widespread development of desalination plants in the U.S. has been limited by both cost and the complexity of permitting processes designed to minimize environmental impact . This work estimates the value of accelerating the permitting timeline without changing environmental or social standards. On average, faster permitting reduces the frequency of desalination plant construction and operation, the overall costs of robust water system operation, the environmental impacts of drought-tolerant water supplies due to shorter duration of plant operation. Expedited permitting allows fundamental changes in how water infrastructure is deployed, facilitating a transition from anticipatory construction and continuous operation of seawater desalination capacity as a redundant drought buffer to just-in-time (i.e. adaptive) deployment of seawater desalination capacity when critical drought thresholds are crossed. We demonstrate the value of expedited desalination permitting in enabling adaptive planning and reducing water system costs using a simple case study in Santa Barbara, CA. We discuss additional forms of adaptive water infrastructure planning as enabled by faster permitting and address their challenges and opportunities. Lastly, we identify synergies between innovation in adaptive planning, innovation in expedited permitting practices, and innovation in water technology.

adaptive planning↗

Multicarrier Energy Systems: Shaping Our Energy Future

Multicarrier energy systems (MCESs) are characterized by strong coordination in operation and planning across multiple energy vectors and/or sectors to deliver reliable, cost-effective energy services to end users/customers with minimal impact on the environment. They have efficiency and flexibility benefits and are deployed in large and small scales on the supply and demand sides and at the network level but are more complex to control and manage. Here, MCESs are reviewed in the context of future low carbon energy systems based on electrification and very high variable renewable energy penetrations. Fully exploiting these systems requires some cost reductions, more sophisticated operations enabled by standardized communications and control capabilities detailed planning paradigms, and addressing their corresponding economic challenges. All these point toward the direction of analysis, markets, and technology research and development coupled with better policy and regulatory frameworks. One futuristic vision of a very low carbon energy system is proposed that illustrates potential pathways to an MCES-dominated energy future.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Roll-to-Roll Manufacturing of Solid Oxide Fuel Cells

The overall goal of this project is to develop a high-volume electrode electrolyte assembly (EEA) production capability to significantly increase throughput of solid oxide fuel cell (SOFC) manufacturing and reduce the cost while maintaining the same level of performance. Specifically, four approaches will be adopted: 1) optimization of the lamination process and correlation of the EEA properties and performance with the lamination conditions; 2) scale up of the lamination process and demonstration of >10 ft of EEA; 3) further increase of the EEA throughput via slot-die coating and demonstration of > 5 m/min in coating the thick anode layer; and 4) minimization of the anode thickness to reduce material cost.

30 DIRECT ENERGY CONVERSION↗

Life cycle cost, energy, and carbon emissions of molds for precast concrete: Exploring the impacts of material choices and additive manufacturing

Molds for precast concrete are commonly used to create simple- to complex-shaped concrete products away from construction sites. These molds are often handmade from wood; however, additively manufacturing (AM, or 3D printing) fiber-reinforced polymer composites is an advantageous alternative, producing significantly more durable, highly complex molds faster, but likely at a higher cost. Here, this study explores the impact of material and production variables on the cost, energy, and carbon emissions of employing composite AM molds over the full lifecycle. The case study employed techno-economic and life cycle assessments to show that using wood flour–poly(lactic acid) or recycled carbon fiber–acrylonitrile butadiene styrene for AM molds can be less expensive than conventional wood molds, especially when considering use phase costs. While wood molds have the least environmental impacts due to wood's higher biogenic carbon sequestration and minimal processing, optimizing AM designs could reduce energy demand, carbon emissions, and cost.

36 MATERIALS SCIENCE↗

Advanced Anti-Fouling Coatings to Improve the Efficiency of Coal Power Plants

As the total cost of carbon to generate energy has become a global concern, operators are increasingly looking at all parts of the generation cycle to find areas where efficiency gains may be found. It has been long identified that fouling of heat exchangers is a persistent cause of up to 2.5% of global CO 2 emissions. Unfortunately, practice has also demonstrated that unless a powerful economic driver exists to encourage preemptive mitigation of fouling, there will always be a strong tendency for operators to minimize any form of intervention due to high costs and challenges in scheduling downtime. The objective of this proposed research effort was to demonstrate how existing power plants could lower their carbon emissions and significantly improve heat transfer efficiency using new surface treatment materials to control fouling in a variety of heat exchange equipment. The surface treatment material which was optimized and deployed in this effort is now known commercially as HeatX. It is a low-surface energy, water- and oil-repellent, abrasion resistant material which can be applied in-situ to a wide variety of previously worn/used/in-service substrates. Once applied, it provides a barrier against corrosion, scale deposit formation, and biofilm adhesion on the circulating water-containing tube-side. Alternatively, if applied to the tube exterior, the non-wetting nature of the surface was demonstrated to promote dropwise condensation, subsequently lowering condenser backpressure and increasing overall plant efficiency. As part of this cooperative effort, the Department of Energy’s support was crucial to de-risk and demonstrate the concept of HeatX, while validating both the performance and economic benefit in multiple pilot field studies. The HeatX material properties were optimized in this effort for full field applicability to heat exchangers and condensers, and Oceanit developed the necessary procedures and protocols to provide enough material to support extended length, multi-year demonstrations in the power generation, desalination, and refining industries, making this technology broadly applicable and ready for commercial transition. Field deployment case studies at thermal power plants have shown that the HeatX treatment can provide economic savings of up to $15,000 per day for an operator based on avoiding maintenance costs and lowering fuel usage. The complete mitigation of fouling effects can increase the efficiency of equipment by up to 7%, in a field where gains of 0.5% are generally seen as operationally significant. The HeatX treatment has also demonstrated exceptional lifetime and compatibility with a wide variety of seawater and hydrocarbon environments, further increasing both the return on investment and the effective emission reduction. Such efficiency improvements correlate to massive carbon savings. For every 1 GW of capacity, operators can see carbon emissions reductions of 300,000 tons of CO 2 per year. When looking at the bigger picture, improved condenser function across the U.S. has the potential to prevent 221.3 million tons of CO 2 emissions, equivalent to the sequestration capacity of 129 million acres of forest. If applied on a global scale, 1.26 billion metric tons of CO 2 could be averted from the atmosphere, the same amount of carbon sequestrated by 1.5 billion acres of forest annually or 262,000 wind turbines operating annually. As businesses across multiple industries take a more active role in focusing on environmental, social, and governance (ESG) solutions as part of their core business operations, HeatX will be an attractive technology for commercial investment.

