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A microgrid deployment framework to support drayage electrification
The electrification of heavy-duty commercial vehicles (HDCVs) is key to enhancing air quality and reducing urban air pollution, but it also imposes significant demands on an electric grid not designed for such high loads. Without complementary infrastructure, electrification may yield limited air quality improvements. This article explores the critical role of microgrids—integrating solar photovoltaics and battery storage—in supporting HDCV electrification. We present an integrated framework to identify viable microgrid sites in a given region, estimate deployment costs, and optimize power use to reduce dependence on the grid. As a case study, we apply the framework to the region around the Port of Savannah, GA, USA, demonstrating how targeted microgrid deployment can enhance grid capacity, improve energy resiliency, and support electrified freight transport.
Near wake evolution of a tidal stream turbine due to asymmetric sheared turbulent inflow with different integral length scales
Tidal stream turbines deployed at highly energetic open water sites are subjected to sheared inflow in the rotor plane. The inflow shear is expected to cause asymmetric loading on the rotor blades and affect the downstream wake. In the current study, two different turbulent inflow conditions, static-high shear and dynamic shear, were generated via an active-grid turbulence generator. A 1:20 scaled three-bladed horizontal axis tidal turbine model was tested in those conditions. The results were compared to a quasi-laminar case with no imposed turbulence or shear. The results show that the high shear reduces the average performance, with a drop of up to 16% in the optimal power coefficient. Besides, the shear profiles increase torque fluctuations and induce significant differences in wake hydrodynamics between the high-speed (upper) and low-speed (lower) regions. The large integral length scales further enhance the load fluctuations perceived by the rotor but have a negligible effect on the mean wake field quantities and the wake recovery. The lower half region featured a faster breakdown of tip vortex structure and a rapid drop of swirl number, a phenomenon conjectured to be a consequence of the strong turbulence intensities and Reynolds stresses in the lower half region. Furthermore, the sheared turbulent inflow also results in a very intensive energy redistribution process towards large-scale, low-frequency motions, which is important to the downstream turbines.
The influence of ambient light spectrum (LED, INC, CFL, Xenon…) on the efficiency of perovskite indoor photovoltaic solar cells for Internet of Things (IoT) applications
This record provides a numerical investigation of a MAGeI₃-based perovskite solar cell with the structure FTO/TiO₂/MAGeI₃/Spiro-OMeTAD, evaluated for both outdoor and indoor light-harvesting applications using the SCAPS-1D simulator. Device performance is analyzed under AM 1.5G sunlight and several artificial light sources, including LED, incandescent, compact fluorescent lamp (CFL), flashlight, and xenon illumination. The study reports initial and optimized power conversion efficiencies and examines the influence of absorber layer thickness and bandgap on device performance under different lighting conditions. The results highlight the potential of MAGeI₃-based perovskite solar cells for indoor energy harvesting and low-power Internet of Things (IoT) applications.
Assessing DFT-D3 Damping Functions Across Widely Used Density Functionals: Can We Do Better?
With the aim of improving the utility of the DFT-D3 empirical dispersion correction, we herein generalize the DFT-D3 damping function by optimizing an additional parameter, an exponent, which controls the rate at which the dispersion tail is activated. This method- DFT-D3(op), shorthand for “optimized power,” where power refers to the newly introduced exponent- is then parametrized for use with ten popular density functional approximations across a small set of noncovalent interactions and isomerization energies; the resulting methods are then evaluated across a large independent test set of 2475 noncovalent binding energies and isomerization energies. We find that the DFT-D3(op) tail represents a substantial improvement over existing damping functions, as it affords significant reductions in errors associated with noncovalent interaction energies and geometries. Finally, the revPBE0-D3(op) and MS2-D3(op) methods in particular stand out, and our extensive testing indicates they are competitive with other modern density functionals.
Enhanced Density-of-States Effective Mass and Strained Endotaxial Nanostructures in Sb-Doped Pb 0.97 Cd 0.03 Te Thermoelectric Alloys
Here we report that CdTe alloying and Sb doping increase the density-of-states effective mass and introduce endotaxial nanostructuring in n-type PbTe, resulting in enhanced thermoelectric performance. A prior theoretical prediction for the presence of resonance states in the conduction band of this system, however, could not be confirmed. An amount of 3 mol % CdTe alloying widens the band gap of PbTe by 50%, leading to enhanced carrier effective mass and Seebeck coefficient. This effect is even more pronounced at high temperatures where the solubility of CdTe increases. At 800 K, when the carrier concentration is the same (4 × 10 19 cm -3 ), the Seebeck coefficient of CdTe-alloyed PbTe is -195 μV K -1 , 16% higher than that of the Cd-free control sample (-168 μV K -1 ). Sb doping considerably increases the electron concentration of Pb 0.97 Cd 0.03 Te, giving rise to optimized power factors of ~17 μW cm -1 K -2 at 800 K. More importantly, Sb induces strained endotaxial nanostructures evenly distributed in the matrix. Furthermore, these Sb-rich nanostructures account for the ~40% reduction in the lattice thermal conductivity over the whole measured temperature range. As a result, a maximum ZT of 1.2 is attained at 750 K in 0.5 mol % Sb-doped Pb 0.97 Cd 0.03 Te alloys.