01 COAL, LIGNITE, AND PEAT↗

T3CO (Transportation Technology Total Cost of Ownership) Open Source [SWR-21-54]

T3CO (Transportation Technology Total Cost of Ownership), is open source software for modeling total cost of ownership for commercial vehicles with advanced powertrains. T3CO is a modeling framework for determining geospatially and temporally optimized total cost of ownership (TCO) for vehicle powertrain technologies. T3CO runs NREL's FASTSim™ software for a representative set of operating conditions to minimize TCO based on vehicle parameters that affect purchase and operating costs (e.g., fuel/electricity consumption, asset depreciation, opportunity costs associated with charging time) while simultaneously ensuring that firm performance constraints (e.g. zero-to-sixty time, gradeability) are satisfied. T3CO will enable the user to control which powertrain parameters are used in optimizing TCO, and these parameters will be modified by a multi-objective optimization (MOO) algorithm to identify a Pareto-optimal solution set. The optimization algorithm will be modular so that users can choose from many different MOO options or insert their own user-defined optimization tool. NREL T3CO Homepage: https://www.nrel.gov/transportation/t3co.html PyPI package: https://pypi.org/project/t3co/

Lustbader, Jason↗

Fluor Solvent Evaluation and Testing New Scope: Techno-economic Assessment of EEMPA Solvent for CO 2 Separations from Natural Gas Combined Cycle Power Plant

In this project, a techno-economic analysis (TEA) and sensitivity studies were conducted to assess the PNNL’s leading water-lean CO 2 capture solvent, EEMPA, for capture CO 2 from a natural gas combined cycle (NGCC) power plant at different levels of capture rate. Process models for the NGCC power plant, integrated with EEMPA carbon capture processes, were developed in Aspen Plus V14 using the most up-to-date property package for EEMPA-H 2 O-CO 2 system. The TEA evaluated EEMPA carbon capture process at normal capture rates (90%, 95% and 97%) against Case B32B (Cansolv) described in NETL Rev4a baseline report, and at higher capture rates aimed at achieving zero or negative emissions from the power plant (400 ppmv, 200 ppmv, and 100 ppmv CO 2 in exhaust gas), compared to typical Direct Air Capture (DAC) technologies. A manuscript was drafted for peer-reviewed publication. The results suggested that the carbon capture cost reaches a minimum of $\$$53.7/tonne CO 2 at 90% capture rate. Compared to Cansolv, one of the industrial benchmarks, EEMPA demonstrates 2-4% cost savings at capture rates up to 95%, but minimal savings at higher capture rate. The water lean-solvent system proves economically attractive for achieving moderate negative emissions (about 200 ppmv CO 2 in exhaust gas, and equivalent to 50% CO 2 removal from air) for NGCC flue gas, with marginal capture costs comparable to direct air capture (DAC) technologies. A sensitivity analysis results reveal that its economic advantage, unaffected by EEMPA price due to low solvent loss and degradation rate. However, the marginal carbon capture cost exceeds $\$$1,000/tonne CO 2 when transitioning from moderate to extreme negative emissions (100 ppmv CO 2 in exhaust gas), suggesting that water-lean solvents may not be economically competitive with other DAC technologies for removing more than 75% CO 2 from air. In addition, initial connection was established with Technology Center Mongstad (TCM) for a potential pilot testing proposal. However, detailed modeling and proposal preparation was not conducted due to the delay of non-disclosure agreement.

20 FOSSIL-FUELED POWER PLANTS↗

Optimized Floating Offshore Wind Turbine Substructure Design Trends for 10–30 MW Turbines in Low-, Medium-, and High-Severity Wave Environments

Floating offshore wind is a promising renewable energy source, as 60% of the wind resources globally are found at depths requiring floating technologies, it minimizes construction at sea, and provides opportunities for industrialization given a lower site dependency. While floating offshore wind has numerous advantages, a current obstacle is its cost in comparison to more established energy sources. One cost-reduction approach for floating wind is increasing turbine capacities, which minimizes the amount of foundations, moorings, cables, and O&M equipment. This work presents trends in mass-optimized VolturnUS hull designs as turbine capacity increases for various wave environments. To do this, a novel rapid hull optimization framework is presented that employs frequency domain modeling, estimations of statistical extreme responses, industry constructability requirements, and genetic algorithm optimization to generate preliminary mass-optimal VolturnUS hull designs for a given turbine design and set of site conditions. Using this framework, mass-optimized VolturnUS hull designs were generated for 10–30 MW turbines for wave environments of varying severities. These design studies show that scaling up turbine capacities increases the mass efficiency of substructure designs, with decreasing returns, throughout the examined turbine capacity range. Additionally, increased wave environment severity is shown to increase the required mass of a given substructure design.

VolturnUS↗