Dual Impacts of Space Heating Electrification and Climate Change Increase Uncertainties in Peak Load Behavior and Grid Capacity Requirements in Texas
Around 60% of households in Texas currently rely on electricity for space heating. As decarbonization efforts increase, non‐electrified households could adopt electric heat pumps, significantly increasing peak (highest) electricity demand in winter. Simultaneously, anthropogenic climate change is expected to increase temperatures, the potential for summer heat waves, and associated electricity demand for cooling. Uncertainty regarding the timing and magnitude of these concurrent changes raises questions about how they will jointly affect the seasonality of peak demand, firm capacity requirements, and grid reliability. This study investigates the net effects of residential space heating electrification and climate change on long‐term demand patterns and load shedding potential, using climate change projections, a predictive load model, and a direct current optimal power flow (DCOPF) model of the Texas grid. Results show that full electrification of residential space heating by replacing existing fossil fuel use with higher efficiency heat pumps could significantly improve reliability under hotter futures. Less efficient heat pumps may result in more severe winter peaking events and increased reliability risks. As heating electrification intensifies, system planners will need to balance the potential for greater resource adequacy risk caused by shifts in seasonal peaking behavior alongside the benefits (improved efficiency and reductions in emissions).
Nanoscopic cross-grain cation homogenization in perovskite solar cells
Multiscale cation inhomogeneity has been a major hurdle in state-of-the-art formamidinium–caesium (FA–Cs) mixed-cation perovskites for achieving perovskite solar cells with optimal power conversion efficiencies and durability. Although the field has attempted to homogenize the overall distributions of FA–Cs in perovskite films from both plan and cross-sectional views, our understanding of grain-to-grain cation inhomogeneity and ability to tailor it—that is, spatially resolving the FA–Cs compositional difference between individual grains down to the nanoscale—are lacking. Here we reveal that as fundamental building blocks of a perovskite film, individual grains exhibit cationic compositions deviating from the prescribed ideal composition, severely limiting the interfacial optoelectronic properties and perovskite layer durability. This performance-limiting nanoscopic factor is linked to thermodynamic-driven morphological grooving, leading to a segmented surface landscape. At the grain triple junctions, grooves form nanoscale groove traps that hinder the mixing of solid-state cations across grains and thus retard inter-grain FA–Cs mixing. By rationally modulating the heterointerfacial energies, we reduced the depth of these nanoscale groove traps by a factor of three, significantly improving cation homogeneity. Perovskite solar cells with shallower nanoscale groove traps demonstrate enhanced power conversion efficiencies (25.62%) and improved stability under various standardized international protocols. In conclusion, our work highlights the significance of resolving surface nano-morphologies for homogeneous properties of perovskites.
Defect-engineered monolayer MoS 2 with enhanced memristive and synaptic functionality for neuromorphic computing
Two-dimensional transition metal dichalcogenides (TMDs)-based memristors are promising candidates for realizing artificial synapses in next-generation computing. However, practical implementation faces several challenges, such as high non-linearity and asymmetry in synaptic weight updates, limited dynamic range, and cycle-to-cycle variability. Here, utilizing optimal-power argon plasma treatment, we significantly enhance the performance matrix of memristors fabricated from monolayer MoS 2 . Our approach not only improves linearity and symmetry in synaptic weight updates but also increases the number of available synaptic weight updates and enhances Spike-Time Dependent Plasticity. Notably, it broadens the switching ratio by two orders, minimizes cycle-to-cycle variability, reduces non-linear factors, and achieves an energy consumption of ~30 fJ per synaptic event. Implementation of these enhancements is demonstrated through Artificial Neural Network simulations, yielding a learning accuracy of ~97% on the MNIST hand-written digits dataset. Our findings underscore the significance of defect engineering as a powerful tool in advancing the synaptic functionality of memristors.
A solution-processed high performance organic solar cell using a small molecule with the thieno[3,2- b ]thiophene central unit
A solution processed acceptor–donor–acceptor (A–D–A) small molecule with thieno[3,2-b]thiophene as the central building block and 2-(1,1-dicyanomethylene)-rhodanine as the terminal unit, DRCN8TT, was designed and synthesized. The optimized power conversion efficiency (PCE) of 8.11% was achieved, which is much higher than that of its analogue molecule DRCN8T. In this work, the improved performance was ascribed to the morphology which consisted of small, highly crystalline domains that were nearly commensurate with the exiton diffusion length.
Stochastic assessment of distributed generation hosting capacity and energy efficiency in active distribution networks
Active network management (ANM) aims to increase the capacity of variable distributed generation (DG), which can be connected to existing distribution networks. In this study, it is proposed to simultaneously consider the efficient use of energy resources when high shares of DG are procured through the ANM approach. To that end, a multi-period and multiobjective optimisation algorithm, based on the linearised optimal power flow, is formulated. The algorithm seeks to maximise the installed capacity of DG while minimising the energy losses and consumption of voltage-dependent loads. The objectives are optimised considering the coordinated operation of voltage regulators and on-load tap changers, and the management of DG generation curtailment and reactive power compensation from DG. Additionally, the effects of load and generation uncertainties are addressed through a two-stage stochastic programming formulation of the multiobjective problem. The result is a set of non-inferior solutions, which allows exploring the degree of conflict among the objectives. The proposed approach was tested on two IEEE test feeders and the solutions show a significant improvement in the system's energy efficiency with a low impact on the amount of connected DG.
Application of real‐time nonlinear model predictive control for wave energy conversion
Abstract This article presents an approach to implement a Nonlinear Model Predictive Controller (NMPC) in real‐time with a non‐standard cost index. The proposed technique's applications are presented to maximize the energy produced by a Wave Energy Converter (WEC) when the cost index is a non‐quadratic piecewise discontinuous functional of some design variables. The presented framework is based on pseudo‐quadratisation and weight scheduling, which is implemented using the ACADO toolkit for MATLAB/Simulink. The proposed strategy features code generation and deployment on the real‐time target machines for industrial applications. The simulations and experiments confirm the success of the proposed approach in achieving the feasible operation of the NMPC and an optimal power capture by the wave energy converters.
Thermoelectric transport properties of diamond-like Cu{sub 1−x}Fe{sub 1+x}S{sub 2} tetrahedral compounds
Polycrystalline samples with the composition of Cu{sub 1−x}Fe{sub 1+x}S{sub 2} (x = 0, 0.01, 0.03, 0.05, 0.1) were synthesized by a melting-annealing-sintering process. X-ray powder diffraction reveals all the samples are phase pure. The backscattered electron image and X-ray map indicate that all elements are distributed homogeneously in the matrix. The measurements of Hall coefficient, electrical conductivity, and Seebeck coefficient show that Fe is an effective n-type dopant in CuFeS{sub 2}. The electron carrier concentration of Cu{sub 1−x}Fe{sub 1+x}S{sub 2} is tuned within a wide range leading to optimized power factors. The lattice phonons are also strongly scattered by the substitution of Fe for Cu, leading to reduced thermal conductivity. We use Debye approximation to model the low temperature lattice thermal conductivity. It is found that the large strain field fluctuation introduced by the disordered Fe ions generates extra strong phonon scatterings for lowered lattice thermal conductivity.
Microfluidic sorting of protein nanocrystals by size for X-ray free-electron laser diffraction
The advent and application of the X-ray free-electron laser (XFEL) has uncovered the structures of proteins that could not previously be solved using traditional crystallography. While this new technology is powerful, optimization of the process is still needed to improve data quality and analysis efficiency. One area is sample heterogeneity, where variations in crystal size (among other factors) lead to the requirement of large data sets (and thus 10–100 mg of protein) for determining accurate structure factors. To decrease sample dispersity, we developed a high-throughput microfluidic sorter operating on the principle of dielectrophoresis, whereby polydisperse particles can be transported into various fluid streams for size fractionation. Using this microsorter, we isolated several milliliters of photosystem I nanocrystal fractions ranging from 200 to 600 nm in size as characterized by dynamic light scattering, nanoparticle tracking, and electron microscopy. Sorted nanocrystals were delivered in a liquid jet via the gas dynamic virtual nozzle into the path of the XFEL at the Linac Coherent Light Source. We obtained diffraction to ∼4 Å resolution, indicating that the small crystals were not damaged by the sorting process. We also observed the shape transforms of photosystem I nanocrystals, demonstrating that our device can optimize data collection for the shape transform-based phasing method. Using simulations, we show that narrow crystal size distributions can significantly improve merged data quality in serial crystallography. From this proof-of-concept work, we expect that the automated size-sorting of protein crystals will become an important step for sample production by reducing the amount of protein needed for a high quality final structure and the development of novel phasing methods that exploit inter-Bragg reflection intensities or use variations in beam intensity for radiation damage-induced phasing. This method will also permit an analysis of the dependence of crystal quality on crystal size.
Correlating thermoelectric (Bi,Sb) 2 Te 3 film electric transport properties with microstructure
The room temperature electronic transport properties of 1 μm thick Bi0.4Sb1.6Te3 (BST) films correlate with overall microstructural quality. Films with homogeneous composition are deposited onto fused silica substrates, capped with SiN to stop both oxidation and Te loss, and postannealed to temperatures ranging from 200 to 450 °C. BST grain sizes and (00l) orientations improve dramatically with annealing to 375 °C, with smaller increases to 450 °C. Tiny few-nanometer-sized voids in the as-deposited film grain boundaries coalesce into larger void sizes up to 300 nm with annealing to 350 °C; the smallest voids continue coalescing with annealing to 450 °C. These voids are decorated with few-nanometer-sized Sb clusters that increase in number with increasing annealing temperatures, reducing the Sb content of the remaining BST film matrix. Resistivity decreases linearly with increasing temperature over the entire range studied, consistent with improving crystalline quality. The Seebeck coefficient also improves with crystalline quality to 350 °C, above which void coalescence and reduced Sb content from the BST matrix correlate with a decrease in the Seebeck coefficient. Yet, a plateau exists for an optimal power factor between 350 and 450 °C, implying thermal stability to higher temperatures than previously reported.
Resiliency of Distribution Systems Incorporating Asynchronous Information for System Restoration
Resiliency of distribution systems under extreme operating conditions is critical, especially when the utility is not available. With the large-scale deployment of distributed resources, it becomes possible to restore critical loads with local non-utility resources. Distribution system operators (DSOs) need to determine the critical loads to be restored, considering limited resources and distribution facilities. Several studies on resiliency have been conducted for the restoration of distribution systems. However, the inherent asynchronous characteristic on the information availability has not been incorporated. With incomplete and asynchronous information, decisions may be made that result in underutilization of generation resources. In this paper, a new distribution system restoration approach is proposed, considering uncertain devices and associated asynchronous information. It uses a two-module architecture that efficiently optimizes restoration actions using a binary linear programming model and evaluates their feasibility with unbalanced optimal power flow. Networked microgrids are included in the model. The IEEE 123-node test feeder is used for validation. Results show that asynchronous messages may affect the restoration actions significantly and the impacts can be mitigated by the proposed decision support tool for the DSOs.
Endogenous Interface Pricing for Consistent Transmission–Distribution Co-Optimization With Discrete Distribution Controls
This paper proposes an endogenous interface pricing model for day-ahead transmission–distribution co-optimization that co-determines the interface locational marginal price (LMP) and the transmission–distribution exchange, ensuring price–dispatch consistency while optimally scheduling discrete distribution controls. The formulation couples a DC optimal power flow (OPF) with a branch-flow AC OPF that schedules distributed energy resources (DERs), tap-changer settings, capacitor banks (CBs), and multi-period energy storage systems (ESSs) under feeder voltage and current limits, and is solved as a mixed-integer second-order cone program (MISOCP). In a T14–D33 system, coordinated device scheduling recovers about 90% of the distribution-to-transmission export achievable in a reference case that ignores distribution network (DN) limits, while satisfying a 1.05 p.u. voltage upper bound. In a T39–D34/D37/D123 system, a sequential decoupled benchmark produces interface LMP distortions up to 12.5% and a 7.28% mismatch in net export energy, whereas the proposed model removes these distortions and the associated settlement mismatches. Second-order cone (SOC) relaxation gaps remain below $10^{-3}$ in all cases.
Coordinated Optimization of Distributed Energy Resources and Smart Loads in Distribution Systems
Distributed energy resources (DERs) and smart loads have the potential to provide flexibility to the distribution system operation. A coordinated optimization approach is proposed in this paper to actively manage DERs and smart loads in distribution systems to achieve the optimal operation status. A three-phase unbalanced Optimal Power Flow (OPF) problem is developed to determine the output from DERs and smart loads with respect to the system operator's control objective. This paper focuses on coordinating PV systems and smart loads to improve the overall voltage profile in distribution systems. Simulations have been carried out in a 12-bus distribution feeder and results illustrate the superior control performance of the proposed approach.